Materials Synthesizability Prediction
To the best of our knowledge, this is the first public, collaborative list of machine learning papers on materials synthesis. We try to classify papers based on a combination of their applications and model type. If you have suggestions for other papers or categories, please make a pull request or issue!
Within each category, papers are listed in reverse chronological order (newest first). Where possible, a link should be provided.
Reviews
Tools
Machine-learning guided directed evolution
Representation learning
Unsupervised variant prediction
Generative models
Predicting stability
Classification and annotation
Other supervised learning
Machine Learning for proteins.
ACS Catalysis, December 2019.
[Github link ]
Population-Based Black-Box Optimization for Biological Sequence Design.
Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley.
ICML, July 2020.
[ICML]
Selene: a PyTorch-based deep learning library for sequence data.
Kathleen M. Chen, Evan M. Cofer, Jian Zhou, Olga G. Troyanskaya.
Nature Methods, March 2019.
[doi.org/10.1038/s41592-019-0360-8]
Predicting the Outcomes of Material Syntheses with Deep Learning. Chem Mater, 33, 2, 616–624, Jan 2021. Shreshth A. Malik, Rhys E. A. Goodall, and Alpha A. Lee. [10.1021/acs.chemmater.0c03885]
Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning. Journal of the American Chemical Society 142, no. 44 (2020): 18836-18843. Jang, Jidon, Geun Ho Gu, Juhwan Noh, Juhwan Kim, and Yousung Jung. [JACS 2020]
Machine learning-guided synthesis of advanced inorganic materials. Materials Today, Volume 41, Pages 72-80, Dec 2020. Bijun Tang, Yuhao Lu, Jiadong Zhou, Tushar Chouhan, Han Wang, Prafful Golani, ManzhangXu, Quan Xu, Cuntai Guan, and Zheng Liu. [doi.org/10.1016/j.mattod.2020.06.010Get]
Recent advances of lead-free metal halide perovskite single crystals and nanocrystals: synthesis, crystal structure, optical properties, and their diverse applications. Materials Today Chemistr, Volume 18, 100363, December 2020. Saikat Bhaumika, Smaranika, and RayaSudip K.Batabyal. [10.1016/j.mtchem.2020.100363]
Can we predict materials that can be synthesised?. Chemical Science, Advanced Article, November 2020. Filip T. Szczypinski, Steven Bennett, and Kim E. Jelfs. [10.1039/D0SC04321D]
Synthesis of Perovskite Nanocrystals. Perovskite Quantum Dots, pp 1-18, Aug 2020. He Huang. [10.1007%2F978-981-15-6637-0_1]
High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials. Advanced Materials, 32 (44), 2020. Ivan G.Clayson, Daniel Hewitt, Martin Hutereau, Tom Pope, and Ben Slater. [doi.org/10.1002/adma.202002780]
Learning with uncertainty for biological discovery and design.
Preprint, August 2020
Brian Hie, Bryan Bryson, Bonnie Berger.
