diff --git a/source/_data/pub.bib b/source/_data/pub.bib index 4ad18bd7..85ed5f35 100644 --- a/source/_data/pub.bib +++ b/source/_data/pub.bib @@ -2581,7 +2581,6 @@ @Article{Gupta_AdvancedEnergyMaterials_2022_vNone_p2200596 doi = {10.1002/aenm.202200596}, volume = 12, issue = 23, - pages = 2200596, } @Article{delaPuente_JAmChemSoc_2022_vNone_pNone, author = {Miguel {de la Puente} and Rolf David and Axel Gomez and Damien Laage}, @@ -3064,7 +3063,7 @@ @Article{Lu_JChemTheoryComput_2022_vNone_pNone Potential Models}}, journal = {J. Chem. Theory Comput.}, year = 2022, - journal=18, + volume=18, issue=9, pages={5555--5567}, annote = {Machine-learning-based interatomic potential energy surface (PES) @@ -4147,3 +4146,3859 @@ @Article{Li_JournaloftheEuropeanCeramicSociety_2023_v43_p208 pages = {208--216}, doi = {10.1016/j.jeurceramsoc.2022.10.014}, } + +@Article{Li_GeophysicalResearchLetters_2022_v49_pNone, + author = {Zhi Li and Sandro Scandolo}, + title = {{Elasticity and Viscosity of hcp Iron at Earth's Inner Core Conditions + From Machine Learning{-}Based Large{-}Scale Atomistic Simulations}}, + journal = {Geophysical Research Letters}, + year = 2022, + volume = 49, + issue = 24, + annote = {AbstractAlthough considerable efforts + have been made in the last years to examine the physical properties of + hexagonal close{-}packed (hcp) iron at extreme conditions, it remains + challenging to explain many geophysical observations in Earth's inner + core. Here we examine the elastic and plastic behavior of hcp iron and + the effects of structural defects at inner core conditions using + large{-}scale atomistic simulations coupled with machine + learning{-}based interatomic potential. Our results suggest that the + seismic anisotropy pattern in the inner core can be ascribed to the + elastic anisotropy (6%) of hcp iron. The observed low shear wave + velocity is largely produced by viscous grain boundaries in iron + polycrystal. We also found highly mobile and abundant vacancies in hcp + iron yield a viscous strength + (1015{\ensuremath{\pm}}1) that is consistent with + the geophysical observations. Therefore, our findings highlight the + role played by structural defects and lessen the demand for light + elements to explain the observed seismic data.}, + doi = {10.1029/2022GL101161}, +} + + +@Article{Chahal_JACSAu_2022_v2_p2693, + author = {Rajni Chahal and Santanu Roy and Martin Brehm and Shubhojit Banerjee + and Vyacheslav Bryantsev and Stephen T Lam}, + title = {{Transferable Deep Learning Potential Reveals Intermediate-Range + Ordering Effects in LiF{\textendash}NaF{\textendash}ZrF4 + Molten Salt}}, + journal = {JACS Au}, + year = 2022, + volume = 2, + issue = 12, + pages = {2693--2702}, + annote = {LiF-NaF-ZrF4 multicomponent molten salts are promising candidate + coolants for advanced clean energy systems owing to their desirable + thermophysical and transport properties. However, the complex + structures enabling these properties, and their dependence on + composition, is scarcely quantified due to limitations in simulating + and interpreting experimental spectra of highly disordered, + intermediate-ranged structures. Specifically, size-limited ab initio + simulations and accuracy-limited classical models used in the past are + unable to capture a wide range of fluctuating motifs found in the + extended heterogeneous structures of liquid salt. This greatly + inhibits our ability to design tailored compositions and materials. + Here, accurate, efficient, and transferable machine learning + potentials are used to predict structures far beyond the first + coordination shell in LiF-NaF-ZrF4. Neural networks trained at only + eutectic compositions with 29% and 37% ZrF4 are shown to accurately + simulate a wide range of compositions (11-40% ZrF4) with dramatically + different coordination chemistries, while showing a remarkable + agreement with theoretical and experimental Raman spectra. The + theoretical Raman calculations further uncovered the previously unseen + shift and flattening of bending band at {\ensuremath{\sim}}250 cm-1 + which validated the simulated extended-range structures as observed in + compositions with higher than 29% ZrF4 content. In such cases, machine + learning-based simulations capable of accessing larger time and length + scales (beyond 17 {\r{A}}) were critical for accurately predicting + both structure and ionic diffusivities.}, + PMCID = {PMC9795562}, + doi = {10.1021/jacsau.2c00526}, +} + + +@Article{Li_ActaPhysSin_2022_v71_p247803, + author = {Zhi-Qiang Li and Xiao-Yu Tan and Xin-Lei Duan and Jing-Yi Zhang and + Jia-Yue Yang}, + title = {{Deep learning molecular dynamics simulation on microwave high- + temperature dielectric function of silicon nitride}}, + journal = {Acta Phys. Sin.}, + year = 2022, + volume = 71, + issue = 24, + pages = 247803, + annote = {Silicon nitride (<i>{\ensuremath{\beta}}&l + t;/i>-Si<sub>3</sub>N<sub>4</sub>) is a + most promising thermal wave-transparent material. The accurate + measurement of its high-temperature dielectric function is essential + to solving the {\textquotedblleft}black barrier{\textquotedblright} + problem of hypersonic vehicles and accelerating the design of silicon + nitride-based thermal wave-transparent materials. Direct experimental + measurement at high temperature is a difficult job and the accuracy of + classical molecular dynamics (CMD) simulations suffers the choice of + empirical potential. In this work, we build a <i>{\ensuremath{\b + eta}}</i>-Si<sub>3</sub>N<sub>4</sub> + model on a nanoscale, train the deep learning potential (DLP) by using + first-principles data, and apply the deep potential molecular dynamics + (DPMD) to simulate the polarization relaxation process. The predicted + energy and force by DLP are excellently consistent with first- + principles calculations, which proves the high accuracy of DLP. The + RMSEs for <i>{\ensuremath{\beta}}</i>- + Si<sub>3</sub>N<sub>4</sub> are quite low + (0.00550 meV/atom for energy and 7.800 meV/{\r{A}} for force). + According to the Cole-Cole formula, the microwave dielectric function + in the temperature range of 300{\textendash}1000 K is calculated by + using the deep learning molecular dynamics method. Compared with the + empirical potential, the computational results of the DLP are + consistent with the experimental results in the sense of order of + magnitude. It is also found that the DPMD performs well in terms of + computational speed. In addition, a mathematical model of the + temperature dependence of the relaxation time is established to reveal + the pattern of relaxation time varying with temperature. The high- + temperature microwave dielectric function of silicon nitride is + calculated by implementing large-scale and high-precision molecular + dynamics simulations. It provides fundamental data for promoting the + application of silicon nitride in high-temperature thermal + transmission.}, + doi = {10.7498/aps.71.20221002}, +} + + +@Article{Fu_JMaterChemA_2023_v11_p742, + author = {Shubin Fu and Dizhou Liu and Yuanpeng Deng and Menglin Li and Han Zhao + and Jingran Guo and Jian Zhou and Pengyu Zhang and Chong Wang and + Hongxuan Yu and Shixuan Dang and Jianing Zhang and Menglong Hao and + Hui Li and Xiang Xu}, + title = {{Medium-entropy ceramic aerogels for robust thermal sealing}}, + journal = {J. Mater. Chem. A}, + year = 2023, + volume = 11, + issue = 2, + pages = {742--752}, + annote = {MECA fabricated by far-field electrospinning exhibit excellent + thermomechanical stability due to the medium entropy effects and + superior high temperature thermal insulation performance due to the + thermal radiation reflection of TiO2.}, + doi = {10.1039/d2ta08264k}, +} + + +@Article{Li_PhysRevApplied_2022_v18_p064067, + author = {Tingwei Li and Peng-Hu Du and Ling Bai and Qiang Sun and Puru Jena}, + title = {{Thermoelectric Figure of Merit of a Superatomic Crystal Re6 + Se8I2 Monolayer}}, + journal = {Phys. Rev. Applied}, + year = 2022, + volume = 18, + issue = 6, + pages = 064067, + doi = {10.1103/PhysRevApplied.18.064067}, +} + + +@Article{Jiang_NatCommun_2022_v13_p6067, + author = {Shuai Jiang and Yi-Rong Liu and Teng Huang and Ya-Juan Feng and Chun- + Yu Wang and Zhong-Quan Wang and Bin-Jing Ge and Quan-Sheng Liu and + Wei-Ran Guang and Wei Huang}, + title = {{Towards fully ab initio simulation of atmospheric aerosol nucleation}}, + journal = {Nat. Commun.}, + year = 2022, + volume = 13, + issue = 1, + pages = 6067, + annote = {Atmospheric aerosol nucleation contributes to approximately half of + the worldwide cloud condensation nuclei. Despite the importance of + climate, detailed nucleation mechanisms are still poorly understood. + Understanding aerosol nucleation dynamics is hindered by the + nonreactivity of force fields (FFs) and high computational costs due + to the rare event nature of aerosol nucleation. Developing reactive + FFs for nucleation systems is even more challenging than developing + covalently bonded materials because of the wide size range and high + dimensional characteristics of noncovalent hydrogen bonding bridging + clusters. Here, we propose a general workflow that is also applicable + to other systems to train an accurate reactive FF based on a deep + neural network (DNN) and further bridge DNN-FF-based molecular + dynamics (MD) with a cluster kinetics model based on Poisson + distributions of reactive events to overcome the high computational + costs of direct MD. We found that previously reported acid-base + formation rates tend to be significantly underestimated, especially in + polluted environments, emphasizing that acid-base nucleation observed + in multiple environments should be revisited.}, + PMCID = {PMC9568664}, + doi = {10.1038/s41467-022-33783-y}, +} + + +@Article{Bayerl_DigitalDiscovery_2022_v1_p61, + author = {Dylan Bayerl and Christopher M. Andolina and Shyam Dwaraknath and + Wissam A. Saidi}, + title = {{Convergence acceleration in machine learning potentials for atomistic + simulations}}, + journal = {Digital Discovery}, + year = 2022, + volume = 1, + issue = 1, + pages = {61--69}, + annote = {Machine learning potentials (MLPs) for atomistic simulations + have an enormous prospective impact on materials modeling, offering + orders of magnitude speedup over density functional theory simulations + without appreciably sacrificing accuracy of material property + prediction.}, + doi = {10.1039/d1dd00005e}, +} + + +@Article{Xu_ACSApplMaterInterfaces_2023_vNone_pNone, + author = {Tingrui Xu and Xuejiao Li and Yang Wang and Zhongfeng Tang}, + title = {{Development of Deep Potentials of Molten + MgCl2{\textendash}NaCl and MgCl2{\textendash}KCl + Salts Driven by Machine Learning}}, + journal = {ACS Appl. Mater. Interfaces}, + year = 2023, + annote = {Molten MgCl2-based chlorides have emerged as potential thermal storage + and heat transfer materials due to high thermal stabilities and lower + costs. In this work, deep potential molecular dynamics (DPMD) + simulations by a method combination of the first principle, classical + molecular dynamics, and machine learning are performed to systemically + study the relationships of structures and thermophysical properties of + molten MgCl2-NaCl (MN) and MgCl2-KCl (MK) eutectic salts at the + temperature range of 800-1000 K. The densities, radial distribution + functions, coordination numbers, potential mean forces, specific heat + capacities, viscosities, and thermal conductivities of these two + chlorides are successfully reproduced under extended temperatures by + DPMD with a larger size (5.2 nm) and longer timescale (5 ns). It is + concluded that the higher specific heat capacity of molten MK is + originated from the strong potential mean force of Mg-Cl bonds, + whereas the molten MN performs better in heat transfer due to the + larger thermal conductivity and lower viscosity, attributed to the + weak interaction between Mg and Cl ions. Innovatively, the + plausibility and reliability of microscopic structures and macroscopic + properties for molten MN and MK verify the extensibilities of these + two deep potentials in temperatures, and these DPMD results also + provide detailed technical parameters to the simulations of other + formulated MN and MK salts.}, + doi = {10.1021/acsami.2c19272}, +} + + +@Article{Li_JPhysCondensMatter_2023_v35_p505001, + author = {Wentao Li and Chenxiu Yang}, + title = {{Thermal conductivity of van der Waals heterostructure of 2D GeS and + SnS based on machine learning interatomic potential}}, + journal = {J. Phys. Condens. Matter}, + year = 2023, + volume = 35, + issue = 50, + pages = 505001, + annote = {van der Waals heterostructures have provided an unprecedented platform + to tune many physical properties for two-dimensional materials. In + this work, thermal transport properties of van der Waals + heterostructures formed by vertical stacking of monolayers GeS and SnS + have been investigated systematically based on machine learning + interatomic potential. The effect of van der Waals interface on the + lattice thermal transport of 2D SnS and GeS can be well clarified by + introducing various stacking configurations. Our results indicate that + the van der Waals interface can strongly suppress the thermal + transport capacity for the considered heterostructures, and either the + average thermal conductivity per layer or the 2D thermal sheet + conductance for the considered heterostructures is lower than that of + corresponding monolayers. The suppressed thermal conductivity with + tunable in-plane anisotropy in SnS/GeS heterostructures can be + ascribed to the enhanced interface anharmonic scattering, and thus + exhibits obvious interface-dependent characteristics. Therefore, this + work highlights that the van der Waals interface can be employed to + effectively modulate thermal transport for the 2D puckered group-IV + monochalcogenides, and the suppressed lattice thermal conductivity + together with interface-dependent phonon transport properties in the + SnS/GeS heterostructure imply the great potential for corresponding + thermoelectrical applications.}, + doi = {10.1088/1361-648X/acf6ea}, +} + + +@Article{Qi_JournalofNonCrystallineSolids_2023_v622_p122682, + author = {Yongnian Qi and Xiaoguang Guo and Ming Li and Chongkun Wang and Qing + Mu and Ping Zhou}, + title = {{Reversible and irreversible photon-absorption in amorphous SiO2 + revealed by deep potential}}, + journal = {Journal of Non-Crystalline Solids}, + year = 2023, + volume = 622, + pages = 122682, + doi = {10.1016/j.jnoncrysol.2023.122682}, +} + + +@Article{Liang_InternationalJournalofHeatandMassTransfer_2023_v217_p124705, + author = {Fei Liang and Jing Ding and Xiaolan Wei and Gechuanqi Pan and Shule + Liu}, + title = {{Interfacial heat and mass transfer at silica/binary molten salt + interface from deep potential molecular dynamics}}, + journal = {International Journal of Heat and Mass Transfer}, + year = 2023, + volume = 217, + pages = 124705, + doi = {10.1016/j.ijheatmasstransfer.2023.124705}, +} + + +@Article{Gupta_NatCommun_2023_v14_p6884, + author = {Sunny Gupta and Xiaochen Yang and Gerbrand Ceder}, + title = {{What dictates soft clay-like lithium superionic conductor formation + from rigid salts mixture}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + issue = 1, + pages = 6884, + annote = {Soft clay-like Li-superionic conductors, integral to realizing all- + solid-state batteries, have been recently synthesized by mixing rigid- + salts. Here, through computational and experimental analysis, we + clarify how a soft clay-like material can be created from a mixture of + rigid-salts. Using molecular dynamics simulations with a deep + learning-based interatomic potential energy model, we uncover the + microscopic features responsible for soft clay-formation from ionic + solid mixtures. We find that salt mixtures capable of forming + molecular solid units on anion exchange, along with the slow kinetics + of such reactions, are key to soft-clay formation. Molecular solid + units serve as sites for shear transformation zones, and their + inherent softness enables plasticity at low stress. Extended X-ray + absorption fine structure spectroscopy confirms the formation of + molecular solid units. A general strategy for creating soft clay-like + materials from ionic solid mixtures is formulated.}, + PMCID = {PMC10613223}, + doi = {10.1038/s41467-023-42538-2}, +} + + +@Article{Zhang_ActaMaterialia_2023_v261_p119364, + author = {Jin-Yu Zhang and Zhi-Peng Sun and Dong Qiu and Fu-Zhi Dai and Yang- + Sheng Zhang and Dongsheng Xu and Wen-Zheng Zhang}, + title = {{Dislocation-mediated migration of the + {\ensuremath{\alpha}}/{\ensuremath{\beta}} interfaces in titanium}}, + journal = {Acta Materialia}, + year = 2023, + volume = 261, + pages = 119364, + doi = {10.1016/j.actamat.2023.119364}, +} + + +@Article{Liu_npjComputMater_2023_v9_p174, + author = {Yunsheng Liu and Xingfeng He and Yifei Mo}, + title = {{Discrepancies and error evaluation metrics for machine learning + interatomic potentials}}, + journal = {npj Comput Mater}, + year = 2023, + volume = 9, + issue = 1, + pages = 174, + annote = {AbstractMachine learning interatomic + potentials (MLIPs) are a promising technique for atomic modeling. + While small errors are widely reported for MLIPs, an open concern is + whether MLIPs can accurately reproduce atomistic dynamics and related + physical properties in molecular dynamics (MD) simulations. In this + study, we examine the state-of-the-art MLIPs and uncover several + discrepancies related to atom dynamics, defects, and rare events + (REs), compared to ab initio methods. We find that low averaged errors + by current MLIP testing are insufficient, and develop quantitative + metrics that better indicate the accurate prediction of atomic + dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation + metrics are demonstrated to have improved prediction in multiple + properties. The identified errors, the evaluation metrics, and the + proposed process of developing such metrics are general to MLIPs, thus + providing valuable guidance for future testing and improvements of + accurate and reliable MLIPs for atomistic modeling.}, + doi = {10.1038/s41524-023-01123-3}, +} + + +@Article{Lin_NatCommun_2023_v14_p4110, + author = {Bo Lin and Jian Jiang and Xiao Cheng Zeng and Lei Li}, + title = {{Temperature-pressure phase diagram of confined monolayer water/ice at + first-principles accuracy with a machine-learning force field}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + issue = 1, + pages = 4110, + annote = {Understanding the phase behaviour of nanoconfined water films is of + fundamental importance in broad fields of science and engineering. + However, the phase behaviour of the thinnest water film - monolayer + water - is still incompletely known. Here, we developed a machine- + learning force field (MLFF){~}at first-principles accuracy to + determine the phase diagram of monolayer water/ice in nanoconfinement + with hydrophobic walls. We observed the spontaneous formation of two + previously unreported high-density ices, namely, zigzag quasi-bilayer + ice (ZZ-qBI) and branched-zigzag quasi-bilayer ice (bZZ-qBI). Unlike + conventional bilayer ices, few inter-layer hydrogen bonds were + observed in both quasi-bilayer ices. Notably, the bZZ-qBI entails a + unique hydrogen-bonding network that consists of two distinctive types + of hydrogen bonds. Moreover, we identified, for the first time, the + stable region for the lowest-density [Formula: see text] monolayer ice + (LD-48MI) at negative pressures (<-0.3{\,}GPa). Overall, the MLFF + enables large-scale first-principle-level molecular dynamics (MD) + simulations of the spontaneous transition from the liquid water to a + plethora of monolayer ices, including hexagonal, pentagonal, square, + zigzag (ZZMI), and hexatic monolayer ices. These findings will enrich + our understanding of the phase behaviour of the nanoconfined + water/ices and provide a guide for future experimental realization of + the 2D ices.}, + PMCID = {PMC10336112}, + doi = {10.1038/s41467-023-39829-z}, +} + + +@Article{Lu_NatCommun_2023_v14_p4077, + author = {Pushun Lu and Yu Xia and Guochen Sun and Dengxu Wu and Siyuan Wu and + Wenlin Yan and Xiang Zhu and Jiaze Lu and Quanhai Niu and Shaochen Shi + and Zhengju Sha and Liquan Chen and Hong Li and Fan Wu}, + title = {{Realizing long-cycling all-solid-state Li-In||TiS2 batteries using + Li6+xMxAs1-xS5I (M=Si, Sn) sulfide solid electrolytes}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + issue = 1, + pages = 4077, + annote = {Inorganic sulfide solid-state electrolytes, especially Li6PS5X (X = + Cl, Br, I), are considered viable materials for developing all-solid- + state batteries because of their high ionic conductivity and low cost. + However, this class of solid-state electrolytes suffers from + structural and chemical instability in humid air environments and a + lack of compatibility with layered oxide positive electrode active + materials. To circumvent these issues, here, we propose + Li6+xMxAs1-xS5I (M=Si, Sn) as sulfide solid electrolytes. When the + Li6+xSixAs1-xS5I (x{\,}={\,}0.8) is tested in combination with a Li-In + negative electrode and Ti2S-based positive electrode at + 30{\,}{\textdegree}C and 30{\,}MPa, the Li-ion lab-scale Swagelok + cells demonstrate long cycle life of almost 62500 cycles at + 2.44{\,}mA{\,}cm-2, decent power performance (up to + 24.45{\,}mA{\,}cm-2) and areal capacity of 9.26 mAh cm-2 at + 0.53{\,}mA{\,}cm-2.}, + PMCID = {PMC10333182}, + doi = {10.1038/s41467-023-39686-w}, +} + + +@Article{Bore_NatCommun_2023_v14_p3349, + author = {Sigbj{\o}rn L{\o}land Bore and Francesco Paesani}, + title = {{Realistic phase diagram of water from {\textquotedblleft}first + principles{\textquotedblright} data-driven quantum simulations}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + issue = 1, + pages = 3349, + annote = {Since the experimental characterization of the low-pressure region of + water's phase diagram in the early 1900s, scientists have been on a + quest to understand the thermodynamic stability of ice polymorphs on + the molecular level. In this study, we demonstrate that combining the + MB-pol data-driven many-body potential for water, which was rigorously + derived from "first principles" and exhibits chemical accuracy, with + advanced enhanced-sampling algorithms, which correctly describe the + quantum nature of molecular motion and thermodynamic equilibria, + enables computer simulations of water's phase diagram with an + unprecedented level of realism. Besides providing fundamental insights + into how enthalpic, entropic, and nuclear quantum effects shape the + free-energy landscape of water, we demonstrate that recent progress in + "first principles" data-driven simulations, which rigorously encode + many-body molecular interactions, has opened the door to realistic + computational studies of complex molecular systems, bridging the gap + between experiments and simulations.}, + PMCID = {PMC10250386}, + doi = {10.1038/s41467-023-38855-1}, +} + + +@Article{Wang_NatCommun_2023_v14_p2924, + author = {Xiaoyang Wang and Zhenyu Wang and Pengyue Gao and Chengqian Zhang and + Jian Lv and Han Wang and Haifeng Liu and Yanchao Wang and Yanming Ma}, + title = {{Data-driven prediction of complex crystal structures of dense lithium}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + issue = 1, + pages = 2924, + annote = {Lithium (Li) is a prototypical simple metal at ambient conditions, but + exhibits remarkable changes in structural and electronic properties + under compression. There has been intense debate about the structure + of dense Li, and recent experiments offered fresh evidence for yet + undetermined crystalline phases near the enigmatic melting minimum + region in the pressure-temperature phase diagram of Li. Here, we + report on an extensive exploration of the energy landscape of Li using + an advanced crystal structure search method combined with a machine- + learning approach, which greatly expands the scale of structure + search, leading to the prediction of four complex Li crystal + structures containing up to 192 atoms in the unit cell that are + energetically competitive with known Li structures. These findings + provide a viable solution to the observed yet unidentified crystalline + phases of Li, and showcase the predictive power of the global + structure search method for discovering complex crystal structures in + conjunction with accurate machine learning potentials.