pub004 - Computationally guided searches for efficient catalysts through chemical/materials space: Progress and outlook

Authors: Charles D Griego, Alex M. Maldonado, Lingyan Zhao, Barbaro Zulueta, Brian M Gentry, Eli Lipsman, Tae Hoon Choi, John A Keith

Journal: Journal of Physical Chemistry C

Citations: 7

DOI: 10.1021/acs.jpcc.0c11345

Abstract

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Computational quantum chemistry promises to help guide the design of catalysts that are more sustainable and economical. This Feature Article gives a tutorial overview of how our group accounts for the thermodynamics and kinetics of chemical reactions in complex environments. We start with explanations of how to include environmental contributions when modeling homogeneous and heterogeneous catalytic processes. We also provide examples of schemes that use machine learning and alchemical perturbation density functional theory that eschew high computational costs while providing useful insights into chemical reaction mechanisms. With this toolbox of computational methods, we highlight progress in understanding how to reliably model renewable energy catalysis reaction mechanisms that occur in complex environments.

Citation

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@article{griego2021computationally,
  title={Computationally guided searches for efficient catalysts through chemical/materials space: Progress and outlook},
  author={Griego, Charles D and Maldonado, Alex M and Zhao, Lingyan and Zulueta, Barbaro and Gentry, Brian M and Lipsman, Eli and Choi, Tae Hoon and Keith, John A},
  journal={J. Phys. Chem. C},
  volume={125},
  number={12},
  pages={6495--6507},
  year={2021},
  publisher={ACS Publications},
  doi={10.1021/acs.jpcc.0c11345}
}