[10.1101/2020.08.11.247072]
Colloidal Synthesis of Shape-Controlled Cs2NaBiX6 (X = Cl, Br) Double Perovskite Nanocrystals: Discrete Optical Transition by Non-Bonding Characters and Energy Transfer to Mn Dopants. Chemistry of Materials, 32 (16), 6864-6874, Jun 2020. Wonseok Lee, Doowon Choi, and Sungjee Kim. [10.1021/acs.chemmater.0c01315]
A “Tips and Tricks” Practical Guide to the Synthesis of Metal Halide Perovskite Nanocrystals. Chemistry of Materials, 32 (13), 5410-5423, Jun 2020. Yangning Zhang, Timothy D. Siegler, Cherrelle J. Thomas, Michael K. Abney, Tushti Shah, Anastacia De Gorostiza, Randalynn M. Greene, and Brian A. Korgel. [10.1021/acs.chemmater.0c01735]
Synthesis and Post‐Synthesis Transformation of Germanosilicate Zeolites. Angewandte Chemie International Edition, 59 (44), 2020. Maksym Opanasenko, Mariya Shamzhy, Yunzheng Wang, Wenfu Yan, Petr Nachtigall, and Jiří Čejka. [doi.org/10.1002/ange.202005776]
Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides. Computational Materials 6, Article no. 44 (2020), May 2020. Guoxiang Hu, Victor Fung, Xiahan Sang, Raymond Unocic, and P. Ganesh. [npj 2020]
Nanoporous materials with predicted zeolite topologies. Royal Society of Chemistry Adv, 10, 17760, May 2020. Vladislav A. Blatov, Olga A. Blatova, Frits Daeyaert, and Michael W. Deem. [10.1039/d0ra01888k]
Machine-enabled inverse design of inorganic solid materials: promises and challenges. Chemical Science, no. 11, 2020. Juhwan Noh, Geun Ho Gu, Sungwon Kim, and Yousung Jung. [10.1039/d0sc00594k]
Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields. ACS Nano, 14 (11) , 14761-14768, 2020. Yu Han, Bijun Tang, Liang Wang, Hong Bao, Yuhao Lu, Cuntai Guan, Liang Zhang, Mengying Le, Zheng Liu, and Minghong Wu. [doi.org/10.1021/acsnano.0c01899]
Machine Learning Tools to Predict Hot Injection Syntheses Outcomes for II–VI and IV–VI Quantum Dots. The Journal of Physical Chemistry C, 124 (44) , 24298-24305, 2020. Fábio Baum, Tatiane Pretto, Ariadne Köche, and Marcos José Leite Santos. [doi.org/10.1021/acs.jpcc.0c05993]
Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. Journal of Chemical Information and Modeling, 60 (6) , 2876-2887, 2020. Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, and Thomas Yong-Jin Han. [doi.org/10.1021/acs.jcim.0c00199]
Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles. Scientific Reports, 10 (1), 2020. Francesco Pellegrino, Raluca Isopescu, Letizia Pellutiè, Fabrizio Sordello, Andrea M. Rossi, Erik Ortel, Gianmario Martra, Vasile-Dan Hodoroaba, and Valter Maurino. [doi.org/10.1038/s41598-020-75967-w]
Navigating the design space of inorganic materials synthesis using statistical methods and machine learning.
Dalton Transactions, 49 (33) , 11480-11488, 2020.
Erick J. Braham, Rachel D. Davidson, Mohammed Al-Hashimi, Raymundo Arróyave, and Sarbajit Banerjee.
[doi.org/10.1039/D0DT02028A]
Efficient Syntheses of 2D Materials from Soft Layered Composites Guided by Yield Prediction Model: Potential of Experiment‐Oriented Materials Informatics. Advanced Theory and Simulations, 3 (7) , 2000084, 2020. Kyohei Noda, Yasuhiko Igarashi, Hiroaki Imai, and Yuya Oaki. [doi.org/10.1002/adts.202000084]
Inverse Design of Solid-State Materials via a Continuous Representation. Matter, Volume 1, Issue 5, Pages 1370-1384, Nov 2019. JuhwanNoh, JaehoonKim, Helge S.Stein, Benjamin Sanchez-Lengeling, John M.Gregoire, Alan Aspuru-Guzik, and Yousung Jung. [doi.org/10.1016/j.matt.2019.08.017]