}, + PMCID = {PMC10203143}, + doi = {10.1038/s41467-023-38650-y}, +} + + +@Article{Sun_NatCommun_2023_v14_p1656, + author = {Shichuan Sun and Yu He and Junyi Yang and Yufeng Lin and Jinfeng Li + and Duck Young Kim and Heping Li and Ho-Kwang Mao}, + title = {{Superionic effect and anisotropic texture in Earth{\textquoteright}s + inner core driven by geomagnetic field}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + issue = 1, + pages = 1656, + annote = {Seismological observations suggest that Earth's inner core (IC) is + heterogeneous and anisotropic. Increasing seismological observations + make the understanding of the mineralogy and mechanism for the complex + IC texture extremely challenging, and the driving force for the + anisotropic texture remains unclear. Under IC conditions, hydrogen + becomes highly diffusive like liquid in the hexagonal-close-packed + (hcp) solid Fe lattice, which is known as the superionic state. Here, + we reveal that H-ion diffusion in superionic Fe-H alloy is anisotropic + with the lowest barrier energy along the c-axis. In the presence of an + external electric field, the alignment of the Fe-H lattice with the + c-axis pointing to the field direction is energetically favorable. Due + to this effect, Fe-H alloys are aligned with the c-axis parallel to + the equatorial plane by the diffusion of the north-south dipole + geomagnetic field into the inner core. The aligned texture driven by + the geomagnetic field presents significant seismic anisotropy, which + explains the anisotropic seismic velocities in the IC, suggesting a + strong coupling between the IC structure and geomagnetic field.}, + PMCID = {PMC10039083}, + doi = {10.1038/s41467-023-37376-1}, +} + + +@Article{Wu_JChemPhys_2023_v159_pNone, + author = {Haiyi Wu and Chenxing Liang and Jinu Jeong and N R Aluru}, + title = {{From ab{~}initio to continuum: Linking multiple scales using + deep-learned forces}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 18, + annote = {We develop a deep learning-based algorithm, called DeepForce, to link + ab{~}initio physics with the continuum theory to predict concentration + profiles of confined water. We show that the deep-learned forces can + be used to predict the structural properties of water confined in a + nanochannel with quantum scale accuracy by solving the continuum + theory given by Nernst-Planck equation. The DeepForce model has an + excellent predictive performance with a relative error less than 7.6% + not only for confined water in small channel systems (L < 6{~}nm) but + also for confined water in large channel systems (L = 20{~}nm) which + are computationally inaccessible through the high accuracy ab{~}initio + molecular dynamics simulations. Finally, we note that classical + Molecular dynamics simulations can be inaccurate in capturing the + interfacial physics of water in confinement (L < 4.0{~}nm) when + quantum scale physics are neglected.}, + doi = {10.1063/5.0166927}, +} + + +@Article{Zhang_EnergyStorageMaterials_2023_v63_p103069, + author = {Yifeng Zhang and Hui Huang and Jie Tian and Chengwei Li and Yuchen + Jiang and Zeng Fan and Lujun Pan}, + title = {{Modelling electrified microporous carbon/electrolyte electrochemical + interface and unraveling charge storage mechanism by machine learning + accelerated molecular dynamics}}, + journal = {Energy Storage Materials}, + year = 2023, + volume = 63, + pages = 103069, + doi = {10.1016/j.ensm.2023.103069}, +} + + +@Article{Deng_TheoreticalandAppliedMechanicsLetters_2023_v13_p100481, + author = {Yuanpeng Deng and Chong Wang and Xiang Xu and Hui Li}, + title = {{Machine learning potential for Ab Initio phase transitions of zirconia}}, + journal = {Theoretical and Applied Mechanics Letters}, + year = 2023, + volume = 13, + issue = 6, + pages = 100481, + doi = {10.1016/j.taml.2023.100481}, +} + + +@Article{Dai_NatEnergy_2023_v8_p1221, + author = {Tao Dai and Siyuan Wu and Yaxiang Lu and Yang Yang and Yuan Liu and + Chao Chang and Xiaohui Rong and Ruijuan Xiao and Junmei Zhao and + Yanhui Liu and Weihua Wang and Liquan Chen and Yong-Sheng Hu}, + title = {{Inorganic glass electrolytes with polymer-like viscoelasticity}}, + journal = {Nat Energy}, + year = 2023, + volume = 8, + issue = 11, + pages = {1221--1228}, + doi = {10.1038/s41560-023-01356-y}, +} + + +@Article{Wang_EarthandPlanetaryScienceLetters_2023_v621_p118368, + author = {Dong Wang and Zhongqing Wu and Xin Deng}, + title = {{Thermal conductivity of Fe-bearing bridgmanite and post-perovskite: + Implications for the heat flux from the core}}, + journal = {Earth and Planetary Science Letters}, + year = 2023, + volume = 621, + pages = 118368, + doi = {10.1016/j.epsl.2023.118368}, +} + + +@Article{Hu_SciChinaChem_2023_v66_p3297, + author = {Youcheng Hu and Xiaoxiao Wang and Peng Li and Junxiang Chen and + Shengli Chen}, + title = {{Understanding the sluggish and highly variable transport kinetics of + lithium ions in LiFePO4}}, + journal = {Sci. China Chem.}, + year = 2023, + volume = 66, + issue = 11, + pages = {3297--3306}, + doi = {10.1007/s11426-023-1662-9}, +} + + +@Article{Liu_ChemicalEngineeringJournal_2023_v474_p145355, + author = {Xi Liu and Wei Sun and Xiang Hu and Junxiang Chen and Zhenhai Wen}, + title = {{Self-powered H2 generation implemented by hydrazine oxidation + assisting hybrid electrochemical cell}}, + journal = {Chemical Engineering Journal}, + year = 2023, + volume = 474, + pages = 145355, + doi = {10.1016/j.cej.2023.145355}, +} + + +@Article{He_SolidStateIonics_2023_v399_p116298, + author = {Yining He and Qian Chen and Wei Lai}, + title = {{Computational study of Na diffusion and conduction in P2- and + O3-Na2x[NixTi1-x]O2 materials with machine-learning interatomic + potentials}}, + journal = {Solid State Ionics}, + year = 2023, + volume = 399, + pages = 116298, + doi = {10.1016/j.ssi.2023.116298}, +} + + +@Article{Wan_JColloidInterfaceSci_2023_v648_p317, + author = {Xuhao Wan and Zhaofu Zhang and Anyang Wang and Jinhao Su and Wenjun + Zhou and John Robertson and Yuan Peng and Yu Zheng and Yuzheng Guo}, + title = {{Deep-learning-assisted theoretical insights into the compatibility of + environment friendly insulation medium with metal surface of power + equipment}}, + journal = {J. Colloid Interface Sci.}, + year = 2023, + volume = 648, + pages = {317--326}, + annote = {Exploring a new generation of eco-friendly gas insulation medium to + replace greenhouse gas sulphur hexafluoride (SF6) in power industry is + significant for reducing the greenhouse effect and building a low- + carbon environment. The gas-solid compatibility of insulation gas with + various electrical equipment is also of significance before practical + applications. Herein, take a promising SF6 replacing gas + trifluoromethyl sulfonyl fluoride (CF3SO2F) for example, one strategy + to theoretically evaluate the gas-solid compatibility between + insulation gas and the typical solid surfaces of common equipment was + raised. Firstly, the active site where the CF3SO2F molecule is prone + to interact with other compounds was identified. Secondly, the + interaction strength and charge transfer between CF3SO2F and four + typical solid surfaces of equipment were studied by first-principles + calculations and further analysis was conducted, with SF6 as the + control group. Then, the dynamic compatibility of CF3SO2F with solid + surfaces was investigated by large-scale molecular dynamics + simulations with the aid of deep learning. The results indicate that + CF3SO2F has excellent compatibility similar to SF6, especially in the + equipment whose contact surface is Cu, CuO, and Al2O3 due to their + similar outermost orbital electronic structures. Besides, the dynamic + compatibility with pure Al surfaces is poor. Finally, preliminary + experimental verifications indicate the validity of the strategy.}, + doi = {10.1016/j.jcis.2023.05.188}, +} + + +@Article{Li_JChemPhys_2023_v159_pNone, + author = {Zhiqiang Li and Jian Wang and Chao Yang and Linhua Liu and Jia-Yue + Yang}, + title = {{Thermal transport across TiO2{\textendash}H2O interface involving + water dissociation: Ab initio-assisted deep potential molecular + dynamics}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 14, + annote = {Water dissociation on TiO2 surfaces has been known for decades and + holds great potential in various applications, many of which require a + proper understanding of thermal transport across the TiO2-H2O + interface. Molecular dynamics (MD) simulations play an important role + in characterizing complex systems' interfacial thermal transport + properties. Nevertheless, due to the imprecision of empirical force + field potentials, the interfacial thermal transport mechanism + involving water dissociation remains to be determined. To cope with + this, a deep potential (DP) model is formulated through the + utilization of ab{~}initio datasets. This model successfully simulates + interfacial thermal transport accompanied by water dissociation on the + TiO2 surfaces. The trained DP achieves a total energy accuracy of + {\ensuremath{\sim}}238.8{~}meV and a force accuracy of + {\ensuremath{\sim}}197.05 meV/{\r{A}}. The DPMD simulations show that + water dissociation induces the formation of hydrogen bonding networks + and molecular bridges. Structural modifications further affect + interfacial thermal transport. The interfacial thermal conductance + estimated by DP is {\ensuremath{\sim}}8.54 {\texttimes} 109 W/m2{~}K, + smaller than {\ensuremath{\sim}}13.17 {\texttimes} 109 W/m2{~}K by + empirical potentials. The vibrational density of states (VDOS) + quantifies the differences between the DP model and empirical + potentials. Notably, the VDOS disparity between the adsorbed hydrogen + atoms and normal hydrogen atoms demonstrates the influence of water + dissociation on heat transfer processes. This work aims to understand + the effect of water dissociation on thermal transport at the TiO2-H2O + interface. The findings will provide valuable guidance for the thermal + management of photocatalytic devices.}, + doi = {10.1063/5.0167238}, +} + + +@Article{Wisesa_JPhysChemLett_2023_v14_p8741, + author = {Pandu Wisesa and Christopher M Andolina and Wissam A Saidi}, + title = {{Machine-Learning Accelerated First-Principles Accurate Modeling of the + Solid{\textendash}Liquid Phase Transition in MgO under Mantle + Conditions}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 39, + pages = {8741--8748}, + annote = {While accurate measurements of MgO under extreme high-pressure + conditions are needed to understand and model planetary behavior, + these studies are challenging from both experimental and computational + modeling perspectives. Herein, we accelerate density functional theory + (DFT) accurate calculations using deep neural network potentials + (DNPs) trained over multiple phases and study the melting behavior of + MgO via the two-phase coexistence (TPC) approach at 0-300 GPa and + {\ensuremath{\leq}}9600 K. The resulting DNP-TPC melting curve is in + excellent agreement with existing experimental studies. We show that + the mitigation of finite-size effects that typically skew the + predicted melting temperatures in DFT-TPC simulations in excess of + several hundred kelvin requires models with {\ensuremath{\sim}}16 000 + atoms and >100 ps molecular dynamics trajectories. In addition, the + DNP can successfully describe MgO metallization well at increased + pressures that are captured by DFT but missed by classical interatomic + potentials.}, + doi = {10.1021/acs.jpclett.3c02424}, +} + + +@Article{Zhang_ProcNatlAcadSciUSA_2023_v120_pe2309952120, + author = {Youjun Zhang and Yong Wang and Yuqian Huang and Junjie Wang and Zhixin + Liang and Long Hao and Zhipeng Gao and Jun Li and Qiang Wu and Hong + Zhang and Yun Liu and Jian Sun and Jung-Fu Lin}, + title = {{Collective motion in hcp-Fe at Earth{\textquoteright}s inner core + conditions}}, + journal = {Proc. Natl. Acad. Sci. U. S. A.}, + year = 2023, + volume = 120, + issue = 41, + pages = {e2309952120}, + annote = {Earth's inner core is predominantly composed of solid iron (Fe) and + displays intriguing properties such as strong shear softening and an + ultrahigh Poisson's ratio. Insofar, physical mechanisms to explain + these features coherently remain highly debated. Here, we have studied + longitudinal and shear wave velocities of hcp-Fe (hexagonal close- + packed iron) at relevant pressure-temperature conditions of the inner + core using in situ shock experiments and machine learning molecular + dynamics (MLMD) simulations. Our results demonstrate that the shear + wave velocity of hcp-Fe along the Hugoniot in the premelting + condition, defined as T/Tm (Tm: melting temperature of iron) above + 0.96, is significantly reduced by ~30%, while Poisson's ratio jumps to + approximately 0.44. MLMD simulations at 230 to 330 GPa indicate that + collective motion with fast diffusive atomic migration occurs in + premelting hcp-Fe primarily along [100] or [010] crystallographic + direction, contributing to its elastic softening and enhanced + Poisson's ratio. Our study reveals that hcp-Fe atoms can diffusively + migrate to neighboring positions, forming open-loop and close-loop + clusters in the inner core conditions. Hcp-Fe with collective motion + at the inner core conditions is thus not an ideal solid previously + believed. The premelting hcp-Fe with collective motion behaves like an + extremely soft solid with an ultralow shear modulus and an ultrahigh + Poisson's ratio that are consistent with seismic observations of the + region. Our findings indicate that premelting hcp-Fe with fast + diffusive motion represents the underlying physical mechanism to help + explain the unique seismic and geodynamic features of the inner core.}, + PMCID = {PMC10576103}, + doi = {10.1073/pnas.2309952120}, +} + + +@Article{Wang_Unknown_2023_v36_p573, + author = {Haidi Wang and Tao Li and Yufan Yao and Xiaofeng Liu and Weiduo Zhu + and Zhao Chen and Zhongjun Li and Wei Hu}, + title = {{Atomistic modeling of lithium materials from deep learning potential + with ab initio + accuracy}}, + year = 2023, + volume = 36, + issue = 5, + pages = {573--581}, + annote = {Lithium has been paid great attention in recent years thanks + to its significant applications for battery and lightweight alloy. + Developing a potential model with high accuracy and efficiency is + important for theoretical simulation of lithium materials. Here, we + build a deep learning potential (DP) for elemental lithium based on a + concurrent-learning scheme and DP representation of the density- + functional theory (DFT) potential energy surface (PES), the DP model + enables material simulations with close-to DFT accuracy but at much + lower computational cost. The simulations show that basic parameters, + equation of states, elasticity, defects and surface are consistent + with the first principles results. More notably, the liquid radial + distribution function based on our DP model is found to match well + with experiment data. Our results demonstrate that the developed DP + model can be used for the simulation of lithium materials.}, + doi = {10.1063/1674-0068/cjcp2211173}, +} + + +@Article{Wu_JPhysChemC_2023_v127_p19115, + author = {Chongteng Wu and Tong Liu and Xiayu Ran and Yuefeng Su and Yun Lu and + Ning Li and Lai Chen and Zhenwei Wu and Feng Wu and Duanyun Cao}, + title = {{Advancing Accurate and Efficient Surface Behavior Modeling of Al + Clusters with Machine Learning Potential}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 38, + pages = {19115--19126}, + doi = {10.1021/acs.jpcc.3c03229}, +} + + +@Article{Urata_Unknown_2023_v134_pNone, + author = {Shingo Urata and Nobuhiro Nakamura and Junghwan Kim and Hideo Hosono}, + title = {{Role of hydrogen-doping for compensating oxygen-defect in non- + stoichiometric amorphous In2O3{\ensuremath{-}}x: Modeling with + a machine-learning potential}}, + year = 2023, + volume = 134, + issue = 11, + annote = {Transparent amorphous oxide semiconductors (TAOSs) are + essential materials and ushering in information and communications + technologies. The performance of TAOS depends on the microstructures + relating to the defects and dopants. Density functional theory (DFT) + is a powerful tool to understand the structure{\textendash}property + relationship relating to electronic state; however, the computation of + DFT is expensive, which often hinders appropriate structural modeling + of amorphous materials. This study, thus, applied machine-learning + potential (MLP) to reproduce the DFT level of accuracy with enhanced + efficiency, to model amorphous In2O3 (a-In2O3), instead of expensive + molecular dynamics (MD) simulations with DFT. MLP-MD could reproduce + a-In2O3 structure closer to the experimental data in comparison with + DFT-MD and classical MD simulations with an analytical force field. + Using the relatively large models obtained by the MLP-MD simulations, + it was unraveled that the anionic hydrogen atoms bonding to indium + atoms attract electrons instead of the missing oxygen and remedy the + optical transparency of the oxygen deficient a-In2O3. The preferential + formation of metal{\textendash}H bonding through the reaction of + oxygen vacancy was demonstrated as analogous to InGaZnOx thin films + [Joonho et al., Appl. Phys. Lett. 110, 232105 (2017)]. The present + simulation suggests that the same mechanism works in a-In2O3, and our + finding on the structure{\textendash}property relationship is + informative to clarify the factors affecting the optical transparency + of In-based TAOS thin films.}, + doi = {10.1063/5.0149199}, +} + + +@Article{Zeng_ActaPhysSin_2023_v72_p187102, + author = {Qi-Yu Zeng and Bo Chen and Dong-Dong Kang and Jia-Yu Dai}, + title = {{Large scale and quantum accurate molecular dynamics simulation: Liquid + iron under extreme condition}}, + journal = {Acta Phys. Sin.}, + year = 2023, + volume = 72, + issue = 18, + pages = 187102, + annote = {Liquid iron is the major component of planetary + cores. Its structure and dynamics under high pressure and temperature + is of great significance in studying geophysics and planetary science. + However, for experimental techniques, it is still difficult to + generate and probe such a state of matter under extreme conditions, + while for theoretical method like molecular dynamics simulation, the + reliable estimation of dynamic properties requires both large + simulation size and <i>ab initio</i> accuracy, resulting + in unaffordable computational costs for traditional method. Owing to + the technical limitation, the understanding of such matters remains + limited. In this work, combining molecular dynamics simulation, we + establish a neural network potential energy surface model to study the + static and dynamic properties of liquid iron at its extreme + thermodynamic state close to core-mantle boundary. The implementation + of deep neural network extends the simulation scales from one hundred + atoms to millions of atoms within quantum accuracy. The estimated + static and dynamic structure factor show good consistency with all + available X-ray diffraction and inelastic X-ray scattering + experimental observations, while the empirical potential based on + embedding-atom-method fails to give a unified description of liquid + iron across a wide range of thermodynamic conditions. We also + demonstrate that the transport property like diffusion coefficient + exhibits a strong size effect, which requires more than at least ten + thousands of atoms to give a converged value. Our results show that + the combination of deep learning technology and molecular modelling + provides a way to describe matter realistically under extreme + conditions.}, + doi = {10.7498/aps.72.20231258}, +} + + +@Article{Shen_JAmChemSoc_2023_v145_p20511, + author = {Yidi Shen and Sergey I Morozov and Kun Luo and Qi An and William A + {Goddard Iii}}, + title = {{Deciphering the Atomistic Mechanism of Si(111)-7 {\texttimes} 7 + Surface Reconstruction Using a Machine-Learning Force Field}}, + journal = {J. Am. Chem. Soc.}, + year = 2023, + volume = 145, + issue = 37, + pages = {20511--20520}, + annote = {While the complex 7 {\texttimes} 7 structure that arises upon + annealing the Si(111) surface is well-known, the mechanism underlying + this unusual surface reconstruction has remained a mystery. Here, we + report molecular dynamics simulations using a machine-learning force + field for Si to investigate the Si(111)-7 {\texttimes} 7 surface + reconstruction from the melt. We find that there are two possible + pathways for the formation of the 7 {\texttimes} 7 structure. The + first path arises from the growth of a faulted half domain from the + metastable 5 {\texttimes} 5 phase to the final 7 {\texttimes} 7 + structure, while the second path involves the direct formation of the + 7 {\texttimes} 7 reconstruction. Both pathways involve the creation of + dimers and bridged five-membered rings, followed by the formation of + additional dimers and the stabilization of the triangular halves of + the unit cell. The corner hole is formed from the joining of several + five-member rings. The insertion of atoms below the adatoms to form a + dumbbell configuration involves extra atom diffusion or rearrangement + during the evolution of triangular halves and dimer formation along + the unit cell boundary. Our findings may provide insights for + manipulating the surface structure by introducing other atomic + species.}, + doi = {10.1021/jacs.3c06540}, +} + + +@Article{Gupta_JMaterChemA_2023_v11_p21864, + author = {Mayanak K. Gupta and Sajan Kumar and Ranjan Mittal and Sanjay K. + Mishra and Stephane Rols and Olivier Delaire and Arumugum Thamizhavel + and P. U. Sastry and Samrath L. Chaplot}, + title = {{Distinct anharmonic characteristics of phonon-driven lattice thermal + conductivity and thermal expansion in bulk MoSe2 and + WSe2}}, + journal = {J. Mater. Chem. A}, + year = 2023, + volume = 11, + issue = 40, + pages = {21864--21873}, + annote = {Machine-learning molecular dynamics simulations pave the way + to completely treat the anharmonicity of phonons. Low-energy + anharmonic modes in transition-metal dichalcogenides drive the thermal + and transport properties.}, + doi = {10.1039/d3ta03830k}, +} + + +@Article{Yu_ChemMater_2023_v35_p6651, + author = {Wei Yu and Zhaofu Zhang and Xuhao Wan and Jinhao Su and Qingzhong Gui + and Hailing Guo and Hong-xia Zhong and John Robertson and Yuzheng Guo}, + title = {{High-Accuracy Machine-Learned Interatomic Potentials for the Phase + Change Material Ge3Sb6Te5}}, + journal = {Chem. Mater.}, + year = 2023, + volume = 35, + issue = 17, + pages = {6651--6658}, + doi = {10.1021/acs.chemmater.3c00524}, +} + + +@Article{Fu_AdvFunctMaterials_2023_v33_pNone, + author = {Fangjia Fu and Xiaoxu Wang and Linfeng Zhang and Yifang Yang and + Jianhui Chen and Bo Xu and Chuying Ouyang and Shenzhen Xu and Fu{-}Zhi + Dai and Weinan E}, + title = {{Unraveling the Atomic{-}scale Mechanism of Phase Transformations and + Structural Evolutions during (de)Lithiation in Si Anodes}}, + journal = {Adv Funct Materials}, + year = 2023, + volume = 33, + issue = 37, + annote = {AbstractUnraveling the reaction paths + and structural evolutions during charging/discharging processes are + critical for the development and tailoring of silicon anodes for + high{-}capacity batteries. However, a mechanistic understanding is + still lacking due to the complex phase transformations between + crystalline (c{-}) and amorphous (a{-}) phases involved in + electrochemical cycles. In this study, by employing a newly developed + machine learning potential, the key experimental phenomena not only + reproduce, including voltage curves and structural evolution pathways, + but also provide atomic scale mechanisms associated with these + phenomena. The voltage plateaus of both the c{-}Si and a{-}Si + lithiation processes are predicted with the plateau value difference + close to experimental measurements, revealing the two{-}phase reaction + mechanism and reaction path differences. The observed voltage + hysteresis between lithiation and delithiation mainly originates from + the transformation between the c{-}Li15{- + }{\ensuremath{\delta}}Si4 and a{-}Li15{- + }{\ensuremath{\delta}}Si4 phases. Furthermore, stress accumulation is simulated + along different reaction paths, indicating a better cycling stability + of the a{-}Si anode due to the lower stress concentration. Overall, + the study provides a theoretical understanding of the thermodynamics + of the complex structural evolutions in Si anodes during + (de)lithiation processes, which may play a role in optimizations for + battery performances.