Alchemical Predictions for Computational Catalysis: Potential and Limitations
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 4107771
Author(s) Saravanan, Karthikeyan; Kitchin, John R.; von Lilienfeld, O. Anatole; Keith, John A.
Author(s) at UniBasel von Lilienfeld, Anatole
Year 2017
Title Alchemical Predictions for Computational Catalysis: Potential and Limitations
Journal Journal of Physical Chemistry Letters
Volume 8
Number 20
Pages / Article-Number 5002-5007
Abstract Kohn–Sham density functional theory (DFT) is the workhorse method for calculating adsorbate binding energies relevant for catalysis. Unfortunately, this method is too computationally expensive to methodically and broadly search through catalyst candidate space. Here, we assess the promise of computational alchemy, a perturbation theory approach that allows for predictions of binding energies thousands of times faster than DFT. We first benchmark the binding energy predictions of oxygen reduction reaction intermediates on alloys of Pt, Pd, and Ni using alchemy against predictions from DFT. Far faster alchemical estimates yield binding energies within 0.1 eV of DFT values in many cases. We also identify distinct cases where alchemy performs significantly worse, indicating areas where modeling improvements are needed. Our results suggest that computational alchemy is a very promising tool that warrants further consideration for high-throughput screening of heterogeneous catalysts.
Publisher American Chemical Society
ISSN/ISBN 1948-7185
Full Text on edoc No
Digital Object Identifier DOI 10.1021/acs.jpclett.7b01974
PubMed ID
ISI-Number WOS:000413798300006
Document type (ISI) Journal Article

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