Vapour-phase-transport rearrangement technique for the synthesis of new zeolites. Nature Communications volume 10, Article number: 5129, Nov 2019. Valeryia Kasneryk, Mariya Shamzhy, Jingtian Zhou, Qiudi Yue, Michal Mazur, Alvaro Mayoral, Zhenlin Luo, Russell E. Morris, Jiří Čejka, and Maksym Opanasenko. [doi.org/10.1038/s41467-019-12882-3]
Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery. Acc. Chem. Res. 2019, 52, 10, 2971–2980, Sep 2019. Manuel Moliner, Yuriy Román-Leshkov, and Avelino Corma. [doi.org/10.1021/acs.accounts.9b00399]
Semi-supervised machine-learning classification of materials synthesis procedures. npj Computational Materials volume 5, Article number: 62, July 2019. Haoyan Huo, Ziqin Rong, Olga Kononova, Wenhao Sun, Tiago Botari, Tanjin He, Vahe Tshitoyan, and Gerbrand Ceder. [s41524-019-0204-1.pdf]
Synthetic Evolution of Colloidal Metal Halide Perovskite Nanocrystals. ACS Langmuir, 35 (36), 11609-11628, June 2019. Chun Kiu Ng, Chujie Wang, and Jacek J. Jasieniak. [10.1021/acs.langmuir.9b00855]
Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning. ACS Nano 13 (3), 3031-3041, March 2019. Nathan C. Frey, Jin Wang, Gabriel Iván Vega Bellido, Babak Anasori, Yury Gogotsi, and Vivek B. Shenoy. [10.1021/acsnano.8b08014]
Metal Halide Perovskite Nanocrystals: Synthesis, Post-Synthesis Modifications, and Their Optical Properties. Chemical Reviews, 119 (5), 3296-3348, Feb 2019. Javad Shamsi, Alexander S. Urban, Muhammad Imran, Luca De Trizio, and Liberato Manna. [10.1021/acs.chemrev.8b00644]
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods. npj Computational Materials volume 5, Article number: 70, 2019. Alfred Ludwig. [doi.org/10.1038/s41524-019-0205-0]
Towards scalable synthesis of high-quality PbS colloidal quantum dots for photovoltaic applications. Journal of Material Chemistry C, Issue 6, 2019. Sijie Zhou, Zeke Liu, Yongjie Wang, Kunyuan Lu, Fan Yang, Mengfan Gu, Yalong Xu, Si Chen, Xufeng Ling, Yannan Zhang, Fangchao Li, Jianyu Yuan, and Wanli Ma. [10.1039/C8TC05353G]
High-throughput assessment of hypothetical zeolite materials for their synthesizeability and industrial deployability. De Gruyter, 2019 Jose Luis Salcedo Perez, Maciej Haranczyk, and Nils Edvin Richard Zimmermann. [doi.org/10.1515/zkri-2018-2155]
A New Lithium‐Ion Conductor LiTaSiO5: Theoretical Prediction, Materials Synthesis, and Ionic Conductivity. Advanced Functional Materials, 2019. Qi Wang, Jian‐Fang Wu, Ziheng Lu, Francesco Ciucci, Wei Kong Pang, and Xin Guo. [doi.org/10.1002/adfm.201904232]
Predicting Synthesizability. UC Berkeley, Journal of physics D: Applied physics, 52(1), 2019. Albert Davydov and Ursula Kattner. [10.1088/1361-6463/aad926]
Network analysis of synthesizable materials discovery. Nature Communications 10, no. 2018 (2019). Muratahan Ayko, Vinay I. Hegde, Linda Hung, Santosh Suram, Patrick Herring, Chris Wolverton2, and Jens S. Hummelshøj. [s41467-019-10030-5]
Robust and synthesizable photocatalysts for CO2 reduction: a data-driven materials discovery Nature Communications 10, no.443, 2019. Arunima Singh, Joseph Montoya, John Gregoire, and Kristin A. Persson. [10.1038/s41467-019-08356-1]
A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction. ACS Central Science, 5 (5) , 892-899, 2019. Zach Jensen, Edward Kim, Soonhyoung Kwon, Terry Z. H. Gani, Yuriy Román-Leshkov, Manuel Moliner, Avelino Corma, and Elsa Olivetti. [doi.org/10.1021/acscentsci.9b00193]