}, + doi = {10.1002/adfm.202303936}, +} + + +@Article{Guo_JChemPhys_2023_v159_pNone, + author = {Yu-Xin Guo and Yong-Bin Zhuang and Jueli Shi and Jun Cheng}, + title = {{ChecMatE: A workflow package to automatically generate machine + learning potentials and phase diagrams for semiconductor alloys}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 9, + annote = {Semiconductor alloy materials are highly versatile due to their + adjustable properties; however, exploring their structural space is a + challenging task that affects the control of their properties. + Traditional methods rely on ad{~}hoc design based on the understanding + of known chemistry and crystallography, which have limitations in + computational efficiency and search space. In this work, we present + ChecMatE (Chemical Material Explorer), a software package that + automatically generates machine learning potentials (MLPs) and uses + global search algorithms to screen semiconductor alloy materials. + Taking advantage of MLPs, ChecMatE enables a more efficient and cost- + effective exploration of the structural space of materials and + predicts their energy and relative stability with ab{~}initio + accuracy. We demonstrate the efficacy of ChecMatE through a case study + of the InxGa1-xN system, where it accelerates structural exploration + at reduced costs. Our automatic framework offers a promising solution + to the challenging task of exploring the structural space of + semiconductor alloy materials.}, + doi = {10.1063/5.0166858}, +} + + +@Article{Wang_PhysRevMaterials_2023_v7_p093601, + author = {Xiao-Yang Wang and Yi-Nan Wang and Ke Xu and Fu-Zhi Dai and Hai-Feng + Liu and Guang-Hong Lu and Han Wang}, + title = {{Deep neural network potential for simulating hydrogen blistering in + tungsten}}, + journal = {Phys. Rev. Materials}, + year = 2023, + volume = 7, + issue = 9, + pages = 093601, + doi = {10.1103/PhysRevMaterials.7.093601}, +} + + +@Article{Yang_NatCatal_2023_v6_p829, + author = {Manyi Yang and Umberto Raucci and Michele Parrinello}, + title = {{Reactant-induced dynamics of lithium imide surfaces during the ammonia + decomposition process}}, + journal = {Nat Catal}, + year = 2023, + volume = 6, + issue = 9, + pages = {829--836}, + doi = {10.1038/s41929-023-01006-2}, +} + + +@Article{Stoppelman_JChemPhys_2023_v159_pNone, + author = {John P Stoppelman and Angus P Wilkinson and Jesse G McDaniel}, + title = {{Equation of state predictions for ScF3 and CaZrF6 with neural network- + driven molecular dynamics}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 8, + annote = {In silico property prediction based on density functional theory (DFT) + is increasingly performed for crystalline materials. Whether + quantitative agreement with experiment can be achieved with current + methods is often an unresolved question, and may require detailed + examination of physical effects such as electron correlation, + reciprocal space sampling, phonon anharmonicity, and nuclear quantum + effects (NQE), among others. In this work, we attempt first-principles + equation of state prediction for the crystalline materials ScF3 and + CaZrF6, which are known to exhibit negative thermal expansion (NTE) + over a broad temperature range. We develop neural network (NN) + potentials for both ScF3 and CaZrF6 trained to extensive DFT data, and + conduct direct molecular dynamics prediction of the equation(s) of + state over a broad temperature/pressure range. The NN potentials serve + as surrogates of the DFT Hamiltonian with enhanced computational + efficiency allowing for simulations with larger supercells and + inclusion of NQE utilizing path integral approaches. The conclusion of + the study is mixed: while some equation of state behavior is predicted + in semiquantitative agreement with experiment, the pressure-induced + softening phenomenon observed for ScF3 is not captured in our + simulations. We show that NQE have a moderate effect on NTE at low + temperature but does not significantly contribute to equation of state + predictions at increasing temperature. Overall, while the NN + potentials are valuable for property prediction of these NTE (and + related) materials, we infer that a higher level of electron + correlation, beyond the generalized gradient approximation density + functional employed here, is necessary for achieving quantitative + agreement with experiment.}, + doi = {10.1063/5.0157615}, +} + + +@Article{Liu_JChemTheoryComput_2023_v19_p5602, + author = {Renxi Liu and Mohan Chen}, + title = {{Characterization of the Hydrogen-Bond Network in High-Pressure Water + by Deep Potential Molecular Dynamics}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + issue = 16, + pages = {5602--5608}, + annote = {The hydrogen-bond (H-bond) network of high-pressure water is + investigated by neural-network-based molecular dynamics (MD) + simulations with first-principles accuracy. The static structure + factors (SSFs) of water at three densities, i.e., 1, 1.115, and 1.24 + g/cm3, are directly evaluated from 512 water MD trajectories, which + are in quantitative agreement with the experiments. We propose a new + method to decompose the computed SSF and identify the changes in the + SSF with respect to the changes in H-bond structures. We find that a + larger water density results in a higher probability for one or two + non-H-bonded water molecules to be inserted into the inner shell, + explaining the changes in the tetrahedrality of water under pressure. + We predict that the structure of the accepting end of water molecules + is more easily influenced by the pressure than by the donating end. + Our work sheds new light on explaining the SSF and H-bond properties + in related fields.}, + doi = {10.1021/acs.jctc.3c00445}, +} + + +@Article{Zhang_PhysRevLett_2023_v131_p076801, + author = {Chunyi Zhang and Shuwen Yue and Athanassios Z Panagiotopoulos and + Michael L Klein and Xifan Wu}, + title = {{Why Dissolving Salt in Water Decreases Its Dielectric Permittivity}}, + journal = {Phys. Rev. Lett.}, + year = 2023, + volume = 131, + issue = 7, + pages = 076801, + annote = {The dielectric permittivity of salt water decreases on dissolving more + salt. For nearly a century, this phenomenon has been explained by + invoking saturation in the dielectric response of the solvent water + molecules. Herein, we employ an advanced deep neural network (DNN), + built using data from density functional theory, to study the + dielectric permittivity of sodium chloride solutions. Notably, the + decrease in the dielectric permittivity as a function of + concentration, computed using the DNN approach, agrees well with + experiments. Detailed analysis of the computations reveals that the + dominant effect, caused by the intrusion of ionic hydration shells + into the solvent hydrogen-bond network, is the disruption of dipolar + correlations among water molecules. Accordingly, the observed decrease + in the dielectric permittivity is mostly due to increasing suppression + of the collective response of solvent waters.}, + doi = {10.1103/PhysRevLett.131.076801}, +} + + +@Article{Zhang_JPhysChemLett_2023_v14_p7141, + author = {Jidong Zhang and Wei Guo and Yugui Yao}, + title = {{Deep Potential Molecular Dynamics Study of Chapman{\textendash}Jouguet + Detonation Events of Energetic Materials}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 32, + pages = {7141--7148}, + annote = {Detonation of energetic materials (EMs) is of great importance for + military applications, while the understanding of detailed events and + mechanisms for the detonation process is scarce. In this study, the + first deep neural network potential NNP_Shock for molecular dynamics + (MD) simulation of shock-induced detonation of EMs was generated based + on a deep potential model, providing DFT accuracy but 106 times the + computational efficiency. On this basis, we employ our deep potential + to perform MD simulations of shock-induced detonation of high- + performance EM material + 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20, + C6H6N12O12) and obtain the theoretical Chapman-Jouguet (C-J) + detonation velocities and pressures directly by multiscale shock + technique (MSST) for the first time, which are in good agreement with + experiment. In addition, the Hugoniot curves and initial reaction + mechanisms were successfully obtained. Therefore, the NNP_Shock + potential is competent in research of the detonation performance and + shock sensitivity of CL-20, and the method can also be transplanted to + studies of other EMs.}, + doi = {10.1021/acs.jpclett.3c01392}, +} + + +@Article{Sowa_JPhysChemLett_2023_v14_p7215, + author = {Jakub K Sowa and Sean T Roberts and Peter J Rossky}, + title = {{Exploring Configurations of Nanocrystal Ligands Using Machine-Learned + Force Fields}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 32, + pages = {7215--7222}, + annote = {Semiconducting nanocrystals passivated with organic ligands have + emerged as a powerful platform for light harvesting, light-driven + chemical reactions, and sensing. Due to their complexity and size, + little structural information is available from experiments, making + these systems challenging to model computationally. Here, we develop a + machine-learned force field trained on DFT data and use it to + investigate the surface chemistry of a PbS nanocrystal interfaced with + acetate ligands. In doing so, we go beyond considering individual + local minimum energy geometries and, importantly, circumvent a + precarious issue associated with the assumption of a single assigned + atomic partial charge for each element in a nanocrystal, independent + of its structural position. We demonstrate that the carboxylate + ligands passivate the metal-rich surfaces by adopting a very wide + range of "tilted-bridge" and "bridge" geometries and investigate the + corresponding ligand IR spectrum. This work illustrates the potential + of machine-learned force fields to transform computational modeling of + these materials.}, + doi = {10.1021/acs.jpclett.3c01618}, +} + + +@Article{Chtchelkatchev_JChemPhys_2023_v159_pNone, + author = {N M Chtchelkatchev and R E Ryltsev and M V Magnitskaya and S M + Gorbunov and K A Cherednichenko and V L Solozhenko and V V Brazhkin}, + title = {{Local structure, thermodynamics, and melting of boron phosphide at + high pressures by deep learning-driven ab{~}initio simulations}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 6, + annote = {Boron phosphide (BP) is a (super)hard semiconductor constituted of + light elements, which is promising for high demand applications at + extreme conditions. The behavior of BP at high temperatures and + pressures is of special interest but is also poorly understood because + both experimental and conventional ab{~}initio methods are restricted + to studying refractory covalent materials. The use of machine learning + interatomic potentials is a revolutionary trend that gives a unique + opportunity for high-temperature study of materials with ab{~}initio + accuracy. We develop a deep machine learning potential (DP) for + accurate atomistic simulations of the solid and liquid phases of BP as + well as their transformations near the melting line. Our DP provides + quantitative agreement with experimental and ab{~}initio molecular + dynamics data for structural and dynamic properties. DP-based + simulations reveal that at ambient pressure, a tetrahedrally bonded + cubic BP crystal melts into an open structure consisting of two + interpenetrating sub-networks of boron and phosphorous with different + structures. Structure transformations of BP melt under compressing are + reflected by the evolution of low-pressure tetrahedral coordination to + high-pressure octahedral coordination. The main contributions to + structural changes at low pressures are made by the evolution of + medium-range order in the B-subnetwork and, at high pressures, by the + change of short-range order in the P-subnetwork. Such transformations + exhibit an anomalous behavior of structural characteristics in the + range of 12-15{~}GPa. DP-based simulations reveal that the Tm(P) curve + develops a maximum at P {\ensuremath{\approx}} 13 GPa, whereas + experimental studies provide two separate branches of the melting + curve, which demonstrate the opposite behavior. Analysis of the + results obtained raises open issues in developing machine learning + potentials for covalent materials and stimulates further experimental + and theoretical studies of melting behavior in BP.}, + doi = {10.1063/5.0165948}, +} + + +@Article{Zhang_JPhysChemB_2023_v127_p7011, + author = {Cunzhi Zhang and Marcello Puligheddu and Linfeng Zhang and Roberto Car + and Giulia Galli}, + title = {{Thermal Conductivity of Water at Extreme Conditions}}, + journal = {J. Phys. Chem. B}, + year = 2023, + volume = 127, + issue = 31, + pages = {7011--7}, + annote = {Measuring the thermal conductivity ({\ensuremath{\kappa}}) of water at + extreme conditions is a challenging task, and few experimental data + are available. We predict {\ensuremath{\kappa}} for temperatures and + pressures relevant to the conditions of the Earth mantle, between + 1,000 and 2,000 K and up to 22 GPa. We employ close to equilibrium + molecular dynamics simulations and a deep neural network potential + fitted to density functional theory data. We then interpret our + results by computing the equation of state of water on a fine grid of + points and using a simple model for {\ensuremath{\kappa}}. We find + that the thermal conductivity is weakly dependent on temperature and + monotonically increases with pressure with an approximate square-root + behavior. In addition, we show how the increase of + {\ensuremath{\kappa}} at high pressure, relative to ambient + conditions, is related to the corresponding increase in the sound + velocity. Although the relationships between the thermal conductivity, + pressure and sound velocity established here are not rigorous, they + are sufficiently accurate to allow for a robust estimate of the + thermal conductivity of water in a broad range of temperatures and + pressures, where experiments are still difficult to perform.}, + PMCID = {PMC10424233}, + doi = {10.1021/acs.jpcb.3c02972}, +} + + +@Article{Tuo_AdvFunctMaterials_2023_v33_pNone, + author = {Ping Tuo and Lei Li and Xiaoxu Wang and Jianhui Chen and Zhicheng + Zhong and Bo Xu and Fu{-}Zhi Dai}, + title = {{Spontaneous Hybrid Nano{-}Domain Behavior of the + Organic{\textendash}Inorganic Hybrid Perovskites}}, + journal = {Adv Funct Materials}, + year = 2023, + volume = 33, + issue = 32, + annote = {AbstractIn hybrid perovskites, the + organic molecules and inorganic frameworks exhibit distinct static and + dynamic characteristics. Their coupling will lead to fascinating + phenomena, such as large polarons, dynamic + Rashba{\textendash}Dresselhaus effects, etc. In this paper, deep + potential molecular dynamics (DPMD) is employed, a large{-}scale MD + simulation scheme with DFT accuracy, to study hybrid perovskites + formamidinium lead iodide (FAPbI3) and + methylamonium lead iodide (MAPbI3). A spontaneous + hybrid nano{-}domain behavior, namely multiple molecular rotation + nano{-}domains embedded into a single + [PbI6]4{\ensuremath{-}} + octahedra rotation domain, is first discovered at low temperatures. + The behavior originates from the interplay between the long range + order of molecular rotation and local lattice deformation, and + clarifies the puzzling structural features of + FAPbI3 at low temperatures. The work provides new + insights into the structural characteristics and stability of hybrid + perovskite, as well as new ideas for the structural characterization + of organic{\textendash}inorganic coupled{~}systems.}, + doi = {10.1002/adfm.202301663}, +} + + +@Article{Piaggi_JChemPhys_2023_v159_pNone, + author = {Pablo M Piaggi and Thomas E Gartner and Roberto Car and Pablo G + Debenedetti}, + title = {{Melting curves of ice polymorphs in the vicinity of the + liquid{\textendash}liquid critical point}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 5, + annote = {The possible existence of a liquid-liquid critical point in deeply + supercooled water has been a subject of debate due to the challenges + associated with providing definitive experimental evidence. The + pioneering work by Mishima and Stanley [Nature 392, 164-168 (1998)] + sought to shed light on this problem by studying the melting curves of + different ice polymorphs and their metastable continuation in the + vicinity of the expected liquid-liquid transition and its associated + critical point. Based on the continuous or discontinuous changes in + the slope of the melting curves, Mishima [Phys. Rev. Lett. 85, 334 + (2000)] suggested that the liquid-liquid critical point lies between + the melting curves of ice III and ice V. We explore this conjecture + using molecular dynamics simulations with a machine learning model + based on ab{~}initio quantum-mechanical calculations. We study the + melting curves of ices III, IV, V, VI, and XIII and find that all of + them are supercritical and do not intersect the liquid-liquid + transition locus. We also find a pronounced, yet continuous, change in + the slope of the melting lines upon crossing of the liquid locus of + maximum compressibility. Finally, we analyze the literature in light + of our findings and conclude that the scenario in which the melting + curves are supercritical is favored by the most recent computational + and experimental evidence. Although the preponderance of evidence is + consistent with the existence of a second critical point in water, the + behavior of ice polymorph melting lines does not provide strong + evidence in support of this viewpoint, according to our calculations.}, + doi = {10.1063/5.0159288}, +} + + +@Article{Zeng_JChemPhys_2023_v159_pNone, + author = {Jinzhe Zeng and Duo Zhang and Denghui Lu and Pinghui Mo and Zeyu Li + and Yixiao Chen and Mari{\'a}n Rynik and Li'ang Huang and Ziyao Li and + Shaochen Shi and Yingze Wang and Haotian Ye and Ping Tuo and Jiabin + Yang and Ye Ding and Yifan Li and Davide Tisi and Qiyu Zeng and Han + Bao and Yu Xia and Jiameng Huang and Koki Muraoka and Yibo Wang and + Junhan Chang and Fengbo Yuan and Sigbj{\o}rn L{\o}land Bore and Chun + Cai and Yinnian Lin and Bo Wang and Jiayan Xu and Jia-Xin Zhu and + Chenxing Luo and Yuzhi Zhang and Rhys E A Goodall and Wenshuo Liang + and Anurag Kumar Singh and Sikai Yao and Jingchao Zhang and Renata + Wentzcovitch and Jiequn Han and Jie Liu and Weile Jia and Darrin M + York and Weinan E and Roberto Car and Linfeng Zhang and Han Wang}, + title = {{DeePMD-kit v2: A software package for deep potential models}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 5, + annote = {DeePMD-kit is a powerful open-source software package that facilitates + molecular dynamics simulations using machine learning potentials known + as Deep Potential (DP) models. This package, which was released in + 2017, has been widely used in the fields of physics, chemistry, + biology, and material science for studying atomistic systems. The + current version of DeePMD-kit offers numerous advanced features, such + as DeepPot-SE, attention-based and hybrid descriptors, the ability to + fit tensile properties, type embedding, model deviation, DP-range + correction, DP long range, graphics processing unit support for + customized operators, model compression, non-von Neumann molecular + dynamics, and improved usability, including documentation, compiled + binary packages, graphical user interfaces, and application + programming interfaces. This article presents an overview of the + current major version of the DeePMD-kit package, highlighting its + features and technical details. Additionally, this article presents a + comprehensive procedure for conducting molecular dynamics as a + representative application, benchmarks the accuracy and efficiency of + different models, and discusses ongoing developments.}, + PMCID = {PMC10445636}, + doi = {10.1063/5.0155600}, +} + + +@Article{Hou_AngewChemIntEdEngl_2023_v62_pe202304205, + author = {Pengfei Hou and Yumiao Tian and Yu Xie and Fei Du and Gang Chen and + Aleksandra Vojvodic and Jianzhong Wu and Xing Meng}, + title = {{Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by + Active{-}Learning{-}Potential Molecular{-}Dynamics Simulation}}, + journal = {Angew. Chem. Int. Ed. Engl.}, + year = 2023, + volume = 62, + issue = 32, + pages = {e202304205}, + annote = {MXenes are 2D materials with great potential in various applications. + However, the degradation of MXenes in humid environments has become a + main obstacle in their practical use. Here we combine deep neural + networks and an active learning scheme to develop a neural network + potential (NNP) for aqueous MXene systems with ab initio precision but + low cost. The oxidation behaviors of super large aqueous MXene systems + are investigated systematically at nanosecond timescales for the first + time. The oxidation process of MXenes is clearly displayed at the + atomic level. Free protons and oxides greatly inhibit subsequent + oxidation reactions, leading to the degree of oxidation of MXenes to + exponentially decay with time, which is consistent with the oxidation + rate of MXenes measured experimentally. Importantly, this + computational study represents the first exploration of the kinetic + process of oxidation of super-sized aqueous MXene systems. It opens a + promising avenue for the future development of effective protection + strategies aimed at controlling the stability of MXenes.}, + doi = {10.1002/anie.202304205}, +} + + +@Article{Andolina_DigitalDiscovery_2023_v2_p1070, + author = {Christopher M. Andolina and Wissam A. Saidi}, + title = {{Highly transferable atomistic machine-learning potentials from curated + and compact datasets across the periodic table}}, + journal = {Digital Discovery}, + year = 2023, + volume = 2, + issue = 4, + pages = {1070--1077}, + annote = {Machine learning atomistic potentials (MLPs) trained using + density functional theory (DFT) datasets allow for the modeling of + complex material properties with near-DFT accuracy while imposing a + fraction of its computational cost.}, + doi = {10.1039/d3dd00046j}, +} + + +@Article{Ren_NatMater_2023_v22_p999, + author = {Qingyong Ren and Mayanak K Gupta and Min Jin and Jingxuan Ding and + Jiangtao Wu and Zhiwei Chen and Siqi Lin and Oscar Fabelo and Jose + Alberto Rodr{\'\i}guez-Velamaz{\'a}n and Maiko Kofu and Kenji Nakajima + and Marcell Wolf and Fengfeng Zhu and Jianli Wang and Zhenxiang Cheng + and Guohua Wang and Xin Tong and Yanzhong Pei and Olivier Delaire and + Jie Ma}, + title = {{Extreme phonon anharmonicity underpins superionic diffusion and + ultralow thermal conductivity in argyrodite Ag8SnSe6}}, + journal = {Nat. Mater.}, + year = 2023, + volume = 22, + issue = 8, + pages = {999--1006}, + annote = {Ultralow thermal conductivity and fast ionic diffusion endow + superionic materials with excellent performance both as thermoelectric + converters and as solid-state electrolytes. Yet the correlation and + interdependence between these two features remain unclear owing to a + limited understanding of their complex atomic dynamics. Here we + investigate ionic diffusion and lattice dynamics in argyrodite + Ag8SnSe6 using synchrotron X-ray and neutron scattering techniques + along with machine-learned molecular dynamics. We identify a critical + interplay of the vibrational dynamics of mobile Ag and a host + framework that controls the overdamping of low-energy Ag-dominated + phonons into a quasi-elastic response, enabling superionicity. + Concomitantly, the persistence of long-wavelength transverse acoustic + phonons across the superionic transition challenges a proposed + 'liquid-like thermal conduction' picture. Rather, a striking thermal + broadening of low-energy phonons, starting even below 50{\,}K, reveals + extreme phonon anharmonicity and weak bonding as underlying features + of the potential energy surface responsible for the ultralow thermal + conductivity (<0.5{\,}W{\,}m-1{\,}K-1) and fast diffusion. Our results + provide fundamental insights into the complex atomic dynamics in + superionic materials for energy conversion and storage.