DMCP:DFT-based Machine learning method for Catalysis Program

DMCP is aimed to implement DFT-based and Machine-learning-accelerated (DFT-ML) scheme for intricate system catalysis. It is possible to predict the catalytic performance of intricate system catalysis such as double-atom catalysts (DACs) and to reveal the intrinsic descriptors which determine the underlying catalytic activity of them with appropriate algorithm and train data features.

Developer:

DMCP is developed within Prof. Yuzheng Guo's group in Wuhan University, in colloboration with Dr. Zhaofu Zhang in Cambridge University. Core developer: Xuhao Wan, Zhaofu Zhang, Wei Yu, and Yuzheng Guo Email: xhwanrm@whu.edu.cn, yguo@whu.edu.cn, zz389@cam.ac.uk

Major Features

1. Ten machine learning algorithms: GBR, KNR, SVR, GPR, FNN, RFR, ETR, KRR, LASSO, and ENR.
2. Multiple methods to improve model accuracy: dataset split, cross validation, repeated trails.
3. Visualization module for research.

Prerequisites

1. Generally, you need some data obtained from DFT calculations such as VASP, QE, and CP2K or available material database.
2. DMCP requires Python 3 with the packages specified in requirements.txt. This is taken care of by pip.

Citation

If you use DMCP in your research, please cite the following paper:

1. X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. doi.org/10.1016/j.matre.2021.100046.

Reference

The work applied DMCP are listed as following:

1. Dou B, Zhu Z, Merkurjev E, et al. Machine learning methods for small data challenges in molecular science. Chemical Reviews, 2023, 123(13): 8736-8780.
2. Tamtaji M, Gao H, Hossain M D, et al. Machine learning for design principles for single atom catalysts towards electrochemical reactions. Journal of Materials Chemistry A, 2022, 10(29): 15309-15331.
3. Liu X, Zhang Y, Wang W, et al. Transition metal and N doping on AlP monolayers for bifunctional oxygen electrocatalysts: density functional theory study assisted by machine learning description. ACS Applied Materials & Interfaces, 2021, 14(1): 1249-1259.
4. Huang Y, Rehman F, Tamtaji M, et al. Mechanistic understanding and design of non-noble metal-based single-atom catalysts supported on two-dimensional materials for CO 2 electroreduction. Journal of Materials Chemistry A, 2022, 10(11): 5813-5834.
5. Liu T, Zhao X, Liu X, et al. Understanding the hydrogen evolution reaction activity of doped single-atom catalysts on two-dimensional GaPS4 by DFT and machine learning. Journal of Energy Chemistry, 2023, 81: 93-100.
6. X. Wan, Z. Zhang*, H. Niu, Y. Yin, C. Kuai, J. Wang, C. Shao, Y. Guo*, Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Sites Catalysts for CO2 Reduction. The Journal of Physical Chemistry Letters, 2021.
7. H. Niu#, X. Wan#, X. Wang, C. Chen, J. Robertson, Z. Zhang*, Y. Guo*, Single-Atom Rhodium on Defective g-C3N4: A Promising Bifunctional Oxygen Electrocatalyst. ACS Sustainable Chem. Eng., 9(9), 3590-3599, 2021.
8. Wan X, Yu W, Niu H, ea al. Revealing the Oxygen Reduction/Evoluti on Reaction Activity Origin of Carbon-Nitride-Related Single-Atom catalysts: Quantum Chemistry in Artificial Intelligence. Chemical Engineering Journal. 2022，440:135946.
9. Khrabrov K, Shenbin I, Ryabov A, et al. nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset. Physical Chemistry Chemical Physics, 2022, 24(42): 25853-25863.
10. Pant D, Pokharel S, Mandal S, et al. DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides. Scientific Reports, 2023, 13(1): 3277.

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