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Acceleration of Catalyst Discovery with Easy, Fast, and Reproducible Computational Alchemy    
  • Charles Griego,
  • John R. Kitchin,
  • John A. Keith
Charles Griego
University of Pittsburgh
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John R. Kitchin
Carnegie Mellon University (CMU)
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John A. Keith
University of Pittsburgh
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The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme called alchemical perturbation density functional theory (APDFT) for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on Kohn-Sham density functional theory reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present bench marking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are done, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.
Keywords --- Computational catalysis, density functional theory (DFT), adsorption energies, nudged elastic band calculations, binding energies, barrier heights 

Peer review status:Published

21 Jan 2020Submitted to IJQC Special Issue
21 Jan 2020Reviewer(s) Assigned
15 Apr 2020Review(s) Completed, Editorial Evaluation Pending
24 Apr 20201st Revision Received
15 Jun 2020Editorial Decision: Accept
09 Jul 2020Published in International Journal of Quantum Chemistry. 10.1002/qua.26380
Mohammad Atif Faiz Afzal posted a review
Referee ReportThe manuscript at hand reports an overview of the computational alchemy approach to evaluate catalysts and provides a standard procedure to perform such computations for specific applications. The paper does not present any new research, but rather outlines the computational alchemy method and provides an open-source and user-friendly tool to the community for them to efficiently perform their own catalyst search. Computational alchemy is an approximate method that allows us to obtain large data from a single DFT calculation, thus allowing us to
Anonymous IJQC Reviewer posted a review
Using computational alchemy, the authors demonstrate how catalyst discovery can be made faster and readily accessible. Starting from electronic structure results on one system, they give predictions for barriers and binding energies for variants of that system. This cashes in on the promise that computational alchemy allows for systematical screening of chemical spaces in pursuit of a design application. Their work is written in an hands-on explicit style including clear figures which render the work accessible to the broad audience for which it is
John A. Keith and 2 more posted a review
Reviewer 1's original text is given in bold while our responses are in plain text. 1. My main question is whether the authors have seen any kind of finite-size effect on the alchemical potential due to the minimal cell employed in this study. Due to the coulombic form of the alchemical potential, it appears unlikely that there is no such effect, but potentially it is a quite systematic contribution for all target systems considered. While the errors might be attributed as due to finite-size effects, our