High-entropy high-hardness metal carbides discovered by entropy descriptors. Nature Communications volume 9, Article number: 4980, Nov 2018. Pranab Sarker, Tyler Harrington, Cormac Toher, Corey Oses, Mojtaba Samiee, Jon-Paul Maria, Donald W. Brenner, Kenneth S. Vecchio, and Stefano Curtarolo. [10.1038/s41467-018-07160-7]
Colloidal Synthesis of Double Perovskite Cs2AgInCl6 and Mn-Doped Cs2AgInCl6 Nanocrystals. Journal of the American Chemical Society, 140 (40), 12989-12995, Sept 2018. Federico Locardi, Matilde Cirignano, Dmitry Baranov, Zhiya Dang, Mirko Prato, Filippo Drago, Maurizio Ferretti, Valerio Pinchetti, Marco Fanciulli, Sergio Brovelli, Luca De Trizio, and Liberato Manna. [10.1021/jacs.8b07983]
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 120, 145301, April 2018. Tian Xie and Jeffrey C. Grossman. [10.1103/PhysRevLett.120]
Thermodynamic limit for synthesis of metastable inorganic materials. Science Advances, Vol. 4, no. 4, eaaq0148, Apr 2018. Muratahan Aykol, Shyam S. Dwaraknath, Wenhao Sun, and Kristin A. Persson. [10.1126/sciadv.aaq0148]
Colloidal Nanocrystals of Lead-Free Double-Perovskite (Elpasolite) Semiconductors: Synthesis and Anion Exchange To Access New Materials. Nano Letters, 18 (2), 1118-1123, Jan 2018. Sidney E. Creutz, Evan N. Crites, Michael C. De Siena, and Daniel R. Gamelin. [doi.org/10.1021/acs.nanolett.7b04659]
Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides.
Journal of Applied Physics, 123 (11) , 115303, 2018.
*Steven R. Young, Artem Maksov, Maxim Ziatdinov, Ye Cao, Matthew Burch, Janakiraman Balachandran, Linglong Li, Suhas Somnath, Robert M. Patton, Sergei V. Kalinin, and Rama K. Vasudevan.
[doi.org/10.1063/1.5009942]
Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry. Nature Communications volume 9, Article number: 4168, 2018. Christopher J. Bartel, Samantha L. Millican, Ann M. Deml, John R. Rumptz, William Tumas, Alan W. Weimer, Stephan Lany, Vladan Stevanović, Charles B. Musgrave & Aaron M. Holder. [doi.org/10.1038/s41467-018-06682-4]
Inverse Material Design in Colloidal Self-Assembly. Deep Blue, Thesis Article, 2018. Geng, Yina. [2027.42/149857]
Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning. Chemistry of Materials, 29, 21, 9436–9444, Oct 2017. Edward Kim, Kevin Huang, Adam Saunders, Andrew McCallum, Gerbrand Ceder, and Elsa Olivetti. [doi.org/10.1021/acs.chemmater.7b03500]
Computationally Driven Two-Dimensional Materials Design: What Is Next?. CS Nano, 11, 8, 7560–7564, Jul 2017. Jie Pan, Stephan Lany, and Yue Qi. [doi.org/10.1021/acsnano.7b04327]
Discovery-Synthesis, Design, and Prediction of Chalcogenide Phases. Inorganic Chemistry, 56, 6, 3158–3173, March 2017. Mercouri G. Kanatzidis. [doi.org/10.1021/acs.inorgchem.7b00188]
Are the inorganic B24N24, Al24N24, B24P24 and Al24P24 nanoclusters synthesizable or not? A DFT study. Inorganica Chimica Acta Volume 456, Pages 18-23, Feb 2017. Ali Akbar Salari. [doi.org/10.1016/j.ica.2016.11.006]
Virtual screening of inorganic materials synthesis parameters with deep learning. npj Computational Materials 3, 53, 2017. Kim, E., Huang, K., Jegelka, S. et al. [10.1038/s41524-017-0055-6]
The thermodynamic scale of inorganic crystalline metastability. Science Advances, Vol. 2, no. 11, e1600225, Nov 2016. Wenhao Sun, Stephen T. Dacek, Shyue Ping Ong, Geoffroy Hautier, Anubhav Jain, William D. Richards, Anthony C. Gamst, Kristin A. Persson, and Gerbrand Ceder. [10.1126/sciadv.1600225]