}, + doi = {10.1038/s41563-023-01560-x}, +} + + +@Article{Xiao_Unknown_2023_v123_pNone, + author = {R. L. Xiao and K. L. Liu and Y. Ruan and B. Wei}, + title = {{Rapid acquisition of liquid thermophysical properties from pure metals + to quaternary alloys by proposing a machine learning strategy}}, + year = 2023, + volume = 123, + issue = 5, + annote = {The establishment of reliable materials genome databases + involving the thermophysical properties of liquid metals and alloys + promotes the progress of materials research and development, whereas + acquiring these properties imposes great challenges on experimental + investigation. Here, we proposed a deep learning method and achieved a + deep neural network (DNN) interatomic potential for the entire + Ti{\textendash}Ni{\textendash}Cr{\textendash}Al system from pure + metals to quaternary alloys. This DNN potential exhibited sufficient + temperature and compositional transformability which extended beyond + the training and provided the prediction of the liquid structure and + thermophysical properties for metallic materials with both density + functional theory accuracy and classic molecular dynamics efficiency. + The predicted results agreed well with the reported experimental data. + This work opens a feasible way to address the challenges of rapidly + and accurately acquiring thermophysical properties data for liquid + pure metals and multicomponent alloys, covering a broad temperature + range from superheated to undercooled state.}, + doi = {10.1063/5.0160046}, +} + + +@Article{Liu_JChemPhys_2023_v159_pNone_2, + author = {Dongfei Liu and Jianzhong Wu and Diannan Lu}, + title = {{Transferability evaluation of the deep potential model for simulating + water-graphene confined system}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 4, + annote = {Machine learning potentials (MLPs) are poised to combine the accuracy + of ab{~}initio predictions with the computational efficiency of + classical molecular dynamics (MD) simulation. While great progress has + been made over the last two decades in developing MLPs, there is still + much to be done to evaluate their model transferability and facilitate + their development. In this work, we construct two deep potential (DP) + models for liquid water near graphene surfaces, Model S and Model F, + with the latter having more training data. A concurrent learning + algorithm (DP-GEN) is adopted to explore the configurational space + beyond the scope of conventional ab{~}initio MD simulation. By + examining the performance of Model S, we find that an accurate + prediction of atomic force does not imply an accurate prediction of + system energy. The deviation from the relative atomic force alone is + insufficient to assess the accuracy of the DP models. Based on the + performance of Model F, we propose that the relative magnitude of the + model deviation and the corresponding root-mean-square error of the + original test dataset, including energy and atomic force, can serve as + an indicator for evaluating the accuracy of the model prediction for a + given structure, which is particularly applicable for large systems + where density functional theory calculations are infeasible. In + addition to the prediction accuracy of the model described above, we + also briefly discuss simulation stability and its relationship to the + former. Both are important aspects in assessing the transferability of + the MLP model.}, + doi = {10.1063/5.0153196}, +} + + +@Article{Deng_ACSNano_2023_v17_p14099, + author = {Yuanpeng Deng and Shubin Fu and Jingran Guo and Xiang Xu and Hui Li}, + title = {{Anisotropic Collective Variables with Machine Learning Potential for + Ab Initio Crystallization of Complex Ceramics}}, + journal = {ACS Nano}, + year = 2023, + volume = 17, + issue = 14, + pages = {14099--14113}, + annote = {Enhanced sampling molecular dynamics (MD) simulations have been + extensively used in the phase transition study of simple crystalline + materials, such as aluminum, silica, and ice. However, MD simulation + of the crystallization process for complex crystalline materials still + faces a formidable challenge due to their multicomponent induced + multiphase problem. Here, we realize the ab initio accuracy MD + crystallization simulations of complex ceramics by using anisotropic + collective variables (CVs) and machine learning (ML) potential. The + anisotropic X-ray diffraction intensity CVs provide precise + identification of complex crystal structures with detailed + crystallography information, while the ML potential makes it feasible + to further perform enhanced sampling simulations with ab initio + accuracy. We verify the universality and accuracy of this method + through complex ceramics with three kinds of representative + structures, i.e., Ti3SiC2 for the MAX structure, zircon for the + mineral structure, and lead zirconate titanate for the perovskite + structure. It demonstrates exceptional efficiency and ab initio + quality in achieving crystallization and generating free energy + surfaces of all these ceramics, facilitating the analysis and design + of complex crystalline materials.}, + doi = {10.1021/acsnano.3c04602}, +} + + +@Article{Crippa_ProcNatlAcadSciUSA_2023_v120_pe2300565120, + author = {Martina Crippa and Annalisa Cardellini and Cristina Caruso and + Giovanni M Pavan}, + title = {{Detecting dynamic domains and local fluctuations in complex molecular + systems via timelapse neighbors shuffling}}, + journal = {Proc. Natl. Acad. Sci. U. S. A.}, + year = 2023, + volume = 120, + issue = 30, + pages = {e2300565120}, + annote = {It is known that the behavior of many complex systems is controlled by + local dynamic rearrangements or fluctuations occurring within them. + Complex molecular systems, composed of many molecules interacting with + each other in a Brownian storm, make no exception. Despite the rise of + machine learning and of sophisticated structural descriptors, + detecting local fluctuations and collective transitions in complex + dynamic ensembles remains often difficult. Here, we show a machine + learning framework based on a descriptor which we name Local + Environments and Neighbors Shuffling (LENS), that allows identifying + dynamic domains and detecting local fluctuations in a variety of + systems in an abstract and efficient way. By tracking how much the + microscopic surrounding of each molecular unit changes over time in + terms of neighbor individuals, LENS allows characterizing the global + (macroscopic) dynamics of molecular systems in phase transition, + phases-coexistence, as well as intrinsically characterized by local + fluctuations (e.g., defects). Statistical analysis of the LENS time + series data extracted from molecular dynamics trajectories of, for + example, liquid-like, solid-like, or dynamically diverse complex + molecular systems allows tracking in an efficient way the presence of + different dynamic domains and of local fluctuations emerging within + them. The approach is found robust, versatile, and applicable + independently of the features of the system and simply provided that a + trajectory containing information on the relative motion of the + interacting units is available. We envisage that "such a LENS" will + constitute a precious basis for exploring the dynamic complexity of a + variety of systems and, given its abstract definition, not necessarily + of molecular ones.}, + PMCID = {PMC10372573}, + doi = {10.1073/pnas.2300565120}, +} + + +@Article{Guo_JChemPhys_2023_v159_pNone_2, + author = {Longfei Guo and Tao Jin and Shuang Shan and Quan Tang and Zhen Li and + Chongyang Wang and Junpeng Wang and Bowei Pan and Qiao Wang and Fuyi + Chen}, + title = {{Structural transformations in single-crystalline AgPd nanoalloys from + multiscale deep potential molecular dynamics}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 2, + annote = {AgPd nanoalloys often undergo structural evolution during catalytic + reactions; the mechanism underlying such restructuring remains largely + unknown due to the use of oversimplified interatomic potentials in + simulations. Herein, a deep-learning potential is developed for AgPd + nanoalloys based on a multiscale dataset spanning from nanoclusters to + bulk configurations, exhibits precise predictions of mechanical + properties and formation energies with near-density functional theory + accuracy, calculates the surface energies closer to experimental + values compared to those obtained by Gupta potentials, and is applied + to investigate the shape reconstruction of single-crystalline AgPd + nanoalloys from cuboctahedron (Oh) to icosahedron (Ih) geometries. The + Oh to Ih shape restructuring is thermodynamically favorable and occurs + at 11 and 92{~}ps for Pd55 \at Ag254 and Ag147 \at Pd162 nanoalloys, + respectively. During the shape reconstruction of Pd \at Ag nanoalloys, + concurrent surface restructuring of the (100) facet and internal + multi-twinned phase change are observed with collaborative displacive + characters. The presence of vacancies can influence the final product + and reconstructing rate of Pd \at Ag core-shell nanoalloys. The Ag outward + diffusion on Ag \at Pd nanoalloys is more pronounced in Ih geometry + compared to Oh geometry and can be further accelerated by the Oh to Ih + deformation. The deformation of single-crystalline Pd \at Ag nanoalloys is + characterized by a displacive transformation involving the + collaborative displacement of a large number of atoms, distinguishing + it from the diffusion-coupled transformation of Ag \at Pd nanoalloys.}, + doi = {10.1063/5.0158918}, +} + + +@Article{Liu_JChemPhys_2023_v159_pNone, + author = {Da-Jiang Liu and James W Evans}, + title = {{Fluorine spillover for ceria- vs silica-supported palladium + nanoparticles: A MD study using machine learning potentials}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 2, + annote = {Supported metallic nanoparticles play a central role in catalysis. + However, predictive modeling is particularly challenging due to the + structural and dynamic complexity of the nanoparticle and its + interface with the support, given that the sizes of interest are often + well beyond those accessible via traditional ab{~}initio methods. With + recent advances in machine learning, it is now feasible to perform MD + simulations with potentials retaining near-density-functional theory + (DFT) accuracy, which can elucidate the growth and relaxation of + supported metal nanoparticles, as well as reactions on those + catalysts, at temperatures and time scales approaching those relevant + to experiments. Furthermore, the surfaces of the support materials can + also be modeled realistically through simulated annealing to include + effects such as defects and amorphous structures. We study the + adsorption of fluorine atoms on ceria and silica supported palladium + nanoparticles using machine learning potential trained by DFT data + using the DeePMD framework. We show defects on ceria and Pd/ceria + interfaces are crucial for the initial adsorption of fluorine, while + the interplay between Pd and ceria and the reverse oxygen migration + from ceria to Pd control spillover of fluorine from Pd to ceria at + later stages. In contrast, silica supports do not induce fluorine + spillover from Pd particles.}, + doi = {10.1063/5.0147132}, +} + + +@Article{Ding_JChemPhys_2023_v159_pNone, + author = {Zhutian Ding and Annabella Selloni}, + title = {{Modeling the aqueous interface of amorphous TiO2 using deep potential + molecular dynamics}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + issue = 2, + annote = {Amorphous titanium dioxide (a-TiO2) is widely used as a coating + material in applications such as electrochemistry and self-cleaning + surfaces where its interface with water has a central role. However, + little is known about the structures of the a-TiO2 surface and aqueous + interface, particularly at the microscopic level. In this work, we + construct a model of the a-TiO2 surface via a cut-melt-and-quench + procedure based on molecular dynamics simulations with deep neural + network potentials (DPs) trained on density functional theory data. + After interfacing the a-TiO2 surface with water, we investigate the + structure and dynamics of the resulting system using a combination of + DP-based molecular dynamics (DPMD) and ab{~}initio molecular dynamics + (AIMD) simulations. Both AIMD and DPMD simulations reveal that the + distribution of water on the a-TiO2 surface lacks distinct layers + normally found at the aqueous interface of crystalline TiO2, leading + to an {\ensuremath{\sim}}10 times faster diffusion of water at the + interface. Bridging hydroxyls (Ti2-ObH) resulting from water + dissociation decay several times more slowly than terminal hydroxyls + (Ti-OwH) due to fast Ti-OwH2 {\textrightarrow} Ti-OwH proton exchange + events. These results provide a basis for a detailed understanding of + the properties of a-TiO2 in electrochemical environments. Moreover, + the procedure of generating the a-TiO2-interface employed here is + generally applicable to studying the aqueous interfaces of amorphous + metal oxides.}, + doi = {10.1063/5.0157188}, +} + + +@Article{Xie_JPhysChemC_2023_v127_p13228, + author = {Jun-Zhong Xie and Hong Jiang}, + title = {{Revealing Carbon Vacancy Distribution on + {\ensuremath{\alpha}}-MoC1{\textendash}x Surfaces by + Machine-Learning Force-Field-Aided Cluster Expansion Approach}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 27, + pages = {13228--13237}, + doi = {10.1021/acs.jpcc.3c01941}, +} + + +@Article{Huo_JChemTheoryComput_2023_v19_p4243, + author = {Jun Huo and Jianghao Chen and Pei Liu and Benkun Hong and Jian Zhang + and Hao Dong and Shuhua Li}, + title = {{Microscopic Mechanism of Proton Transfer in Pure Water under Ambient + Conditions}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + issue = 13, + pages = {4243--4254}, + annote = {Water molecules and the associated proton transfer (PT) are prevalent + in chemical and biological systems and have been a hot research topic. + Spectroscopic characterization and ab initio molecular dynamics (AIMD) + simulations have previously revealed insights into acidic and basic + liquids. Presumably, the situation in the acidic/basic solution is not + necessarily the same as in pure water; in addition, the autoionization + constant for water is only 10-14 under ambient conditions, making the + study of PT in pure water challenging. To overcome this issue, we + modeled periodic water box systems containing 1000 molecules for tens + of nanoseconds based on a neural network potential (NNP) with quantum + mechanical accuracy. The NNP was generated by training a dataset + containing the energies and atomic forces of 17 075 configurations of + periodic water box systems, and these data points were calculated at + the MP2 level that considers electron correlation effects. We found + that the size of the system and the duration of the simulation have a + significant impact on the convergence of the results. With these + factors considered, our simulations showed that hydronium (H3O+) and + hydroxide (OH-) ions in water have distinct hydration structures, + thermodynamic and kinetic properties, e.g., the longer-lasting and + more stable hydrated structure of OH- ions than that of H3O+, as well + as a significantly higher free energy barrier for the OH--associated + PT than that of H3O+, leading the two to exhibit completely different + PT behaviors. Given these characteristics, we further found that PT + via OH- ions tends not to occur multiple times or between many + molecules. In contrast, PT via H3O+ can synergistically occur among + multiple molecules and prefers to adopt a cyclic pattern among three + water molecules, while it occurs mostly in a chain pattern when more + water molecules are involved. Therefore, our studies provide a + detailed and solid microscopic explanation for the PT process in pure + water.}, + doi = {10.1021/acs.jctc.3c00244}, +} + + +@Article{Ko_JChemTheoryComput_2023_v19_p4182, + author = {Hsin-Yu Ko and Marcos F {Calegari Andrade} and Zachary M Sparrow and + Ju-An Zhang and Robert A {DiStasio Jr}}, + title = {{High-Throughput Condensed-Phase Hybrid Density Functional Theory for + Large-Scale Finite-Gap Systems: The SeA Approach}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + issue = 13, + pages = {4182--4201}, + annote = {High-throughput electronic structure calculations (often performed + using density functional theory (DFT)) play a central role in + screening existing and novel materials, sampling potential energy + surfaces, and generating data for machine learning applications. By + including a fraction of exact exchange (EXX), hybrid functionals + reduce the self-interaction error in semilocal DFT and furnish a more + accurate description of the underlying electronic structure, albeit at + a computational cost that often prohibits such high-throughput + applications. To address this challenge, we have constructed a robust, + accurate, and computationally efficient framework for high-throughput + condensed-phase hybrid DFT and implemented this approach in the PWSCF + module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = + SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected + columns of the density matrix method (SCDM, a robust noniterative + orbital localization scheme that sidesteps system-dependent + optimization protocols), (ii) a recently extended version of exx (a + black-box linear-scaling EXX algorithm that exploits sparsity between + localized orbitals in real space when evaluating the action of the + standard/full-rank V^xx operator), and (iii) adaptively compressed + exchange (ACE, a low-rank V^xx approximation). In doing so, SeA + harnesses three levels of computational savings: pair selection and + domain truncation from SCDM + exx (which only considers spatially + overlapping orbitals on orbital-pair-specific and system-size- + independent domains) and low-rank V^xx approximation from ACE (which + reduces the number of calls to SCDM + exx during the self-consistent + field (SCF) procedure). Across a diverse set of 200 nonequilibrium + (H2O)64 configurations (with densities spanning 0.4-1.7 g/cm3), SeA + provides a 1-2 order-of-magnitude speedup in the overall time-to- + solution, i.e., {\ensuremath{\approx}}8-26{\texttimes} compared to the + convolution-based PWSCF(ACE) implementation in QE and + {\ensuremath{\approx}}78-247{\texttimes} compared to the conventional + PWSCF(Full) approach, and yields energies, ionic forces, and other + properties with high fidelity. As a proof-of-principle high-throughput + application, we trained a deep neural network (DNN) potential for + ambient liquid water at the hybrid DFT level using SeA via an actively + learned data set with {\ensuremath{\approx}}8,700 (H2O)64 + configurations. Using an out-of-sample set of (H2O)512 configurations + (at nonambient conditions), we confirmed the accuracy of this SeA- + trained potential and showcased the capabilities of SeA by computing + the ground-truth ionic forces in this challenging system containing + >1,500 atoms.}, + doi = {10.1021/acs.jctc.2c00827}, +} + + +@Article{Ran_JPhysChemLett_2023_v14_p6028, + author = {Jingyi Ran and Bipeng Wang and Yifan Wu and Dongyu Liu and Carlos + {Mora Perez} and Andrey S Vasenko and Oleg V Prezhdo}, + title = {{Halide Vacancies Create No Charge Traps on Lead Halide Perovskite + Surfaces but Can Generate Deep Traps in the Bulk}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 26, + pages = {6028--6036}, + annote = {Metal halide perovskites (MHPs) have attracted attention because of + their high optoelectronic performance that is fundamentally rooted in + the unusual properties of MHP defects. By developing an ab initio- + based machine-learning force field, we sample the structural dynamics + of MHPs on a nanosecond time scale and show that halide vacancies + create midgap trap states in the MHP bulk but not on a surface. Deep + traps result from Pb-Pb dimers that can form across the vacancy in + only the bulk. The required shortening of the Pb-Pb distance by nearly + 3 {\r{A}} is facilitated by either charge trapping or 50 ps thermal + fluctuations. The large-scale structural deformations are possible + because MHPs are soft. Halide vacancies on the MHP surface create no + deep traps but separate electrons from holes, keeping the charges + mobile. This is particularly favorable for MHP quantum dots, which do + not require sophisticated surface passivation to emit light and blink + less than quantum dots formed from traditional inorganic + semiconductors.}, + doi = {10.1021/acs.jpclett.3c01231}, +} + + +@Article{Wen_InternationalJournalofPlasticity_2023_v166_p103644, + author = {Tongqi Wen and Anwen Liu and Rui Wang and Linfeng Zhang and Jian Han + and Han Wang and David J. Srolovitz and Zhaoxuan Wu}, + title = {{Modelling of dislocations, twins and crack-tips in HCP and BCC Ti}}, + journal = {International Journal of Plasticity}, + year = 2023, + volume = 166, + pages = 103644, + doi = {10.1016/j.ijplas.2023.103644}, +} + + +@Article{Fan_JournalofEnergyChemistry_2023_v82_p239, + author = {Xue-Ting Fan and Xiao-Jian Wen and Yong-Bin Zhuang and Jun Cheng}, + title = {{Molecular insight into the GaP(110)-water interface using machine + learning accelerated molecular dynamics}}, + journal = {Journal of Energy Chemistry}, + year = 2023, + volume = 82, + pages = {239--247}, + doi = {10.1016/j.jechem.2023.03.013}, +} + + +@Article{Qu_JElectronMater_2023_v52_p4475, + author = {Ruijin Qu and Yawei Lv and Zhihong Lu}, + title = {{A Deep Neural Network Potential to Study the Thermal Conductivity of + MnBi2Te4 and Bi2Te3/MnBi2Te4 Superlattice}}, + journal = {J. Electron. Mater.}, + year = 2023, + volume = 52, + issue = 7, + pages = {4475--4483}, + doi = {10.1007/s11664-023-10403-z}, +} + + +@Article{CalegariAndrade_JPhysChemLett_2023_v14_p5560, + author = {Marcos F {Calegari Andrade} and Tuan Anh Pham}, + title = {{Probing Confinement Effects on the Infrared Spectra of Water with Deep + Potential Molecular Dynamics Simulations}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 24, + pages = {5560--5566}, + annote = {The hydrogen-bond network of confined water is expected to deviate + from that of the bulk liquid, yet probing these deviations remains a + significant challenge. In this work, we combine large-scale molecular + dynamics simulations with machine learning potential derived from + first-principles calculations to examine the hydrogen bonding of water + confined in carbon nanotubes (CNTs). We computed and compared the + infrared spectrum (IR) of confined water to existing experiments to + elucidate confinement effects. For CNTs with diameters >1.2 nm, we + find that confinement imposes a monotonic effect on the hydrogen-bond + network and on the IR spectrum of water. In contrast, confinement + below 1.2 nm CNT diameter affects the water structure in a complex + fashion, leading to a strong directional dependence of hydrogen + bonding that varies nonlinearly with the CNT diameter. When integrated + with existing IR measurements, our simulations provide a new + interpretation for the IR spectrum of water confined in CNTs, pointing + to previously unreported aspects of hydrogen bonding in this system. + This work also offers a general platform for simulating water in CNTs + with quantum accuracy on time and length scales beyond the reach of + conventional first-principles approaches.