Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Abstract

AbstractThe 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 the Kohn‐Sham density functional theory (DFT) 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 benchmarking 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 performed, 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.