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds. Chemistry of Materials, 28 (20), 7324-7331, 2016. Anton O. Oliynyk, Erin Antono, Taylor D. Sparks, Leila Ghadbeigi, Michael W. Gaultois, Bryce Meredig, and Arthur Mar. [10.1021/acs.chemmater.6b02724]
Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis. Nature Communications 7, 11962,2016. Hinuma, Y., Hatakeyama, T., Kumagai, Y. et al. [doi.org/10.1038/ncomms11962]
Computationally-Guided Synthesis of the 8-Ring Zeolite AEI. Topics in Catalysis volume 58, pages410–415, 2015. Joel E. Schmidt, Michael W. Deem, Christopher Lew, and Tracy M. Davis . [10.1007/s11244-015-0381-1]
Perspective: Toward “synthesis by design”: Exploring atomic correlations during inorganic materials synthesis. APL Materials 4, 053212, 2016. *L. Soderhol and J. F. Mitchell. [doi.org/10.1063/1.4952712]
The Synthesizability of Texture Examples. Computer Vision Lab, ETH Zurich, 2014. Dengxin Dai, Hayko Riemenschneider, and Luc Van Gool. [0.13140/2.1.2685.6325]
Computational prediction of chemically synthesizable organic structure directing agents for zeolites. Journals of Materials Chemistry A, Issue 23, 2013. Ramdas Pophale, Frits Daeyaertb, and Michael W. Deem. [10.1039/C3TA10626H]
SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Scientific Data 7, Article number: 384 (2020). Hitesh Patel, Wolf-Dietrich Ihlenfeldt, Philip N. Judson, Yurii S. Moroz, Yuri Pevzner, Megan L. Peach, Victorien Delannée, Nadya Tarasova, and Marc C. Nicklaus. [doi.org/10.1038/s41597-020-00727-4]
Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical Science, Advanced Article, 2020. Amol Thakkar, Veronika Chadimova, Esben Jannik Bjerrum, Ola Engkvist, and Jean-Louis Reymond. [10.1039/d0sc05401a]
Artificial applicability labels for improving policies in retrosynthesis prediction. Machine Learning: Science Technology 2 017001, Dec 2020. Esben Jannik Bjerrum, Amol Thakkar1, and Ola Engkvist. [10.1088/2632-2153/abcf90]
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist. arXiv:2011.02893, Nov 2020. Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, and Junzhou Huang. [2011.02893.pdf]
Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level. Molecules. 25(10):2357, May 2020. Renren Bai, Chengyun Zhang, Ling Wang, Chuansheng Yao, Jiamin Ge, and Hongliang Duan. [10.3390/molecules25102357]
A Graph to Graphs Framework for Retrosynthesis Prediction. 37th ICML, Online, PMLR 119, March 2020. Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang. [2003.12725]
Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chemical Science 11, 3316, March 2020. Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu Nair, Rico Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, and Teodoro Laino. [10.1039/c9sc05704h]
Retrosynthesis Prediction with Conditional Graph Logic Network. arXiv:2001.01408 [cs.LG], Jan 2020. Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, and Le Song. [2001.01408.pdf]
A Model to Search for Synthesizable Molecules. arXivLabs, 1906.05221, Dec 2019. John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel, and Hernández-Lobato. [1906.05221.pdf]
Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks. J Chem Inf Model, 59, 12, 5026–5033, 2019. Shoichi Ishida, Kei Terayama, Ryosuke Kojima, Kiyosei Takasu, and Yasushi Okuno. [10.1021/acs.jcim.9b00538]