}, + doi = {10.1021/acs.jpclett.3c01054}, +} + + +@Article{Wang_JPhysChemC_2023_v127_p11369, + author = {Jing Wang and Xin Wang and Hua Zhu and Dingguo Xu}, + title = {{Investigating the Hydroxyl Reorientation in Hydroxyapatite Using + Machine Learning Potentials}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 23, + pages = {11369--11377}, + doi = {10.1021/acs.jpcc.3c02426}, +} + + +@Article{Li_Unknown_2023_v133_pNone, + author = {Zhiqiang Li and Xinlei Duan and Linhua Liu and Jia-Yue Yang}, + title = {{Temperature-dependent microwave dielectric permittivity of gallium + oxide: A deep potential molecular dynamics study}}, + year = 2023, + volume = 133, + issue = 22, + annote = {The microwave (MW) dielectric permittivity of gallium oxide + ({\ensuremath{\beta}}-Ga2O3) fundamentally determines its interaction + with an electromagnetic wave in bulk power. Yet, there is a lack of + experimental data due to limitations of high-temperature MW dielectric + measurements and the large uncertainty under variable-temperature + conditions. Herein, we develop a deep potential (DP) based on density + functional theory (DFT) results and apply deep potential molecular + dynamics (DPMD) for accurately predicting temperature-dependent MW + dielectric permittivity of {\ensuremath{\beta}}-Ga2O3. The predicted + energies and forces by DP demonstrate excellent agreement with DFT + results, and DPMD successfully simulates systems up to 1280 atoms with + quantum precision over nanosecond scales. Overall, the real part of + the MW dielectric permittivity decreases with rising frequency, but + the dielectric loss increases. The MW dielectric permittivity + gradually increases as the temperature increases, which is closely + related to the reduced dielectric relaxation time and increased static + and high-frequency dielectric constants. Besides, the oxygen vacancy + defects significantly reduce the relaxation time; however, augmenting + the defect concentration will cause a slight rise in relaxation time. + The electron localization function analysis reveals that more free + electrons and low localization of electrons produced by high defect + concentrations facilitate the increased relaxation time. This study + provides an alternative route to investigate the temperature-dependent + MW permittivity of {\ensuremath{\beta}}-Ga2O3, which attains prime + importance for its potential applications in RF and power + electronics.}, + doi = {10.1063/5.0149447}, +} + + +@Article{Zhuang_JPhysChemC_2023_v127_p10532, + author = {Yong-Bin Zhuang and Jun Cheng}, + title = {{Deciphering the Anomalous Acidic Tendency of Terminal Water at + Rutile(110){\textendash}Water Interfaces}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 22, + pages = {10532--10540}, + doi = {10.1021/acs.jpcc.3c01870}, +} + + +@Article{Caruso_JChemPhys_2023_v158_pNone, + author = {Cristina Caruso and Annalisa Cardellini and Martina Crippa and Daniele + Rapetti and Giovanni M Pavan}, + title = {{TimeSOAP: Tracking high-dimensional fluctuations in complex + molecular systems via time variations of SOAP spectra}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 21, + annote = {Many molecular systems and physical phenomena are controlled by local + fluctuations and microscopic dynamical rearrangements of the + constitutive interacting units that are often difficult to detect. + This is the case, for example, of phase transitions, phase equilibria, + nucleation events, and defect propagation, to mention a few. A + detailed comprehension of local atomic environments and of their + dynamic rearrangements is essential to understand such phenomena and + also to draw structure-property relationships useful to unveil how to + control complex molecular systems. Considerable progress in the + development of advanced structural descriptors [e.g., Smooth Overlap + of Atomic Position (SOAP), etc.] has certainly enhanced the + representation of atomic-scale simulations data. However, despite such + efforts, local dynamic environment rearrangements still remain + difficult to elucidate. Here, exploiting the structurally rich + description of atomic environments of SOAP and building on the concept + of time-dependent local variations, we developed a SOAP-based + descriptor, TimeSOAP ({\ensuremath{\tau}}SOAP), which essentially + tracks time variations in local SOAP environments surrounding each + molecule (i.e., each SOAP center) along ensemble trajectories. We + demonstrate how analysis of the time-series {\ensuremath{\tau}}SOAP + data and of their time derivatives allows us to detect dynamic domains + and track instantaneous changes of local atomic arrangements (i.e., + local fluctuations) in a variety of molecular systems. The approach is + simple and general, and we expect that it will help shed light on a + variety of complex dynamical phenomena.}, + doi = {10.1063/5.0147025}, +} + + +@Article{Wang_Unknown_2023_v122_pNone, + author = {Q. Wang and C. H. Zheng and M. X. Li and L. Hu and H. P. Wang and B. + Wei}, + title = {{A genome dependence of metastable phase selection on atomic structure + for undercooled liquid Nb90Si10 hypoeutectic alloy}}, + year = 2023, + volume = 122, + issue = 23, + annote = {The phase selection mechanism within undercooled liquid + Nb90Si10 hypoeutectic alloy was investigated by electrostatic + levitation technique combined with deep neural network molecular + dynamics. A stepwise-solidification procedure was conducted, where the + primary phase and eutectic microstructure successively solidified from + undercooled liquid alloy and undercooled residual liquid, + respectively. The intermetallic phase of the eutectic structure + transfers from Nb3Si to {\ensuremath{\beta}}Nb5Si3 and finally into + {\ensuremath{\alpha}}Nb5Si3 compound with the increase in liquid + undercooling. The deep neural network molecular dynamic simulations + have shown that the phase selection between Nb3Si and Nb5Si3 is mainly + controlled by the short-range order of residual liquid, considering + that the predominant short-range configuration transforms from Nb3Si- + like to Nb5Si3-like structures. The {\ensuremath{\alpha}}Nb5Si3-like + medium-range order, which is characterized by vertex-connected + {\ensuremath{\langle}}0,2,8,4{\ensuremath{\rangle}} clusters, is shown + to significantly influence the competitive nucleation of the + {\ensuremath{\alpha}}Nb5Si3 and {\ensuremath{\beta}}Nb5Si3 phases. The + residual liquid favors the {\ensuremath{\alpha}}Nb5Si3-like medium- + range order rather than {\ensuremath{\beta}}Nb5Si3 at large + undercoolings, which explains the transformation from + {\ensuremath{\beta}}Nb5Si3 to {\ensuremath{\alpha}}Nb5Si3.}, + doi = {10.1063/5.0152293}, +} + + +@Article{Fronzi_Nanomaterials_2023_v13_p1832, + author = {Marco Fronzi and Roger D Amos and Rika Kobayashi}, + title = {{Evaluation of Machine Learning Interatomic Potentials for Gold + Nanoparticles{\textemdash}Transferability towards Bulk}}, + journal = {Nanomaterials (Basel).}, + year = 2023, + volume = 13, + issue = 12, + pages = 1832, + annote = {We analyse the efficacy of machine learning (ML) interatomic + potentials (IP) in modelling gold (Au) nanoparticles. We have explored + the transferability of these ML models to larger systems and + established simulation times and size thresholds necessary for + accurate interatomic potentials. To achieve this, we compared the + energies and geometries of large Au nanoclusters using VASP and LAMMPS + and gained better understanding of the number of VASP simulation + timesteps required to generate ML-IPs that can reproduce the + structural properties. We also investigated the minimum atomic size of + the training set necessary to construct ML-IPs that accurately + replicate the structural properties of large Au nanoclusters, using + the LAMMPS-specific heat of the Au147 icosahedral as reference. Our + findings suggest that minor adjustments to a potential developed for + one system can render it suitable for other systems. These results + provide further insight into the development of accurate interatomic + potentials for modelling Au nanoparticles through machine learning + techniques.}, + PMCID = {PMC10303715}, + doi = {10.3390/nano13121832}, +} + + +@Article{Sun_PhysRevB_2023_v107_p224301, + author = {Huaijun Sun and Chao Zhang and Ling Tang and Renhai Wang and Weiyi Xia + and Cai-Zhuang Wang}, + title = {{Molecular dynamics simulation of Fe-Si alloys using a neural network + machine learning potential}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 107, + issue = 22, + pages = 224301, + doi = {10.1103/PhysRevB.107.224301}, +} + + +@Article{Qi_JMaterSci_2023_v58_p9515, + author = {Yongnian Qi and Xiaoguang Guo and Hao Wang and Shuohua Zhang and Ming + Li and Ping Zhou and Dongming Guo}, + title = {{Reversible densification and cooperative atomic movement induced + {\textquotedblleft}compaction{\textquotedblright} in vitreous silica: + a new sight from deep neural network interatomic potentials}}, + journal = {J Mater Sci}, + year = 2023, + volume = 58, + issue = 23, + pages = {9515--9532}, + doi = {10.1007/s10853-023-08599-w}, +} + + +@Article{Wang_GeochimicaetCosmochimicaActa_2023_v350_p57, + author = {Kai Wang and Xiancai Lu and Xiandong Liu and Kun Yin}, + title = {{Noble gas (He, Ne, and Ar) solubilities in high-pressure silicate + melts calculated based on deep-potential modeling}}, + journal = {Geochimica et Cosmochimica Acta}, + year = 2023, + volume = 350, + pages = {57--68}, + doi = {10.1016/j.gca.2023.03.032}, +} + + +@Article{Zhao_IEEETransCircuitsSystI_2023_v70_p2439, + author = {Zhuoying Zhao and Ziling Tan and Pinghui Mo and Xiaonan Wang and Dan + Zhao and Xin Zhang and Ming Tao and Jie Liu}, + title = {{A Heterogeneous Parallel Non-von Neumann Architecture System for + Accurate and Efficient Machine Learning Molecular Dynamics}}, + journal = {IEEE Trans. Circuits Syst. I}, + year = 2023, + volume = 70, + issue = 6, + pages = {2439--2449}, + doi = {10.1109/TCSI.2023.3255199}, +} + + +@Article{Xie_SolarEnergyMaterialsandSolarCells_2023_v254_p112275, + author = {Yun Xie and Min Bu and Guiming Zou and Ye Zhang and Guimin Lu}, + title = {{Molecular dynamics simulations of CaCl2{\textendash}NaCl molten salt + based on the machine learning potentials}}, + journal = {Solar Energy Materials and Solar Cells}, + year = 2023, + volume = 254, + pages = 112275, + doi = {10.1016/j.solmat.2023.112275}, +} + + +@Article{Yeo_AppliedSurfaceScience_2023_v621_p156893, + author = {Kangmo Yeo and Sukmin Jeong}, + title = {{Machine learning insight into h-BN growth on Pt(111) from atomic + states}}, + journal = {Applied Surface Science}, + year = 2023, + volume = 621, + pages = 156893, + doi = {10.1016/j.apsusc.2023.156893}, +} + + +@Article{Achar_ACSApplMaterInterfaces_2023_v15_p25873, + author = {Siddarth K Achar and Leonardo Bernasconi and Ruby I DeMaio and Katlyn + R Howard and J Karl Johnson}, + title = {{In Silico Demonstration of Fast Anhydrous Proton Conduction on + Graphanol}}, + journal = {ACS Appl. Mater. Interfaces}, + year = 2023, + volume = 15, + issue = 21, + pages = {25873--25883}, + annote = {Development of new materials capable of conducting protons in the + absence of water is crucial for improving the performance, reducing + the cost, and extending the operating conditions for proton exchange + membrane fuel cells. We present detailed atomistic simulations showing + that graphanol (hydroxylated graphane) will conduct protons + anhydrously with very low diffusion barriers. We developed a deep + learning potential (DP) for graphanol that has near-density functional + theory accuracy but requires a very small fraction of the + computational cost. We used our DP to calculate proton self-diffusion + coefficients as a function of temperature, to estimate the overall + barrier to proton diffusion, and to characterize the impact of thermal + fluctuations as a function of system size. We propose and test a + detailed mechanism for proton conduction on the surface of graphanol. + We show that protons can rapidly hop along Grotthuss chains containing + several hydroxyl groups aligned such that hydrogen bonds allow for + conduction of protons forward and backward along the chain without + hydroxyl group rotation. Long-range proton transport only takes place + as new Grotthuss chains are formed by rotation of one or more hydroxyl + groups in the chain. Thus, the overall diffusion barrier consists of a + convolution of the intrinsic proton hopping barrier and the intrinsic + hydroxyl rotation barrier. Our results provide a set of design rules + for developing new anhydrous proton conducting membranes with even + lower diffusion barriers.}, + PMCID = {PMC10236431}, + doi = {10.1021/acsami.3c04022}, +} + + +@Article{Lu_JChemPhys_2023_v158_pNone, + author = {Jiajun Lu and Jinkai Wang and Kaiwei Wan and Ying Chen and Hao Wang + and Xinghua Shi}, + title = {{An accurate interatomic potential for the TiAlNb ternary alloy + developed by deep neural network learning method}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 20, + annote = {The complex phase diagram and bonding nature of the TiAl system make + it difficult to accurately describe its various properties and phases + by traditional atomistic force fields. Here, we develop a machine + learning interatomic potential with a deep neural network method for + the TiAlNb ternary alloy based on a dataset built by first-principles + calculations. The training set includes bulk elementary metals and + intermetallic structures with slab and amorphous configurations. This + potential is validated by comparing bulk properties-including lattice + constant and elastic constants, surface energies, vacancy formation + energies, and stacking fault energies-with their respective density + functional theory values. Moreover, our potential could accurately + predict the average formation energy and stacking fault energy of + {\ensuremath{\gamma}}-TiAl doped with Nb. The tensile properties of + {\ensuremath{\gamma}}-TiAl are simulated by our potential and verified + by experiments. These results support the applicability of our + potential under more practical conditions.}, + doi = {10.1063/5.0147720}, +} + + +@Article{Li_JPhysChemC_2023_v127_p9750, + author = {Lesheng Li and Marcos F. {Calegari Andrade} and Roberto Car and + Annabella Selloni and Emily A. Carter}, + title = {{Characterizing Structure-Dependent TiS2/Water Interfaces + Using Deep-Neural-Network-Assisted Molecular Dynamics}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 20, + pages = {9750--9758}, + doi = {10.1021/acs.jpcc.2c08581}, +} + + +@Article{Mathur_JPhysChemB_2023_v127_p4562, + author = {Reha Mathur and Maria Carolina Muniz and Shuwen Yue and Roberto Car + and Athanassios Z Panagiotopoulos}, + title = {{First-Principles-Based Machine Learning Models for Phase Behavior and + Transport Properties of CO2}}, + journal = {J. Phys. Chem. B}, + year = 2023, + volume = 127, + issue = 20, + pages = {4562--4569}, + annote = {In this work, we construct distinct first-principles-based machine- + learning models of CO2, reproducing the potential energy surface of + the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density + functional theory. We employ the Deep Potential methodology to develop + the models and consequently achieve a significant computational + efficiency over ab initio molecular dynamics (AIMD) that allows for + larger system sizes and time scales to be explored. Although our + models are trained only with liquid-phase configurations, they are + able to simulate a stable interfacial system and predict vapor-liquid + equilibrium properties, in good agreement with results from the + literature. Because of the computational efficiency of the models, we + are also able to obtain transport properties, such as viscosity and + diffusion coefficients. We find that the SCAN-based model presents a + temperature shift in the position of the critical point, while the + SCAN-rvv10-based model shows improvement but still exhibits a + temperature shift that remains approximately constant for all + properties investigated in this work. We find that the BLYP-D3-based + model generally performs better for the liquid phase and vapor-liquid + equilibrium properties, but the PBE-D3-based model is better suited + for predicting transport properties.}, + doi = {10.1021/acs.jpcb.3c00610}, +} + + +@Article{Sanchez-Burgos_JChemPhys_2023_v158_pNone, + author = {Ignacio Sanchez-Burgos and Maria Carolina Muniz and Jorge R Espinosa + and Athanassios Z Panagiotopoulos}, + title = {{A Deep Potential model for liquid{\textendash}vapor equilibrium and + cavitation rates of water}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 18, + annote = {Computational studies of liquid water and its phase transition into + vapor have traditionally been performed using classical water models. + Here, we utilize the Deep Potential methodology-a machine learning + approach-to study this ubiquitous phase transition, starting from the + phase diagram in the liquid-vapor coexistence regime. The machine + learning model is trained on ab{~}initio energies and forces based on + the SCAN density functional, which has been previously shown to + reproduce solid phases and other properties of water. Here, we compute + the surface tension, saturation pressure, and enthalpy of vaporization + for a range of temperatures spanning from 300 to 600{~}K and evaluate + the Deep Potential model performance against experimental results and + the semiempirical TIP4P/2005 classical model. Moreover, by employing + the seeding technique, we evaluate the free energy barrier and + nucleation rate at negative pressures for the isotherm of 296.4{~}K. + We find that the nucleation rates obtained from the Deep Potential + model deviate from those computed for the TIP4P/2005 water model due + to an underestimation in the surface tension from the Deep Potential + model. From analysis of the seeding simulations, we also evaluate the + Tolman length for the Deep Potential water model, which is (0.091 + {\ensuremath{\pm}} 0.008) nm at 296.4{~}K. Finally, we identify that + water molecules display a preferential orientation in the liquid-vapor + interface, in which H atoms tend to point toward the vapor phase to + maximize the enthalpic gain of interfacial molecules. We find that + this behavior is more pronounced for planar interfaces than for the + curved interfaces in bubbles. This work represents the first + application of Deep Potential models to the study of liquid-vapor + coexistence and water cavitation.}, + doi = {10.1063/5.0144500}, +} + + +@Article{Giese_JChemPhys_2023_v158_pNone, + author = {Timothy J Giese and Darrin M York}, + title = {{Estimation of frequency factors for the calculation of kinetic isotope + effects from classical and path integral free energy simulations}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 17, + annote = {We use the modified Bigeleisen-Mayer equation to compute kinetic + isotope effect values for non-enzymatic phosphoryl transfer reactions + from classical and path integral molecular dynamics umbrella sampling. + The modified form of the Bigeleisen-Mayer equation consists of a ratio + of imaginary mode vibrational frequencies and a contribution arising + from the isotopic substitution's effect on the activation free energy, + which can be computed from path integral simulation. In the present + study, we describe a practical method for estimating the frequency + ratio correction directly from umbrella sampling in a manner that does + not require normal mode analysis of many geometry optimized + structures. Instead, the method relates the frequency ratio to the + change in the mass weighted coordinate representation of the minimum + free energy path at the transition state induced by isotopic + substitution. The method is applied to the calculation of 16/18O and + 32/34S primary kinetic isotope effect values for six non-enzymatic + phosphoryl transfer reactions. We demonstrate that the results are + consistent with the analysis of geometry optimized transition state + ensembles using the traditional Bigeleisen-Mayer equation. The method + thus presents a new practical tool to enable facile calculation of + kinetic isotope effect values for complex chemical reactions in the + condensed phase.}, + PMCID = {PMC10154067}, + doi = {10.1063/5.0147218}, +} + + +@Article{Chen_PhysRevMaterials_2023_v7_p053603, + author = {Tao Chen and Fengbo Yuan and Jianchuan Liu and Huayun Geng and Linfeng + Zhang and Han Wang and Mohan Chen}, + title = {{Modeling the high-pressure solid and liquid phases of tin from deep + potentials with ab initio accuracy}}, + journal = {Phys. Rev. Materials}, + year = 2023, + volume = 7, + issue = 5, + pages = 053603, + doi = {10.1103/PhysRevMaterials.7.053603}, +} + + +@Article{Han_Nanomaterials_2023_v13_p1576, + author = {Jinsen Han and Qiyu Zeng and Ke Chen and Xiaoxiang Yu and Jiayu Dai}, + title = {{Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine + Learning Potential}}, + journal = {Nanomaterials (Basel).}, + year = 2023, + volume = 13, + issue = 9, + pages = 1576, + annote = {The two-dimensional post-transition-metal chalcogenides, particularly + indium selenide (InSe), exhibit salient carrier transport properties + and evince extensive interest for broad applications. A comprehensive + understanding of thermal transport is indispensable for thermal + management. However, theoretical predictions on thermal transport in + the InSe system are found in disagreement with experimental + measurements. In this work, we utilize both the Green-Kubo approach + with deep potential (GK-DP), together with the phonon Boltzmann + transport equation with density functional theory (BTE-DFT) to + investigate the thermal conductivity ({\ensuremath{\kappa}}) of InSe + monolayer. The {\ensuremath{\kappa}} calculated by GK-DP is 9.52 W/mK + at 300 K, which is in good agreement with the experimental value, + while the {\ensuremath{\kappa}} predicted by BTE-DFT is 13.08 W/mK. + After analyzing the scattering phase space and cumulative + {\ensuremath{\kappa}} by mode-decomposed method, we found that, due to + the large energy gap between lower and upper optical branches, the + exclusion of four-phonon scattering in BTE-DFT underestimates the + scattering phase space of lower optical branches due to large group + velocities, and thus would overestimate their contribution to + {\ensuremath{\kappa}}. The temperature dependence of + {\ensuremath{\kappa}} calculated by GK-DP also demonstrates the effect + of higher-order phonon scattering, especially at high temperatures. + Our results emphasize the significant role of four-phonon scattering + in InSe monolayer, suggesting that combining molecular dynamics with + machine learning potential is an accurate and efficient approach to + predict thermal transport.}, + PMCID = {PMC10180940}, + doi = {10.3390/nano13091576}, +} + + +@Article{Yuan_EarthandPlanetaryScienceLetters_2023_v609_p118084, + author = {Liang Yuan and Gerd Steinle-Neumann}, + title = {{Hydrogen distribution between the Earth's inner and outer core}}, + journal = {Earth and Planetary Science Letters}, + year = 2023, + volume = 609, + pages = 118084, + doi = {10.1016/j.epsl.2023.118084}, +} + + +@Article{He_ComputationalMaterialsScience_2023_v223_p112111, + author = {Xi He and Jinde Liu and Chen Yang and Gang Jiang}, + title = {{Predicting thermodynamic stability of magnesium alloys in machine + learning}}, + journal = {Computational Materials Science}, + year = 2023, + volume = 223, + pages = 112111, + doi = {10.1016/j.commatsci.2023.112111}, +} + + +@Article{Hu_JPhysChemLett_2023_v14_p3677, + author = {Taiping Hu and Fu-Zhi Dai and Guobing Zhou and Xiaoxu Wang and + Shenzhen Xu}, + title = {{Unraveling the Dynamic Correlations between Transition Metal Migration + and the Oxygen Dimer Formation in the Highly Delithiated + LixCoO2 Cathode}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 15, + pages = {3677--3684}, + annote = {The voltage-window expansion can increase the practical capacity of + LixCoO2 cathodes, but it would lead to serious structural degradations + and oxygen release induced by transition metal (TM) migration. + Therefore, it is crucial to understand the dynamic correlations + between the TM migration and the oxygen dimer formation. Here, + machine-learning-potential-assisted molecular dynamics simulations + combined with enhanced sampling techniques are performed to resolve + the above question using a representative CoO2 model. Our results show + that the occurrence of the Co migration exhibits local + characteristics. The formation of the Co vacancy cluster is necessary + for the oxygen dimer generation. The introduction of the Ti dopant can + significantly increase the kinetic barrier of the Co ion migration and + thus effectively suppress the formation of the Co vacancy cluster. Our + work reveals atomic-scale dynamic correlations between the TM + migration and the oxygen sublattice's instability and provides + insights about the dopant's promotion of the structural stability.