Phystone

A Python package for computational alchemy, a tool that allows high-throughput screening of heterogeneous catalysts.

Installation

pip install git+https://github.com/KeithLabPitt/comp_alchemy.git

Examples

Interact with example notebooks:

Binding Energy Predictions: H on Pt

Machine Learning Corrections

References

1. Charles. D. Griego, Lingyan. Zhao, Karthikeyan. Saravanan, John. A. Keith, Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis. ChemRxiv. 2020. DOI: https://doi.org/10.26434/chemrxiv.12251462.v1

   Data available: https://github.com/keithgroup/comp_alchemy/tree/master/example-notebooks/Machine-learning-corrections

2. Charles D. Griego, John R. Kitchin, John A. Keith. Acceleration of Catalyst Discovery with Easy, Fast, and Reproducible Computational Alchemy. International Journal of Quantum Chemistry, 2020. DOI: https://doi.org/10.1002/qua.26380

   Data available: https://github.com/chaszg/phystone/tree/master/example-notebooks

3. Charles D. Griego, Karthikeyan Saravanan, John A. Keith. Benchmarking Computational Alchemy for Carbide Nitride, and Oxide Catalysts. Advanced Theory and Simulations 2, 1800142 Wiley, 2018. DOI: https://doi.org/10.1002/adts.201800142
4. Karthikeyan Saravanan, John R. Kitchin, O. Anatole von Lilienfeld, John A. Keith. Alchemical Predictions for Computational Catalysis: Potential and Limitations. The Journal of Physical Chemistry Letters 8, 5002–5007 American Chemical Society (ACS), 2017. DOI: https://doi.org/10.1021/acs.jpclett.7b01974

Copyright

Copyright (c) 2019, Chasz Griego

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.