Retrosynthetic Reaction Prediction Using Neural Sequence-toSequence Models. ACS Cent. Sci. 2017, 3, 10, 1103–1113, September 2017. Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender,and Vijay Pande. [10.1021/acscentsci.7b00303]
Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. Chemistry A European Journal, January 2017. Marwin Segler and Prof. Mark P. Waller. [10.1002/chem.201605499]
A Short Review of Chemical Reaction Database Systems, Computer‐Aided Synthesis Design, Reaction Prediction and Synthetic Feasibility. Molecular Informatics, 2014. Wendy A. Warr. [10.1002/minf.201400052]
Substructure-based neural machine translation for retrosynthetic prediction. Journal of Cheminformatics 13, Article no. 4, Jan 2021. Umit V. Ucak1, Taek Kang2, Junsu Ko3, and Juyong Lee. [10.1186/s13321-020-00482-z]
RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades. Nature Catalysis, Jan 2021. William Finnigan, Lorna J. Hepworth, Sabine L. Flitsch, and Nicholas J. Turner. [10.1038/s41929-020-00556-z]
Artificial intelligence and automation in computer aided synthesis planning. React Chem. Eng 6, 27-51, 2021 Amol Thakkar, Simon Johansson, Kjell Jorner, David Buttar, Jean-Louis Reymond, and Ola Engkvist. [10.1039/D0RE00340A]
The Synthesizability of Molecules Proposed by Generative Models. Journal of Chemical Information and Modeling 60 (12), 5714-5723, 2020. Wenhao Gao and Connor W. Coley. [10.1021/acs.jcim.0c00174]
Computational planning of the synthesis of complex natural products. Nature 588, 83–88, Oct 2020. Barbara Mikulak-Klucznik, Patrycja Gołębiowska, Alison Bayly, Oskar Popik, Tomasz Klucznik, Sara Szymkuć, Ewa Gajewska, Piotr Dittwald, Olga Staszewska-Krajewska, Wiktor Beker, Tomasz Badowski, Karl Scheidt, Karol Molga, Jacek Mlynarski, Milan Mrksich, and Bartosz Grzybowski. [10.1038/s41586-020-2855-y]
Automatic retrosynthetic route planning using template-free models. Chem. Sci 11, 3355-3364, Mar 2020. Kangjie Lin, Youjun Xu, Jianfeng Pei, and Luhua Lai. [10.1039/c9sc03666k]
Learning Retrosynthetic Planning through Simulated Experience. ACS Cent Sci, 5, 6, 970–981, 2019. John S. Schreck, Connor W. Coley, and Kyle J. M. Bishop. [10.1021/acscentsci.9b00055]
Machine Learning in Computer-Aided Synthesis Planning. Acc Chem Res, 51, 5, 1281–1289, May 2018. Connor W. Coley, William H. Green, and Klavs F. Jensen. [10.1021/acs.accounts.8b00087]
Computer‐Assisted Synthetic Planning: The End of the Beginning. A Journal of the German Chemical Society, 2016. Sara Szymkuć, Ewa P. Gajewska, Tomasz Klucznik, Karol Molga, Dr. Piotr Dittwald, Dr. Michał Startek, Michał Bajczyk, and Prof. Dr. Bartosz A. Grzybowsk. [10.1002/anie.201506101]
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. Dept. of Materials Science and Engineering, Massachusetts Institute of Technology, 2019 Edward Kim, Zach Jensen, Alexander van Grootel, Kevin Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, and Elsa Olivetti. [1901.00032]
Unsupervised inference of protein fitness landscape from deep mutational scan.
Jorge Fernandez-de-Cossio-Diaz, Guido Uguzzoni, Andrea Pagnani.
Preprint, March 2020.
[10.1101/2020.03.18.996595]
Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset. Peishan Huang, Simon K. S. Chu, Henrique N. Frizzo, Morgan P. Connolly, Ryan W. Caster, and Justin B. Siegel [10.1021/acsomega.9b04105]
Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes.
Mirko Torrisi, Manaz Kaleel, Gianluca Pollastri.
Preprint, October 2018.
[10.1101/289033] [bioRxiv]