}, + doi = {10.1021/acs.jpclett.3c00506}, +} + + +@Article{Luo_JPhysChemC_2023_v127_p7071, + author = {Kun Luo and Yidi Shen and Jun Li and Qi An}, + title = {{Pressure-Induced Stability of Methane Hydrate from Machine Learning + Force Field Simulations}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 15, + pages = {7071--7077}, + doi = {10.1021/acs.jpcc.2c09121}, +} + + +@Article{Ghosh_JPhysCondensMatter_2023_v35_p154002, + author = {Maitrayee Ghosh and Shuai Zhang and Lianming Hu and S X Hu}, + title = {{Cooperative diffusion in body-centered cubic iron in Earth and super- + Earths{\textquoteright} inner core conditions}}, + journal = {J. Phys. Condens. Matter}, + year = 2023, + volume = 35, + issue = 15, + pages = 154002, + annote = {The physical chemistry of iron at the inner-core conditions is key to + understanding the evolution and habitability of Earth and super-Earth + planets. Based on full first-principles simulations, we report + cooperative diffusion along the longitudinally + fast{\ensuremath{\langle}}111{\ensuremath{\rangle}}directions of body- + centered cubic (bcc) iron in temperature ranges of up to 2000-4000 K + below melting and pressures of {\ensuremath{\sim}}300-4000{\,}GPa. The + diffusion is due to the low energy barrier in the corresponding + direction and is accompanied by mechanical and dynamical stability, as + well as strong elastic anisotropy of bcc iron. These findings provide + a possible explanation for seismological signatures of the Earth's + inner core, particularly the positive correlation between P wave + velocity and attenuation. The diffusion can also change the detailed + mechanism of core convection by increasing the diffusivity and + electrical conductivity and lowering the viscosity. The results need + to be considered in future geophysical and planetary models and should + motivate future studies of materials under extreme conditions.}, + doi = {10.1088/1361-648X/acba71}, +} + + +@Article{Zhao_JPhysChemC_2023_v127_p6852, + author = {C. Y. Zhao and Y. B. Tao and Y. He}, + title = {{Microstructure and Thermophysical Property Prediction for Chloride + Composite Phase Change Materials: A Deep Potential Molecular Dynamics + Study}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 14, + pages = {6852--6860}, + doi = {10.1021/acs.jpcc.2c08589}, +} + + +@Article{Chang_PhysChemChemPhys_2023_v25_p12841, + author = {Xiaoya Chang and Qingzhao Chu and Dongping Chen}, + title = {{Monitoring the melting behavior of boron nanoparticles using a neural + network potential}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2023, + volume = 25, + issue = 18, + pages = {12841--12853}, + annote = {The melting behavior of metal additives is fundamental for various + propulsion and energy-conversion applications. A neural network + potential (NNP) is proposed to examine the size-dependent melting + behaviors of boron nanoparticles. Our NNP model is proven to possess a + desirable computational efficiency and retain ab initio accuracy, + allowing investigation of the physicochemical properties of bulk boron + crystals from an atomic perspective. In this work, a series of NNP- + based molecular dynamics simulations were conducted and numerical + evidence of the size-dependent melting behavior of boron nanoparticles + with diameters from 3 to 6 nm was reported for the first time. + Evolution of the intermolecular energy and the Lindemann index are + used to monitor the melting process. A liquid layer forms on the + particle surface and further expands with increased temperature. Once + the liquid layer reaches the core region, the particle is completely + molten. The reduced melting temperature of the boron nanoparticle + decreases with its particle size following a linear relationship with + reciprocal size, similar to other commonly used metals (Al and Mg). + Additionally, boron nanoparticles are more sensitive to particle size + than Al particles and less sensitive than Mg particles. These findings + provide an atomistic perspective for developing manufacturing + techniques and tailoring combustion performance in practical + applications.}, + doi = {10.1039/d3cp00571b}, +} + + +@Article{Wu_JPhysChemC_2023_v127_p6262, + author = {Jiawei Wu and Dingming Chen and Jianfu Chen and Haifeng Wang}, + title = {{Structural and Composition Evolution of Palladium Catalyst for CO + Oxidation under Steady-State Reaction Conditions}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + issue = 13, + pages = {6262--6270}, + doi = {10.1021/acs.jpcc.2c07877}, +} + + +@Article{JaffrelotInizan_ChemSci_2023_v14_p5438, + author = {Th{\'e}o {Jaffrelot Inizan} and Thomas Pl{\'e} and Olivier Adjoua and + Pengyu Ren and Hatice G{\"o}kcan and Olexandr Isayev and Louis + Lagard{\`e}re and Jean-Philip Piquemal}, + title = {{Scalable hybrid deep neural networks/polarizable potentials + biomolecular simulations including long-range effects}}, + journal = {Chem. Sci.}, + year = 2023, + volume = 14, + issue = 20, + pages = {5438--5452}, + annote = {Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular + dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep + Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities + by orders of magnitude offering access to ns simulations for 100k-atom + biosystems while offering the possibility of coupling DNNs to any + classical (FFs) and many-body polarizable (PFFs) force fields. It + allows therefore the introduction of the ANI-2X/AMOEBA hybrid + polarizable potential designed for ligand binding studies where + solvent-solvent and solvent-solute interactions are computed with the + AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN. + ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range + interactions via an efficient Particle Mesh Ewald implementation while + preserving ANI-2X's solute short-range quantum mechanical accuracy. + The DNN/PFF partition can be user-defined allowing for hybrid + simulations to include key ingredients of biosimulation such as + polarizable solvents, polarizable counter ions, etc.{\textellipsis} + ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy + focusing on the model's contributions to low-frequency modes of + nuclear forces. It primarily evaluates AMOEBA forces while including + ANI-2X ones only via correction-steps resulting in an order of + magnitude acceleration over standard Velocity Verlet integration. + Simulating more than 10 {\ensuremath{\mu}}s, we compute + charged/uncharged ligand solvation free energies in 4 solvents, and + absolute binding free energies of host-guest complexes from SAMPL + challenges. ANI-2X/AMOEBA average errors are discussed in terms of + statistical uncertainty and appear in the range of chemical accuracy + compared to experiment. The availability of the Deep-HP computational + platform opens the path towards large-scale hybrid DNN simulations, at + force-field cost, in biophysics and drug discovery.}, + PMCID = {PMC10208042}, + doi = {10.1039/d2sc04815a}, +} + + +@Article{Liu_ACSMaterialsLett_2023_v5_p1009, + author = {Jiahui Liu and Shuo Wang and Yoshiyuki Kawazoe and Qiang Sun}, + title = {{A New Spinel Chloride Solid Electrolyte with High Ionic Conductivity + and Stability for Na-Ion Batteries}}, + journal = {ACS Materials Lett.}, + year = 2023, + volume = 5, + issue = 4, + pages = {1009--1017}, + doi = {10.1021/acsmaterialslett.3c00119}, +} + + +@Article{Xu_Nanomaterials_2023_v13_p1352, + author = {Hui Xu and Zeyuan Li and Zhaofu Zhang and Sheng Liu and Shengnan Shen + and Yuzheng Guo}, + title = {{High-Accuracy Neural Network Interatomic Potential for Silicon Nitride}}, + journal = {Nanomaterials (Basel).}, + year = 2023, + volume = 13, + issue = 8, + pages = 1352, + annote = {In the field of machine learning (ML) and data science, it is + meaningful to use the advantages of ML to create reliable interatomic + potentials. Deep potential molecular dynamics (DEEPMD) are one of the + most useful methods to create interatomic potentials. Among ceramic + materials, amorphous silicon nitride (SiNx) features good electrical + insulation, abrasion resistance, and mechanical strength, which is + widely applied in industries. In our work, a neural network potential + (NNP) for SiNx was created based on DEEPMD, and the NNP is confirmed + to be applicable to the SiNx model. The tensile tests were simulated + to compare the mechanical properties of SiNx with different + compositions based on the molecular dynamic method coupled with NNP. + Among these SiNx, Si3N4 has the largest elastic modulus (E) and yield + stress ({\ensuremath{\sigma}}s), showing the desired mechanical + strength owing to the largest coordination numbers (CN) and radial + distribution function (RDF). The RDFs and CNs decrease with the + increase of x; meanwhile, E and {\ensuremath{\sigma}}s of SiNx + decrease when the proportion of Si increases. It can be concluded that + the ratio of nitrogen to silicon can reflect the RDFs and CNs in micro + level and macro mechanical properties of SiNx to a large extent.}, + PMCID = {PMC10145480}, + doi = {10.3390/nano13081352}, +} + + +@Article{Wang_Unknown_2023_v12_p803, + author = {Yinan Wang and Bo Wen and Xingjian Jiao and Ya Li and Lei Chen and + Yujin Wang and Fu-Zhi Dai}, + title = {{The highest melting point material: Searched by Bayesian global + optimization with deep potential molecular dynamics}}, + year = 2023, + volume = 12, + issue = 4, + pages = {803--814}, + doi = {10.26599/JAC.2023.9220721}, +} + + +@Article{Li_InternationalJournalofPlasticity_2023_v163_p103552, + author = {Jun Li and Kun Luo and Qi An}, + title = {{Atomic structure, stability, and dissociation of dislocations in + cadmium telluride}}, + journal = {International Journal of Plasticity}, + year = 2023, + volume = 163, + pages = 103552, + doi = {10.1016/j.ijplas.2023.103552}, +} + + +@Article{Bu_JournalofMolecularLiquids_2023_v375_p120689, + author = {Min Bu and Taixi Feng and Guimin Lu}, + title = {{Prediction on local structure and properties of LiCl-KCl-AlCl3 ternary + molten salt with deep learning potential}}, + journal = {Journal of Molecular Liquids}, + year = 2023, + volume = 375, + pages = 120689, + doi = {10.1016/j.molliq.2022.120689}, +} + + +@Article{Cioni_JChemPhys_2023_v158_p124701, + author = {Matteo Cioni and Daniela Polino and Daniele Rapetti and Luca Pesce and + Massimo {Delle Piane} and Giovanni M Pavan}, + title = {{Innate dynamics and identity crisis of a metal surface unveiled by + machine learning of atomic environments}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 12, + pages = 124701, + annote = {Metals are traditionally considered hard matter. However, it is well + known that their atomic lattices may become dynamic and undergo + reconfigurations even well below the melting temperature. The innate + atomic dynamics of metals is directly related to their bulk and + surface properties. Understanding their complex structural dynamics + is, thus, important for many applications but is not easy. Here, we + report deep-potential molecular dynamics simulations allowing to + resolve at an atomic resolution the complex dynamics of various types + of copper (Cu) surfaces, used as an example, near the H{\"u}ttig + ({\ensuremath{\sim}}1/3 of melting) temperature. The development of + deep neural network potential trained on density functional theory + calculations provides a dynamically accurate force field that we use + to simulate large atomistic models of different Cu surface types. A + combination of high-dimensional structural descriptors and + unsupervized machine learning allows identifying and tracking all the + atomic environments (AEs) emerging in the surfaces at finite + temperatures. We can directly observe how AEs that are non-native in a + specific (ideal) surface, but that are, instead, typical of other + surface types, continuously emerge/disappear in that surface in + relevant regimes in dynamic equilibrium with the native ones. Our + analyses allow estimating the lifetime of all the AEs populating these + Cu surfaces and to reconstruct their dynamic interconversions + networks. This reveals the elusive identity of these metal surfaces, + which preserve their identity only in part and in part transform into + something else under relevant conditions. This also proposes a concept + of "statistical identity" for metal surfaces, which is key to + understanding their behaviors and properties.}, + doi = {10.1063/5.0139010}, +} + + +@Article{Zeng_JChemPhys_2023_v158_p124110, + author = {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York}, + title = {{Modern semiempirical electronic structure methods and machine learning + potentials for drug discovery: Conformers, tautomers, and protonation + states}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 12, + pages = 124110, + annote = {Modern semiempirical electronic structure methods have considerable + promise in drug discovery as universal "force fields" that can + reliably model biological and drug-like molecules, including + alternative tautomers and protonation states. Herein, we compare the + performance of several neglect of diatomic differential overlap-based + semiempirical (MNDO/d, AM1, PM6, PM6-D3H4X, PM7, and ODM2), density- + functional tight-binding based (DFTB3, DFTB/ChIMES, GFN1-xTB, and + GFN2-xTB) models with pure machine learning potentials (ANI-1x and + ANI-2x) and hybrid quantum mechanical/machine learning potentials + (AIQM1 and QD{\ensuremath{\pi}}) for a wide range of data computed at + a consistent {\ensuremath{\omega}}B97X/6-31G* level of theory (as in + the ANI-1x database). This data includes conformational energies, + intermolecular interactions, tautomers, and protonation states. + Additional comparisons are made to a set of natural and synthetic + nucleic acids from the artificially expanded genetic information + system that has important implications for the design of new + biotechnology and therapeutics. Finally, we examine the acid/base + chemistry relevant for RNA cleavage reactions catalyzed by small + nucleolytic ribozymes, DNAzymes, and ribonucleases. Overall, the + hybrid quantum mechanical/machine learning potentials appear to be the + most robust for these datasets, and the recently developed + QD{\ensuremath{\pi}} model performs exceptionally well, having + especially high accuracy for tautomers and protonation states relevant + to drug discovery.}, + PMCID = {PMC10052497}, + doi = {10.1063/5.0139281}, +} + + +@Article{Zheng_ACSNano_2023_v17_p5579, + author = {Bowen Zheng and Felipe Lopes Oliveira and Rodrigo {Neumann Barros + Ferreira} and Mathias Steiner and Hendrik Hamann and Grace X Gu and + Binquan Luan}, + title = {{Quantum Informed Machine-Learning Potentials for Molecular Dynamics + Simulations of CO2{\textquoteright}s Chemisorption and + Diffusion in Mg-MOF-74}}, + journal = {ACS Nano}, + year = 2023, + volume = 17, + issue = 6, + pages = {5579--5587}, + annote = {Among various porous solids for gas separation and purification, + metal-organic frameworks (MOFs) are promising materials that + potentially combine high CO2 uptake and CO2/N2 selectivity. So far, + within the hundreds of thousands of MOF structures known today, it + remains a challenge to computationally identify the best suited + species. First principle-based simulations of CO2 adsorption in MOFs + would provide the necessary accuracy; however, they are impractical + due to the high computational cost. Classical force field-based + simulations would be computationally feasible; however, they do not + provide sufficient accuracy. Thus, the entropy contribution that + requires both accurate force fields and sufficiently long computing + time for sampling is difficult to obtain in simulations. Here, we + report quantum-informed machine-learning force fields (QMLFFs) for + atomistic simulations of CO2 in MOFs. We demonstrate that the method + has a much higher computational efficiency + ({\ensuremath{\sim}}1000{\texttimes}) than the first-principle one + while maintaining the quantum-level accuracy. As a proof of concept, + we show that the QMLFF-based molecular dynamics simulations of CO2 in + Mg-MOF-74 can predict the binding free energy landscape and the + diffusion coefficient close to experimental values. The combination of + machine learning and atomistic simulation helps achieve more accurate + and efficient in silico evaluations of the chemisorption and diffusion + of gas molecules in MOFs.}, + doi = {10.1021/acsnano.2c11102}, +} + + +@Article{Li_InternationalJournalofMechanicalSciences_2023_v242_p107998, + author = {Jun Li and Qi An}, + title = {{Nanotwinning-induced pseudoplastic deformation in boron carbide under + low temperature}}, + journal = {International Journal of Mechanical Sciences}, + year = 2023, + volume = 242, + pages = 107998, + doi = {10.1016/j.ijmecsci.2022.107998}, +} + + +@Article{Wu_JPhysChemLett_2023_v14_p2208, + author = {Zhihong Wu and Wen-Jin Yin and Bo Wen and Dongwei Ma and Li-Min Liu}, + title = {{Oxygen Vacancy Diffusion in Rutile TiO2: Insight from Deep + Neural Network Potential Simulations}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 8, + pages = {2208--2214}, + annote = {Defects play a crucial role in the surface reactivity and electronic + engineering of titanium dioxide (TiO2). In this work, we have used an + active learning method to train deep neural network potentials from + the ab initio data of a defective TiO2 surface. Validations show a + good consistency between the deep potentials (DPs) and density + functional theory (DFT) results. Therefore, the DPs were further + applied on the extended surface and executed for nanoseconds. The + results show that the oxygen vacancy at various sites are very stable + under 330 K. However, some unstable defect sites will convert to the + most favorable ones after tens or hundreds of picoseconds, while the + temperature was elevated to 500 K. The DP predicated barriers of + oxygen vacancy diffusion were similar to those of DFT. These results + show that machine-learning trained DPs could accelerate the molecular + dynamics with a DFT-level accuracy and promote people's understanding + of the microscopic mechanism of fundamental reactions.}, + doi = {10.1021/acs.jpclett.2c03827}, +} + + +@Article{Balyakin_JetpLett_2023_v117_p370, + author = {I. A. Balyakin and R. E. Ryltsev and N. M. Chtchelkatchev}, + title = {{Liquid{\textendash}Crystal Structure Inheritance in Machine Learning + Potentials for Network-Forming Systems}}, + journal = {Jetp Lett.}, + year = 2023, + volume = 117, + issue = 5, + pages = {370--376}, + annote = {It has been studied whether machine learning interatomic + potentials parameterized with only disordered configurations + corresponding to liquid can describe the properties of crystalline + phases and predict their structure. The study has been performed for a + network-forming system SiO2, which has numerous + polymorphic phases significantly different in structure and density. + Using only high-temperature disordered configurations, a machine + learning interatomic potential based on artificial neural networks + (DeePMD model) has been parameterized. The potential reproduces well + ab initio dependences of the energy on the volume and the vibrational + density of states for all considered tetra- and octahedral crystalline + phases of SiO2. Furthermore, the combination of + the evolutionary algorithm and the developed DeePMD potential has made + it possible to reproduce the really observed crystalline structures of + SiO2. Such a good liquid{\textendash}crystal + portability of the machine learning interatomic potential opens + prospects for the simulation of the structure and properties of new + systems for which experimental information on crystalline phases is + absent.}, + doi = {10.1134/S0021364023600234}, +} + + +@Article{Wang_PhysRevMaterials_2023_v7_p034601, + author = {Zhi-Hao Wang and Xuan-Yan Chen and Zhen Zhang and Xie Zhang and Su- + Huai Wei}, + title = {{Profiling the off-center atomic displacements in CuCl at finite + temperatures with a deep-learning potential}}, + journal = {Phys. Rev. Materials}, + year = 2023, + volume = 7, + issue = 3, + pages = 034601, + doi = {10.1103/PhysRevMaterials.7.034601}, +} + + +@Article{Li_CementandConcreteResearch_2023_v165_p107092, + author = {Yunjian Li and Hui Pan and Zongjin Li}, + title = {{Unravelling the dissolution dynamics of silicate minerals by deep + learning molecular dynamics simulation: A case of dicalcium silicate}}, + journal = {Cement and Concrete Research}, + year = 2023, + volume = 165, + pages = 107092, + doi = {10.1016/j.cemconres.2023.107092}, +} + + +@Article{Sterkhova_JournalofPhysicsandChemistryofSolids_2023_v174_p111143, + author = {I.V. Sterkhova and L.V. Kamaeva and V.I. Lad'yanov and N.M. + Chtchelkatchev}, + title = {{Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax + (x=0{\textendash}2) melts: Experiment and machine learning}}, + journal = {Journal of Physics and Chemistry of Solids}, + year = 2023, + volume = 174, + pages = 111143, + doi = {10.1016/j.jpcs.2022.111143}, +} + + +@Article{Zhai_JChemPhys_2023_v158_p084111, + author = {Yaoguang Zhai and Alessandro Caruso and Sigbj{\o}rn L{\o}land Bore and + Zhishang Luo and Francesco Paesani}, + title = {{A {\textquotedblleft}short blanket{\textquotedblright} dilemma for a + state-of-the-art neural network potential for water: Reproducing + experimental properties or the physics of the underlying many-body + interactions?}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 8, + pages = 084111, + annote = {Deep neural network (DNN) potentials have recently gained popularity + in computer simulations of a wide range of molecular systems, from + liquids to materials. In this study, we explore the possibility of + combining the computational efficiency of the DeePMD framework and the + demonstrated accuracy of the MB-pol data-driven, many-body potential + to train a DNN potential for large-scale simulations of water across + its phase diagram. We find that the DNN potential is able to reliably + reproduce the MB-pol results for liquid water, but provides a less + accurate description of the vapor-liquid equilibrium properties. This + shortcoming is traced back to the inability of the DNN potential to + correctly represent many-body interactions. An attempt to explicitly + include information about many-body effects results in a new DNN + potential that exhibits the opposite performance, being able to + correctly reproduce the MB-pol vapor-liquid equilibrium properties, + but losing accuracy in the description of the liquid properties. These + results suggest that DeePMD-based DNN potentials are not able to + correctly "learn" and, consequently, represent many-body interactions, + which implies that DNN potentials may have limited ability to predict + the properties for state points that are not explicitly included in + the training process. The computational efficiency of the DeePMD + framework can still be exploited to train DNN potentials on data- + driven many-body potentials, which can thus enable large-scale, + "chemically accurate" simulations of various molecular systems, with + the caveat that the target state points must have been adequately + sampled by the reference data-driven many-body potential in order to + guarantee a faithful representation of the associated properties.}, + doi = {10.1063/5.0142843}, +} + + +@Article{Xiao_Unknown_2023_v133_pNone, + author = {R. L. Xiao and Q. Wang and J. Y. Qin and J. F. Zhao and Y. Ruan and H. + P. Wang and H. Li and B. Wei}, + title = {{A deep learning approach to predict thermophysical properties of + metastable liquid Ti-Ni-Cr-Al alloy}}, + year = 2023, + volume = 133, + issue = 8, + annote = {The physical properties of liquid alloy are crucial for many + science fields. However, acquiring these properties remains + challenging. By means of the deep neural network (DNN), here we + presented a deep learning interatomic potential for the + Ti{\textendash}Ni{\textendash}Cr{\textendash}Al liquid system. + Meanwhile, the thermophysical properties of the + Ti{\textendash}Ni{\textendash}Cr{\textendash}Al liquid alloy were + experimentally measured by electrostatic levitation and + electromagnetic levitation technologies. The DNN potential predicted + this liquid system accurately in terms of both atomic structures and + thermophysical properties, and the results were in agreement with the + ab initio molecular dynamics calculation and the experimental values. + A further study on local structure carried out by Voronoi polyhedron + analysis showed that the cluster exhibited a tendency to transform + into high-coordinated cluster with a decrease in the temperature, + indicating the enhancement of local structure stability. This + eventually contributed to the linear increase in the density and + surface tension, and the exponential variation in the viscosity and + the diffusion coefficient with the rise of undercooling.}, + doi = {10.1063/5.0138001}, +} + + +@Article{Zeng_JChemTheoryComput_2023_v19_p1261, + author = {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York}, + title = {{QD{\ensuremath{\pi}}: A Quantum Deep Potential Interaction Model for + Drug Discovery}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + issue = 4, + pages = {1261--1275}, + annote = {We report QD{\ensuremath{\pi}}-v1.0 for modeling the internal energy + of drug molecules containing H, C, N, and O atoms. The + QD{\ensuremath{\pi}} model is in the form of a quantum + mechanical/machine learning potential correction + (QM/{\ensuremath{\Delta}}-MLP) that uses a fast third-order self- + consistent density-functional tight-binding (DFTB3/3OB) model that is + corrected to a quantitatively high-level of accuracy through a deep- + learning potential (DeepPot-SE). The model has the advantage that it + is able to properly treat electrostatic interactions and handle + changes in charge/protonation states. The model is trained against + reference data computed at the {\ensuremath{\omega}}B97X/6-31G* level + (as in the ANI-1x data set) and compared to several other approximate + semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, + MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QD{\ensuremath{\pi}} + model is demonstrated to be accurate for a wide range of intra- and + intermolecular interactions (despite its intended use as an internal + energy model) and has shown to perform exceptionally well for relative + protonation/deprotonation energies and tautomers. An example + application to model reactions involved in RNA strand cleavage + catalyzed by protein and nucleic acid enzymes illustrates + QD{\ensuremath{\pi}} has average errors less than 0.5 kcal/mol, + whereas the other models compared have errors over an order of + magnitude greater. Taken together, this makes QD{\ensuremath{\pi}} + highly attractive as a potential force field model for drug discovery.}, + PMCID = {PMC9992268}, + doi = {10.1021/acs.jctc.2c01172}, +} + + +@Article{Zhang_JChemInfModel_2023_v63_p1133, + author = {Jintu Zhang and Haotian Zhang and Zhixin Qin and Yu Kang and Xin Hong + and Tingjun Hou}, + title = {{Quasiclassical Trajectory Simulation as a Protocol to Build Locally + Accurate Machine Learning Potentials}}, + journal = {J. Chem. Inf. Model.}, + year = 2023, + volume = 63, + issue = 4, + pages = {1133--1142}, + annote = {Direct trajectory calculations have become increasingly popular in + recent computational chemistry investigations. However, the exorbitant + computational cost of ab initio trajectory calculations usually limits + its application in mechanistic explorations. Recently, machine + learning-based potential energy surface (ML-PES) provides a powerful + strategy to circumvent the heavy computational cost and meanwhile + maintain the required accuracy. Despite the appealing potential, + constructing a robust ML-PES is still challenging since the training + set of the PES should cover a broad enough configuration space. In + this work, we demonstrate that when the concerned properties could be + collected by the localized sampling of the configuration space, + quasiclassical trajectory (QCT) calculations can be invoked to + efficiently obtain locally accurate ML-PESs. We prove our concept with + two model reactions: methyl migration of i-pentane cation and + dimerization of cyclopentadiene. We found that the locally accurate + ML-PESs are sufficiently robust for reproducing the static and dynamic + features of the reactions, including the time-resolved free energy and + entropy changes, and time gaps.}, + doi = {10.1021/acs.jcim.2c01497}, +} + + +@Article{Li_PhysChemChemPhys_2023_v25_p6746, + author = {Zhiqiang Li and Xiaoyu Tan and Zhiwei Fu and Linhua Liu and Jia-Yue + Yang}, + title = {{Thermal transport across copper{\textendash}water interfaces according + to deep potential molecular dynamics}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2023, + volume = 25, + issue = 9, + pages = {6746--6756}, + annote = {Nanoscale thermal transport at solid-liquid interfaces plays an + essential role in many engineering fields. This work performs deep + potential molecular dynamics (DPMD) simulations to investigate thermal + transport across copper-water interfaces. Unlike traditional classical + molecular dynamics (CMD) simulations, we independently train a deep + learning potential (DLP) based on density functional theory (DFT) + calculations and demonstrated its high computational efficiency and + accuracy. The trained DLP predicts radial distribution functions + (RDFs), vibrational densities of states (VDOS), density curves, and + thermal conductivity of water confined in the nanochannel at a DFT + accuracy. The thermal conductivity decreases slightly with an increase + in the channel height, while the influence of the cross-sectional area + is negligible. Moreover, the predicted interfacial thermal conductance + (ITC) across the copper-water interface by DPMD is 2.505 {\texttimes} + 108 W m-2 K-1, the same order of magnitude as the CMD and experimental + results but with a high computational accuracy. This work seeks to + simulate the thermal transport properties of solid-liquid interfaces + with DFT accuracy at large-system and long-time scales.}, + doi = {10.1039/d2cp05530a}, +} + + +@Article{Gomes-Filho_JPhysChemB_2023_v127_p1422, + author = {M{\'a}rcio S Gomes-Filho and Alberto Torres and Alexandre {Reily + Rocha} and Luana S Pedroza}, + title = {{Size and Quality of Quantum Mechanical Data Set for Training Neural + Network Force Fields for Liquid Water}}, + journal = {J. Phys. Chem. B}, + year = 2023, + volume = 127, + issue = 6, + pages = {1422--1428}, + annote = {Molecular dynamics simulations have been used in different scientific + fields to investigate a broad range of physical systems. However, the + accuracy of calculation is based on the model considered to describe + the atomic interactions. In particular, ab initio molecular dynamics + (AIMD) has the accuracy of density functional theory (DFT) and thus is + limited to small systems and a relatively short simulation time. In + this scenario, Neural Network Force Fields (NNFFs) have an important + role, since they provide a way to circumvent these caveats. In this + work, we investigate NNFFs designed at the level of DFT to describe + liquid water, focusing on the size and quality of the training data + set considered. We show that structural properties are less dependent + on the size of the training data set compared to dynamical ones (such + as the diffusion coefficient), and a good sampling (selecting data + reference for the training process) can lead to a small sample with + good precision.}, + doi = {10.1021/acs.jpcb.2c09059}, +} + + +@Article{FidalgoCandido_JChemPhys_2023_v158_p064502, + author = {Vitor {Fidalgo C{\^a}ndido} and Filipe Matusalem and Maurice {de + Koning}}, + title = {{Melting conditions and entropies of superionic water ice: Free-energy + calculations based on hybrid solid/liquid reference systems}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 158, + issue = 6, + pages = 064502, + annote = {Superionic (SI) water ices-high-temperature, high-pressure phases of + water in which oxygen ions occupy a regular crystal lattice whereas + the protons flow in a liquid-like manner-have attracted a growing + amount of attention over the past few years, in particular due to + their possible role in the magnetic anomalies of the ice giants + Neptune and Uranus. In this paper, we consider the calculation of the + free energies of such phases, exploring hybrid reference systems + consisting of a combination of an Einstein solid for the oxygen ions + occupying a crystal lattice and a Uhlenbeck-Ford potential for the + protonic fluid that avoids irregularities associated with possible + particle overlaps. Applying this approach to a recent neural-network + potential-energy landscape for SI water ice, we compute Gibbs free + energies as a function of temperature for the SI fcc and liquid phases + to determine the melting temperature Tm at 340{~}GPa. The results are + consistent with previous estimates and indicate that the entropy + difference between both phases is comparatively small, in particular + due to the large amplitude of vibration of the oxygen ions in the fcc + phase at the melting temperature.}, + doi = {10.1063/5.0138987}, +} + + +@Article{Deng_ComputationalMaterialsScience_2023_v218_p111941, + author = {Fenglin Deng and Hongyu Wu and Ri He and Peijun Yang and Zhicheng + Zhong}, + title = {{Large-scale atomistic simulation of dislocation core structure in + face-centered cubic metal with Deep Potential method}}, + journal = {Computational Materials Science}, + year = 2023, + volume = 218, + pages = 111941, + doi = {10.1016/j.commatsci.2022.111941}, +} + + +@Article{Yao_RSCAdv_2023_v13_p4565, + author = {Songyuan Yao and Richard Van and Xiaoliang Pan and Ji Hwan Park and + Yuezhi Mao and Jingzhi Pu and Ye Mei and Yihan Shao}, + title = {{Machine learning based implicit solvent model for aqueous-solution + alanine dipeptide molecular dynamics simulations}}, + journal = {RSC Adv.}, + year = 2023, + volume = 13, + issue = 7, + pages = {4565--4577}, + annote = {Inspired by the recent work from No{\'e} and coworkers on the + development of machine learning based implicit solvent model for the + simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, + 155, 084101], here we report another investigation of the possibility + of using machine learning (ML) techniques to "derive" an implicit + solvent model directly from explicit solvent molecular dynamics (MD) + simulations. For alanine dipeptide, a machine learning potential (MLP) + based on the DeepPot-SE representation of the molecule was trained to + capture its interactions with its average solvent environment + configuration (ASEC). The predicted forces on the solute deviated only + by an RMSD of 0.4 kcal mol-1 {\r{A}}-1 from the reference values, and + the MLP-based free energy surface differed from that obtained from + explicit solvent MD simulations by an RMSD of less than 0.9 kcal + mol-1. Our MLP training protocol could also accurately reproduce + combined quantum mechanical molecular mechanical (QM/MM) forces on the + quantum mechanical (QM) solute in ASEC environment, thus enabling the + development of accurate ML-based implicit solvent models for ab + initio-QM MD simulations. Such ML-based implicit solvent models for QM + calculations are cost-effective in both the training stage, where the + use of ASEC reduces the number of data points to be labelled, and the + inference stage, where the MLP can be evaluated at a relatively small + additional cost on top of the QM calculation of the solute.}, + PMCID = {PMC9900604}, + doi = {10.1039/d2ra08180f}, +} + + +@Article{Deng_PhysRevB_2023_v107_p064103, + author = {Jie Deng and Haiyang Niu and Junwei Hu and Mingyi Chen and Lars + Stixrude}, + title = {{Melting of < + mml:mrow>MgSiO + 3 + determined by machine learning potentials}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 107, + issue = 6, + pages = 064103, + doi = {10.1103/PhysRevB.107.064103}, +} + + +@Article{Sours_JPhysChemCNanomaterInterfaces_2023_v127_p1455, + author = {Tyler G Sours and Ambarish R Kulkarni}, + title = {{Predicting Structural Properties of Pure Silica Zeolites Using Deep + Neural Network Potentials}}, + journal = {J. Phys. Chem. C. Nanomater. Interfaces}, + year = 2023, + volume = 127, + issue = 3, + pages = {1455--1463}, + annote = {Machine learning potentials (MLPs) capable of accurately describing + complex ab initio potential energy surfaces (PESs) have revolutionized + the field of multiscale atomistic modeling. In this work, using an + extensive density functional theory (DFT) data set (denoted as Si- + ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT + calculations) found in the International Zeolite Association (IZA) + database, we have trained a DeePMD-kit MLP to model the dynamics of + silica frameworks. The performance of our model is evaluated by + calculating various properties that probe the accuracy of the energy + and force predictions. This MLP demonstrates impressive agreement with + DFT for predicting zeolite structural properties, energy-volume + trends, and phonon density of states. Furthermore, our model achieves + reasonable predictions for stress-strain relationships without + including DFT stress data during training. These results highlight the + ability of MLPs to capture the flexibility of zeolite frameworks and + motivate further MLP development for nanoporous materials with near-ab + initio accuracy.}, + PMCID = {PMC9885523}, + doi = {10.1021/acs.jpcc.2c08429}, +} + + +@Article{Zhang_PhysChemChemPhys_2023_v25_p6164, + author = {Pan Zhang and Mi Qin and Zhenhua Zhang and Dan Jin and Yong Liu and + Ziyu Wang and Zhihong Lu and Jing Shi and Rui Xiong}, + title = {{Accessing the thermal conductivities of Sb2Te3 + and Bi2Te3/Sb2Te3 + superlattices by molecular dynamics simulations with a deep neural + network potential}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2023, + volume = 25, + issue = 8, + pages = {6164--6174}, + annote = {Phonon thermal transport is a key feature for the operation of + thermoelectric materials, but it is challenging to accurately + calculate the thermal conductivity of materials with strong + anharmonicity or large cells. In this work, a deep neural network + potential (NNP) is developed using a dataset based on density + functional theory (DFT) and applied to describe the lattice dynamics + of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices. The lattice thermal + conductivities of Sb2Te3 are first predicted using equilibrium + molecular dynamics (EMD) simulations combined with an NNP and the + results match well with experimental values. Then, through further + exploration of weighted phase spaces and the Gr{\"u}neisen parameter, + we find that there is a stronger anharmonicity in the out-of-plane + direction in Sb2Te3, which is the reason why the thermal + conductivities are overestimated more in the out-of-plane direction + than in the in-plane direction by solving the phonon Boltzmann + transport equation (BTE) with only three-phonon scattering processes + being considered. More importantly, the lattice thermal conductivities + of Bi2Te3/Sb2Te3 superlattices with different periods are accurately + predicted using non-equilibrium molecular dynamics (NEMD) simulations + together with an NNP, which serves as a good example to explore the + thermal transport physics of superlattices using a deep neural network + potential.}, + doi = {10.1039/d2cp05590b}, +} + + +@Article{Xu_Unknown_2023_v122_pNone, + author = {Xiong Xu and Fangbiao Li and Chang Niu and Min Li and Hui Wang}, + title = {{Machine learning assisted investigation of the barocaloric performance + in ammonium iodide}}, + year = 2023, + volume = 122, + issue = 4, + annote = {Using the ab initio-based training database, we trained the + potential function for ammonium iodide (NH4I) based on a deep neural + network-based model. On the basis of this potential function, we + simulated the temperature-driven {\ensuremath{\beta}}{\,}{\ensuremath{ + \Rightarrow}}{\,}{\ensuremath{\alpha}}-phase transition of NH4I with + isobaric isothermal ensemble via molecular dynamics simulations, the + results of which are in good agreement with recent experimental + results. As it increases near the phase transition temperature, a + quarter of ionic bonds of NH4+-I{\ensuremath{-}} break so that NH4+ + starts to rotate randomly in a disorderly manner, being able to store + thermal energy without a temperature rise. It is found that NH4I + possesses a giant isothermal entropy change ({\ensuremath{\sim}}93{\,} + J{\,}K{\ensuremath{-}}1{\,}kg{\ensuremath{-}}1) and adiabatic + temperature ({\ensuremath{\sim}}27{\,}K) at low driving pressure + ({\ensuremath{\sim}}10{\,}MPa). In addition, through partial + substitution of I by Br in NH4I, it is found that the thermal + conductivity can be remarkably improved, ascribed to the enhancement + of lifetime of low frequency phonons contributed by bromine and + iodine. The present work provides a method and important guidance for + the future exploration and design of barocaloric material for + practical applications.}, + doi = {10.1063/5.0131696}, +} + + +@Article{Wisesa_JPhysChemLett_2023_v14_p468, + author = {Pandu Wisesa and Christopher M Andolina and Wissam A Saidi}, + title = {{Development and Validation of Versatile Deep Atomistic Potentials for + Metal Oxides}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 2, + pages = {468--475}, + annote = {Machine learning interatomic potentials powered by neural networks + have been shown to readily model a gradient of compositions in + metallic systems. However, their application to date on ionic systems + tends to focus on specific compositions and oxidation states owing to + their more heterogeneous chemical nature. Herein we show that a deep + neural network potential (DNP) can model various properties of metal + oxides with different oxidation states without additional charge + information. We created and validated DNPs for AgxOy, CuxOy MgxOy, + PtxOy, and ZnxOy, whereby each system was trained without any + limitations on oxidation states. We illustrate how the database can be + augmented to enhance the DNP transferability for a new polymorph, + surface energies, and thermal expansion. In addition, we show that + these potentials can correctly interpolate significant pressure and + temperature ranges, exhibit stability over long molecular dynamics + simulation time scales, and replicate nonharmonic thermal expansion, + consistent with experimental results.}, + doi = {10.1021/acs.jpclett.2c03445}, +} + + +@Article{Panagiotopoulos_JPhysChemB_2023_v127_p430, + author = {Athanassios Z Panagiotopoulos and Shuwen Yue}, + title = {{Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations}}, + journal = {J. Phys. Chem. B}, + year = 2023, + volume = 127, + issue = 2, + pages = {430--437}, + annote = {This Perspective article focuses on recent simulation work on the + dynamics of aqueous electrolytes. It is well-established that full- + charge, nonpolarizable models for water and ions generally predict + solution dynamics that are too slow in comparison to experiments. + Models with reduced (scaled) charges do better for solution + diffusivities and viscosities but encounter issues describing other + dynamic phenomena such as nucleation rates of crystals from solution. + Polarizable models show promise, especially when appropriately + parametrized, but may still miss important physical effects such as + charge transfer. First-principles calculations are starting to emerge + for these properties that are in principle able to capture + polarization, charge transfer, and chemical transformations in + solution. While direct ab initio simulations are still too slow for + simulations of large systems over long time scales, machine-learning + models trained on appropriate first-principles data show significant + promise for accurate and transferable modeling of electrolyte solution + dynamics.}, + doi = {10.1021/acs.jpcb.2c07477}, +} + + +@Article{Wen_ProcNatlAcadSciUSA_2023_v120_pe2212250120, + author = {Bo Wen and Marcos F {Calegari Andrade} and Li-Min Liu and Annabella + Selloni}, + title = {{Water dissociation at the water{\textendash}rutile TiO + 2 (110) interface from ab{~}initio-based deep + neural network simulations}}, + journal = {Proc. Natl. Acad. Sci. U. S. A.}, + year = 2023, + volume = 120, + issue = 2, + pages = {e2212250120}, + annote = {The interaction of water with TiO2 surfaces is of crucial importance + in various scientific fields and applications, from photocatalysis for + hydrogen production and the photooxidation of organic pollutants to + self-cleaning surfaces and bio-medical devices. In particular, the + equilibrium fraction of water dissociation at the TiO2-water interface + has a critical role in the surface chemistry of TiO2, but is difficult + to determine both experimentally and computationally. Among TiO2 + surfaces, rutile TiO2(110) is of special interest as the most abundant + surface of TiO2's stable rutile phase. While surface-science studies + have provided detailed information on the interaction of rutile + TiO2(110) with gas-phase water, much less is known about the + TiO2(110)-water interface, which is more relevant to many + applications. In this work, we characterize the structure of the + aqueous TiO2(110) interface using nanosecond timescale molecular + dynamics simulations with ab{~}initio-based deep neural network + potentials that accurately describe water/TiO2(110) interactions over + a wide range of water coverages. Simulations on TiO2(110) slab models + of increasing thickness provide insight into the dynamic equilibrium + between molecular and dissociated adsorbed water at the interface and + allow us to obtain a reliable estimate of the equilibrium fraction of + water dissociation. We find a dissociation fraction of 22 + {\ensuremath{\pm}} 6% with an associated average hydroxyl lifetime of + 7.6 {\ensuremath{\pm}} 1.8 ns. These quantities are both much larger + than corresponding estimates for the aqueous anatase TiO2(101) + interface, consistent with the higher water photooxidation activity + that is observed for rutile relative to anatase.}, + PMCID = {PMC9926290}, + doi = {10.1073/pnas.2212250120}, +} + + +@Article{Jin_JChemTheoryComput_2023_v19_p7343, + author = {Bin Jin and Taiping Hu and Kuang Yu and Shenzhen Xu}, + title = {{Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical + Reaction Simulations}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + issue = 20, + pages = {7343--7357}, + annote = {Most electrochemical reactions should be studied under a grand + canonical ensemble condition with a constant potential and/or a + constant pH value. Free energy profiles provide key insights into + understanding the reaction mechanisms. However, many molecular + dynamics (MD)-based theoretical studies for electrochemical reactions + did not employ an exact grand canonical ensemble sampling scheme for + the free energy calculations, partially due to the issues of + discontinuous trajectories induced by the particle-number variations + during MD simulations. An alternative statistical sampling approach, + the Monte Carlo (MC) method, is naturally appropriate for the open- + system simulations if we focus on the thermodynamic properties. An + advanced MC scheme, the hybrid Monte Carlo (HMC) method, which can + efficiently sample the configurations of a system with large degrees + of freedom, however, has limitations in the constrained-sampling + applications. In this work, we propose an adjusted constrained HMC + method to compute free energy profiles using the thermodynamic + integration (TI) method. The key idea of the method for handling the + constraint in TI is to integrate the reaction coordinate and sample + the rest degrees of freedom by two types of MC schemes, the HMC scheme + and the Metropolis algorithm with unbiased trials (M(RT)2-UB). We test + the proposed method on three different systems involving two kinds of + reaction coordinates, which are the distance between two particles and + the difference of particles' distances, and compare the results to + those generated by the constrained M(RT)2-UB method serving as + benchmarks. We show that our proposed method has the advantages of + high sampling efficiency and convenience of implementation, and the + accuracy is justified as well. In addition, we show in the third test + system that the proposed constrained HMC method can be combined with + the path integral method to consider the nuclear quantum effects, + indicating a broader application scenario of the sampling method + reported in this work.}, + doi = {10.1021/acs.jctc.3c00571}, +} + + +@Article{Zhao_JMaterChemA_2023_v11_p23999, + author = {Jia Zhao and Taixi Feng and Guimin Lu and Jianguo Yu}, + title = {{Insights into the local structure evolution and thermophysical + properties of NaCl{\textendash}KCl{\textendash}MgCl2{\texte + ndash}LaCl3 melt driven by machine learning}}, + journal = {J. Mater. Chem. A}, + year = 2023, + volume = 11, + issue = 44, + pages = {23999--24012}, + annote = {The local structure evolution and thermophysical properties of + the NaCl{\textendash}KCl{\textendash}MgCl2{\texte + ndash}LaCl3 melt were thoroughly understood, + which facilitates the advancement and innovation of molten salt + electrolytic production for Mg{\textendash}La alloys.}, + doi = {10.1039/d3ta03434h}, +} + + +@Article{Avula_JPhysChemLett_2023_v14_p9500, + author = {Nikhil V S Avula and Michael L Klein and Sundaram Balasubramanian}, + title = {{Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes + Using Machine Learned Potentials}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + issue = 42, + pages = {9500--9507}, + annote = {The diffusivity of water in aqueous cesium iodide solutions is larger + than that in neat liquid water and vice versa for sodium chloride + solutions. Such peculiar ion-specific behavior, called anomalous + diffusion, is not reproduced in typical force field based molecular + dynamics (MD) simulations due to inadequate treatment of ion-water + interactions. Herein, this hurdle is tackled by using machine learned + atomic potentials (MLPs) trained on data from density functional + theory calculations. MLP based atomistic MD simulations of aqueous + salt solutions reproduce experimentally determined thermodynamic, + structural, dynamical, and transport properties, including their + varied trends in water diffusivities across salt concentration. This + enables an examination of their intermolecular structure to unravel + the microscopic underpinnings of the differences in their transport + properties. While both ions in CsI solutions contribute to the faster + diffusion of water molecules, the competition between the heavy + retardation by Na ions and the slight acceleration by Cl ions in NaCl + solutions reduces their water diffusivity.}, + doi = {10.1021/acs.jpclett.3c02112}, +} + + +@Article{Muniz_JPhysChemB_2023_v127_p9165, + author = {Maria Carolina Muniz and Roberto Car and Athanassios Z Panagiotopoulos}, + title = {{Neural Network Water Model Based on the MB-Pol Many-Body Potential}}, + journal = {J. Phys. Chem. B}, + year = 2023, + volume = 127, + issue = 42, + pages = {9165--9171}, + annote = {The MB-pol many-body potential accurately predicts many properties of + water, including cluster, liquid phase, and vapor-liquid equilibrium + properties, but its high computational cost can make applying it in + large-scale simulations quite challenging. In order to address this + limitation, we developed a "deep potential" neural network (DPMD) + model based on the MB-pol potential for water. We find that a DPMD + model trained on mostly liquid configurations yields a good + description of the bulk liquid phase but severely underpredicts vapor- + liquid coexistence densities. By contrast, adding cluster + configurations to the neural network training set leads to a good + agreement for the vapor coexistence densities. Liquid phase densities + under supercooled conditions are also represented well, even though + they were not included in the training set. These results confirm that + neural network models can combine accuracy and transferability if + sufficient attention is given to the construction of a representative + training set for the target system.}, + doi = {10.1021/acs.jpcb.3c04629}, +} + + +@Article{Zhang_JPhysChemB_2023_v127_p8926, + author = {Wei Zhang and Li Zhou and Tinggui Yan and Mohan Chen}, + title = {{Speciation of La3+{\textendash}Cl{\textendash} + Complexes in Hydrothermal Fluids from Deep Potential Molecular + Dynamics}}, + journal = {J. Phys. Chem. B}, + year = 2023, + volume = 127, + issue = 41, + pages = {8926--8937}, + annote = {The stability of rare earth element (REE) complexes plays a crucial + role in quantitatively assessing their hydrothermal migration and + transformation. However, reliable data are lacking under high- + temperature hydrothermal conditions, which hampers our understanding + of the association behavior of REE. Here a deep learning potential + model for the LaCl3-H2O system in hydrothermal fluids is developed + based on the first-principles density functional theory calculations. + The model accurately predicts the radial distribution functions + compared to ab initio molecular dynamics (AIMD) simulations. + Furthermore, species of La-Cl complexes, the dissociation pathway of + the La-Cl complexes dissociation process, and the potential of mean + forces and corresponding association constants (logK) for LaCln3-n (n + = 1-4) are extensively investigated under a wide range of temperatures + and pressures. Empirical density models for logK calculation are + fitted with these data and can accurately predict logK data from both + experimental results and AIMD simulations. The distribution of La-Cl + species is also evaluated across a wide range of temperatures, + pressures, and initial chloride concentration conditions. The results + show that La-Cl complexes are prone to forming in a low-density + solution, and the number of bonded Cl- ions increases with rising + temperature. In contrast, in a high-density solution, La3+ dominates + and becomes the more prevalent species.}, + doi = {10.1021/acs.jpcb.3c05428}, +} + + +@Article{Xia_JMaterChemA_2023_vNone_pNone, + author = {Weiyi Xia and Ling Tang and Huaijun Sun and Chao Zhang and Kai-Ming Ho + and Gayatri Viswanathan and Kirill Kovnir and Cai-Zhuang Wang}, + title = {{Accelerating materials discovery using integrated deep machine + learning approaches}}, + journal = {J. Mater. Chem. A}, + year = 2023, + annote = {Our work introduces an innovative deep machine learning + framework to significantly accelerate novel materials discovery, as + demonstrated by its application to the La{\textendash}Si{\textendash}P + system where new ternary and quaternary compounds were successfully + identified.}, + doi = {10.1039/d3ta03771a}, +} + + +@Article{Piaggi_FaradayDiscuss_2023_vNone_pNone, + author = {Pablo M Piaggi and Annabella Selloni and Athanassios Z Panagiotopoulos + and Roberto Car and Pablo G Debenedetti}, + title = {{A first-principles machine-learning force field for heterogeneous ice + nucleation on microcline feldspar}}, + journal = {Faraday Discuss.}, + year = 2023, + annote = {The formation of ice in the atmosphere affects precipitation and cloud + properties, and plays a key role in the climate of our planet. + Although ice can form directly from liquid water under deeply + supercooled conditions, the presence of foreign particles can aid ice + formation at much warmer temperatures. Over the past decade, + experiments have highlighted the remarkable efficiency of feldspar + minerals as ice nuclei compared to other particles present in the + atmosphere. However, the exact mechanism of ice formation on feldspar + surfaces has yet to be fully understood. Here, we develop a first- + principles machine-learning model for the potential energy surface + aimed at studying ice nucleation at microcline feldspar surfaces. The + model is able to reproduce with high-fidelity the energies and forces + derived from density-functional theory (DFT) based on the SCAN + exchange and correlation functional. Our training set includes + configurations of bulk supercooled water, hexagonal and cubic ice, + microcline, and fully-hydroxylated feldspar surfaces exposed to a + vacuum, liquid water, and ice. We apply the machine-learning force + field to study different fully-hydroxylated terminations of the (100), + (010), and (001) surfaces of microcline exposed to a vacuum. Our + calculations suggest that terminations that do not minimize the number + of broken bonds are preferred in a vacuum. We also study the structure + of supercooled liquid water in contact with microcline surfaces, and + find that water density correlations extend up to around 10 {\r{A}} + from the surfaces. Finally, we show that the force field maintains a + high accuracy during the simulation of ice formation at microcline + surfaces, even for large systems of around 30{\,}000 atoms. Future + work will be directed towards the calculation of nucleation free- + energy barriers and rates using the force field developed herein, and + understanding the role of different microcline surfaces in ice + nucleation.}, + doi = {10.1039/d3fd00100h}, +} + +@article{Gegentana_Ionics_2023, + title={A deep potential molecular dynamics study on the ionic structure and transport properties of NaCl-CaCl2 molten salt}, + author={Gegentana and Cui, Liu and Zhou, Leping and Du, Xiaoze}, + journal={Ionics}, + pages={1--11}, + year={2023}, + publisher={Springer} +} + +@Article{Li_JournaloftheEuropeanCeramicSociety_2024_v44_p659, + author = {Jun Li and Kun Luo and Qi An}, + title = {{Mobile dislocation mediated Hall-Petch and inverse Hall-Petch + behaviors in nanocrystalline Al-doped boron carbide}}, + journal = {Journal of the European Ceramic Society}, + year = 2024, + volume = 44, + issue = 2, + pages = {659--667}, + doi = {10.1016/j.jeurceramsoc.2023.09.079}, +} + + +@Article{Achar_JournalofMaterialsResearch_2023_vNone_pNone, + author = {Siddarth K. Achar and Leonardo Bernasconi and Juan J. Alvarez and J. + Karl Johnson}, + title = {{Deep-learning potentials for proton transport in double-sided + graphanol}}, + journal = {Journal of Materials Research}, + year = 2023, + doi = {10.1557/s43578-023-01141-3}, +} + + +@Article{Ding_Tungsten_2023_vNone_pNone, + author = {Chang-Jie Ding and Ya-Wei Lei and Xiao-Yang Wang and Xiao-Lin Li and + Xiang-Yan Li and Yan-Ge Zhang and Yi-Chun Xu and Chang-Song Liu and + Xue-Bang Wu}, + title = {{A deep learning interatomic potential suitable for simulating + radiation damage in bulk tungsten}}, + journal = {Tungsten}, + year = 2023, + doi = {10.1007/s42864-023-00230-4}, +} + + +@Article{Zhang_PhysChemChemPhys_2023_v25_p15422, + author = {Pan Zhang and Wenkai Liao and Ziyang Zhu and Mi Qin and Zhenhua Zhang + and Dan Jin and Yong Liu and Ziyu Wang and Zhihong Lu and Rui Xiong}, + title = {{Tuning the lattice thermal conductivity of + Sb2Te3 by Cr doping: a deep potential molecular + dynamics study}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2023, + volume = 25, + issue = 22, + pages = {15422--15432}, + annote = {Element doping is a prominent method for reducing the lattice thermal + conductivity and optimizing the thermoelectric performance of + materials in the thermoelectric field. However, determination of the + thermal conductivity of element-doped systems is a challenging task, + especially when the elements are randomly doped. In this work, a + first-principles based deep neural network potential (NNP) is + developed to investigate the lattice thermal transport properties of + Cr-doped Sb2Te3 using molecular dynamics simulations. Compared with + pure Sb2Te3, the thermal conductivity of orderly Cr-doped Sb2Te3 with + Cr atoms locating at specific atomic layer positions decreases + slightly in the in-plane direction, but sharply in the out-of-plane + direction. The decrease of the low frequency phonon density of states + and the enhancement of phonon scattering near 2.5 THz are the primary + reasons for the decrease in the thermal conductivity of Cr-doped + Sb2Te3, while the decrease of phonon velocity due to band flattening + is the reason for the sharp decrease of thermal conductivity in the + out-of-plane direction. Moreover, the thermal conductivities of + randomly Cr-doped Sb2Te3 with different Cr concentrations are also + investigated using the NNP. It is found that the thermal + conductivities in both the in-plane and out-of-plane directions are + reduced by 76% and 80%, respectively, for Sb36Cr36Te108. Furthermore, + the influence of different Cr dopant arrays on the thermal + conductivity of Sb2Te3 is also predicted using the NNP. Our work + provides a good example for predicting the thermal conductivity of + element-doped systems using the NNP combined with molecular dynamics + simulations.}, + doi = {10.1039/d3cp00999h}, +} + + +@Article{Li_JournalofSustainableCementBasedMaterials_2023_v12_p1335, + author = {Weihuan Li and Yang Zhou and Li Ding and Pengfei Lv and Yifan Su and + Rui Wang and Changwen Miao}, + title = {{A deep learning-based potential developed for calcium silicate + hydrates with both high accuracy and efficiency}}, + journal = {Journal of Sustainable Cement-Based Materials}, + year = 2023, + volume = 12, + issue = 11, + pages = {1335--1346}, + doi = {10.1080/21650373.2023.2219251}, +} + + +@Article{Zhang_PhysChemChemPhys_2023_v25_p13297, + author = {Zhou Zhang and Zhongyun Ma and Yong Pei}, + title = {{Li ion diffusion behavior of Li3OCl solid-state + electrolytes with different defect structures: insights from the deep + potential model}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2023, + volume = 25, + issue = 19, + pages = {13297--13307}, + annote = {Li3OX (X = Cl, Br), a lithium-rich anti-perovskite material developed + in recent years, has received tremendous attention due to its high + ionic conductivity of >10-3 S cm-1 at room temperature. However, the + origin of the high ionic conductivity of the material at the atomic + level is still not clear. In this work, we investigated the dynamic + behavior of the Li3OCl system with three different defect structures + (Li-Frenkel, LiCl-Schottky, and Cl-O anti-site disorder) at seven + temperature intervals and calculated its ionic conductivity using the + deep potential (DP) model. The results show that the presence of LiCl- + Schottky defects is the main reason for the high performance of the + Li3OCl system, and the Li vacancy is the main carrier. The ionic + conductivity obtained from the DP model is 0.49 {\texttimes} 10-3 S + cm-1 at room temperature and it can reach 10-2 S cm-1 above the + melting point, which is in the same order of magnitude as the + experimentally reported results. We also explored the effect of + different defect concentrations on the ionic conductivity and + migration activation energy. This work also demonstrates the + feasibility of the DP method for solving the accuracy-efficiency + dilemma of ab initio molecular dynamics (AIMD) and classical molecular + dynamics simulations.}, + doi = {10.1039/d2cp06073f}, +} + + +@Article{Thong_PhysRevB_2023_v107_p014101, + author = {Hao-Cheng Thong and XiaoYang Wang and Jian Han and Linfeng Zhang and + Bei Li and Ke Wang and Ben Xu}, + title = {{Machine learning interatomic potential for molecular dynamics + simulation of the ferroelectric KNbO3< + /mml:math> perovskite}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 107, + issue = 1, + pages = 014101, + doi = {10.1103/PhysRevB.107.014101}, +} + + +@Article{Shang_Fuel_2024_v357_p129909, + author = {Zhe Shang and Hui Li}, + title = {{Unraveling pyrolysis mechanisms of lignin dimer model compounds: + Neural network-based molecular dynamics simulation investigations}}, + journal = {Fuel}, + year = 2024, + volume = 357, + pages = 129909, + doi = {10.1016/j.fuel.2023.129909}, +} + + +@Article{He_ActaMaterialia_2024_v262_p119416, + author = {Ri He and Bingwen Zhang and Hua Wang and Lei Li and Ping Tang and + Gerrit Bauer and Zhicheng Zhong}, + title = {{Ultrafast switching dynamics of the ferroelectric order in stacking- + engineered ferroelectrics}}, + journal = {Acta Materialia}, + year = 2024, + volume = 262, + pages = 119416, + doi = {10.1016/j.actamat.2023.119416}, +} + diff --git a/source/papers/deepmd-kit/index.md b/source/papers/deepmd-kit/index.md index 4b09af66..8b6ee2b8 100644 --- a/source/papers/deepmd-kit/index.md +++ b/source/papers/deepmd-kit/index.md @@ -1,14 +1,154 @@ --- title: Publications driven by DeePMD-kit date: 2022-05-01 -update: 2022-10-25 +update: 2023-11-28 mathjax: true --- The following publications have used the DeePMD-kit software. Publications that only mentioned the DeePMD-kit will not be included below. +We encourage explicitly mentioning DeePMD-kit with proper citations in your publications, so we can more easily find and list these publications. + +Last update date: Nov 28, 2023 + ## 2023 {% publications %} +Thong_PhysRevB_2023_v107_p014101, +Zhang_PhysChemChemPhys_2023_v25_p13297, +Li_JournalofSustainableCementBasedMaterials_2023_v12_p1335, +Zhang_PhysChemChemPhys_2023_v25_p15422, +Ding_Tungsten_2023_vNone_pNone, +Achar_JournalofMaterialsResearch_2023_vNone_pNone, +Gegentana_Ionics_2023, +Piaggi_FaradayDiscuss_2023_vNone_pNone, +Xia_JMaterChemA_2023_vNone_pNone, +Zhang_JPhysChemB_2023_v127_p8926, +Muniz_JPhysChemB_2023_v127_p9165, +Avula_JPhysChemLett_2023_v14_p9500, +Zhao_JMaterChemA_2023_v11_p23999, +Jin_JChemTheoryComput_2023_v19_p7343, +Wen_ProcNatlAcadSciUSA_2023_v120_pe2212250120, +Wisesa_JPhysChemLett_2023_v14_p468, +Xu_Unknown_2023_v122_pNone, +Zhang_PhysChemChemPhys_2023_v25_p6164, +Sours_JPhysChemCNanomaterInterfaces_2023_v127_p1455, +Deng_PhysRevB_2023_v107_p064103, +Yao_RSCAdv_2023_v13_p4565, +Deng_ComputationalMaterialsScience_2023_v218_p111941, +FidalgoCandido_JChemPhys_2023_v158_p064502, +Gomes-Filho_JPhysChemB_2023_v127_p1422, +Li_PhysChemChemPhys_2023_v25_p6746, +Zhang_JChemInfModel_2023_v63_p1133, +Zeng_JChemTheoryComput_2023_v19_p1261, +Xiao_Unknown_2023_v133_pNone, +Zhai_JChemPhys_2023_v158_p084111, +Sterkhova_JournalofPhysicsandChemistryofSolids_2023_v174_p111143, +Li_CementandConcreteResearch_2023_v165_p107092, +Wang_PhysRevMaterials_2023_v7_p034601, +Balyakin_JetpLett_2023_v117_p370, +Wu_JPhysChemLett_2023_v14_p2208, +Li_InternationalJournalofMechanicalSciences_2023_v242_p107998, +Zheng_ACSNano_2023_v17_p5579, +Zeng_JChemPhys_2023_v158_p124110, +Cioni_JChemPhys_2023_v158_p124701, +Bu_JournalofMolecularLiquids_2023_v375_p120689, +Li_InternationalJournalofPlasticity_2023_v163_p103552, +Wang_Unknown_2023_v12_p803, +Xu_Nanomaterials_2023_v13_p1352, +Liu_ACSMaterialsLett_2023_v5_p1009, +JaffrelotInizan_ChemSci_2023_v14_p5438, +Wu_JPhysChemC_2023_v127_p6262, +Chang_PhysChemChemPhys_2023_v25_p12841, +Zhao_JPhysChemC_2023_v127_p6852, +Ghosh_JPhysCondensMatter_2023_v35_p154002, +Luo_JPhysChemC_2023_v127_p7071, +Hu_JPhysChemLett_2023_v14_p3677, +He_ComputationalMaterialsScience_2023_v223_p112111, +Yuan_EarthandPlanetaryScienceLetters_2023_v609_p118084, +Han_Nanomaterials_2023_v13_p1576, +Chen_PhysRevMaterials_2023_v7_p053603, +Giese_JChemPhys_2023_v158_pNone, +Sanchez-Burgos_JChemPhys_2023_v158_pNone, +Mathur_JPhysChemB_2023_v127_p4562, +Li_JPhysChemC_2023_v127_p9750, +Lu_JChemPhys_2023_v158_pNone, +Achar_ACSApplMaterInterfaces_2023_v15_p25873, +Yeo_AppliedSurfaceScience_2023_v621_p156893, +Xie_SolarEnergyMaterialsandSolarCells_2023_v254_p112275, +Zhao_IEEETransCircuitsSystI_2023_v70_p2439, +Wang_GeochimicaetCosmochimicaActa_2023_v350_p57, +Qi_JMaterSci_2023_v58_p9515, +Sun_PhysRevB_2023_v107_p224301, +Fronzi_Nanomaterials_2023_v13_p1832, +Wang_Unknown_2023_v122_pNone, +Caruso_JChemPhys_2023_v158_pNone, +Zhuang_JPhysChemC_2023_v127_p10532, +Li_Unknown_2023_v133_pNone, +Wang_JPhysChemC_2023_v127_p11369, +CalegariAndrade_JPhysChemLett_2023_v14_p5560, +Qu_JElectronMater_2023_v52_p4475, +Fan_JournalofEnergyChemistry_2023_v82_p239, +Wen_InternationalJournalofPlasticity_2023_v166_p103644, +Ran_JPhysChemLett_2023_v14_p6028, +Ko_JChemTheoryComput_2023_v19_p4182, +Huo_JChemTheoryComput_2023_v19_p4243, +Xie_JPhysChemC_2023_v127_p13228, +Ding_JChemPhys_2023_v159_pNone, +Liu_JChemPhys_2023_v159_pNone_2, +Guo_JChemPhys_2023_v159_pNone_2, +Crippa_ProcNatlAcadSciUSA_2023_v120_pe2300565120, +Deng_ACSNano_2023_v17_p14099, +Liu_JChemPhys_2023_v159_pNone, +Xiao_Unknown_2023_v123_pNone, +Ren_NatMater_2023_v22_p999, +Andolina_DigitalDiscovery_2023_v2_p1070, +Hou_AngewChemIntEdEngl_2023_v62_pe202304205, +Zeng_JChemPhys_2023_v159_pNone, +Piaggi_JChemPhys_2023_v159_pNone, +Tuo_AdvFunctMaterials_2023_v33_pNone, +Zhang_JPhysChemB_2023_v127_p7011, +Chtchelkatchev_JChemPhys_2023_v159_pNone, +Sowa_JPhysChemLett_2023_v14_p7215, +Zhang_JPhysChemLett_2023_v14_p7141, +Zhang_PhysRevLett_2023_v131_p076801, +Liu_JChemTheoryComput_2023_v19_p5602, +Stoppelman_JChemPhys_2023_v159_pNone, +Yang_NatCatal_2023_v6_p829, +Wang_PhysRevMaterials_2023_v7_p093601, +Guo_JChemPhys_2023_v159_pNone, +Fu_AdvFunctMaterials_2023_v33_pNone, +Yu_ChemMater_2023_v35_p6651, +Gupta_JMaterChemA_2023_v11_p21864, +Shen_JAmChemSoc_2023_v145_p20511, +Zeng_ActaPhysSin_2023_v72_p187102, +Urata_Unknown_2023_v134_pNone, +Wu_JPhysChemC_2023_v127_p19115, +Wang_Unknown_2023_v36_p573, +Zhang_ProcNatlAcadSciUSA_2023_v120_pe2309952120, +Wisesa_JPhysChemLett_2023_v14_p8741, +Li_JChemPhys_2023_v159_pNone, +Wan_JColloidInterfaceSci_2023_v648_p317, +He_SolidStateIonics_2023_v399_p116298, +Liu_ChemicalEngineeringJournal_2023_v474_p145355, +Hu_SciChinaChem_2023_v66_p3297, +Wang_EarthandPlanetaryScienceLetters_2023_v621_p118368, +Dai_NatEnergy_2023_v8_p1221, +Deng_TheoreticalandAppliedMechanicsLetters_2023_v13_p100481, +Zhang_EnergyStorageMaterials_2023_v63_p103069, +Wu_JChemPhys_2023_v159_pNone, +Sun_NatCommun_2023_v14_p1656, +Wang_NatCommun_2023_v14_p2924, +Bore_NatCommun_2023_v14_p3349, +Lu_NatCommun_2023_v14_p4077, +Lin_NatCommun_2023_v14_p4110, +Liu_npjComputMater_2023_v9_p174, +Zhang_ActaMaterialia_2023_v261_p119364, +Gupta_NatCommun_2023_v14_p6884, +Liang_InternationalJournalofHeatandMassTransfer_2023_v217_p124705, +Qi_JournalofNonCrystallineSolids_2023_v622_p122682, +Li_JPhysCondensMatter_2023_v35_p505001, +Xu_ACSApplMaterInterfaces_2023_vNone_pNone, +Fu_JMaterChemA_2023_v11_p742, Huang_EnergyandAI_2023_v11_p100210, Ling_JournalofPowerSources_2023_v555_p232350, Li_JournaloftheEuropeanCeramicSociety_2023_v43_p208, @@ -20,6 +160,12 @@ Zhai_ComputationalMaterialsScience_2023_v216_p111843 ## 2022 {% publications %} +Bayerl_DigitalDiscovery_2022_v1_p61, +Jiang_NatCommun_2022_v13_p6067, +Li_PhysRevApplied_2022_v18_p064067, +Li_ActaPhysSin_2022_v71_p247803, +Chahal_JACSAu_2022_v2_p2693, +Li_GeophysicalResearchLetters_2022_v49_pNone, Mondal_JChemTheoryComput_2022_vNone_pNone, Kobayashi_ChemCommun_2022_v58_p13939, Wu_ACSApplMaterInterfaces_2022_v14_p55753, diff --git a/source/papers/dpgen/index.md b/source/papers/dpgen/index.md index b69c1340..a30142bb 100644 --- a/source/papers/dpgen/index.md +++ b/source/papers/dpgen/index.md @@ -1,14 +1,61 @@ --- title: Publications driven by DP-GEN date: 2022-05-11 -update: 2022-10-25 +update: 2023-11-28 mathjax: true --- The following publications have used the DP-GEN software. Publications that only mentioned the DP-GEN will not be included below. +We encourage explicitly mentioning DP-GEN with proper citations in your publications, so we can more easily find and list these publications. + +Last update date: Nov 28, 2023 + ## 2023 {% publications %} +Thong_PhysRevB_2023_v107_p014101, +Zhang_PhysChemChemPhys_2023_v25_p13297, +Ding_Tungsten_2023_vNone_pNone, +Achar_JournalofMaterialsResearch_2023_vNone_pNone, +Zhang_JPhysChemB_2023_v127_p8926, +Muniz_JPhysChemB_2023_v127_p9165, +Zhao_JMaterChemA_2023_v11_p23999, +Jin_JChemTheoryComput_2023_v19_p7343, +Xu_Unknown_2023_v122_pNone, +Li_PhysChemChemPhys_2023_v25_p6746, +Sterkhova_JournalofPhysicsandChemistryofSolids_2023_v174_p111143, +Li_CementandConcreteResearch_2023_v165_p107092, +Wang_PhysRevMaterials_2023_v7_p034601, +Balyakin_JetpLett_2023_v117_p370, +Li_InternationalJournalofPlasticity_2023_v163_p103552, +Wang_Unknown_2023_v12_p803, +Wu_JPhysChemC_2023_v127_p6262, +Chang_PhysChemChemPhys_2023_v25_p12841, +Hu_JPhysChemLett_2023_v14_p3677, +Yuan_EarthandPlanetaryScienceLetters_2023_v609_p118084, +Han_Nanomaterials_2023_v13_p1576, +Chen_PhysRevMaterials_2023_v7_p053603, +Sanchez-Burgos_JChemPhys_2023_v158_pNone, +Mathur_JPhysChemB_2023_v127_p4562, +Lu_JChemPhys_2023_v158_pNone, +Achar_ACSApplMaterInterfaces_2023_v15_p25873, +Wang_GeochimicaetCosmochimicaActa_2023_v350_p57, +Zhuang_JPhysChemC_2023_v127_p10532, +Wang_JPhysChemC_2023_v127_p11369, +Fan_JournalofEnergyChemistry_2023_v82_p239, +Xie_JPhysChemC_2023_v127_p13228, +Liu_JChemPhys_2023_v159_pNone, +Chtchelkatchev_JChemPhys_2023_v159_pNone, +Zhang_JPhysChemLett_2023_v14_p7141, +Wang_PhysRevMaterials_2023_v7_p093601, +Fu_AdvFunctMaterials_2023_v33_pNone, +Zhang_ProcNatlAcadSciUSA_2023_v120_pe2309952120, +Deng_TheoreticalandAppliedMechanicsLetters_2023_v13_p100481, +Zhang_EnergyStorageMaterials_2023_v63_p103069, +Wang_NatCommun_2023_v14_p2924, +Lu_NatCommun_2023_v14_p4077, +Zhang_ActaMaterialia_2023_v261_p119364, +Liang_InternationalJournalofHeatandMassTransfer_2023_v217_p124705, Ling_JournalofPowerSources_2023_v555_p232350, Liu_PhysChemChemPhys_2023_vNone_pNone, Zhai_ComputationalMaterialsScience_2023_v216_p111843 @@ -16,6 +63,8 @@ Zhai_ComputationalMaterialsScience_2023_v216_p111843 ## 2022 {% publications %} +Bayerl_DigitalDiscovery_2022_v1_p61, +Jiang_NatCommun_2022_v13_p6067, Mondal_JChemTheoryComput_2022_vNone_pNone, Yang_ChinesePhysLett_2022_v39_p116301, Zhuang_JChemPhys_2022_v157_p164701,