Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Göltl, Florian; Mavrikakis, Manos

doi:10.1002/cctc.202201108

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Journal Article · Fri Nov 18 00:00:00 EST 2022 · ChemCatChem

DOI:https://doi.org/10.1002/cctc.202201108· OSTI ID:1898916

Göltl, Florian ^[1]; Mavrikakis, Manos ^[2]

Department of Chemical and Biological Engineering University of Wisconsin – Madison 1415 Engineering Drive WI 53706 Madison USA, Current affiliation Department of Biosystems Engineering The University of Arizona 1177 E 4th Street AZ-postal code missing Tucson USA
Department of Chemical and Biological Engineering University of Wisconsin – Madison 1415 Engineering Drive WI 53706 Madison USA

Abstract

Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1898916

Alternate ID(s):: OSTI ID: 2422967
OSTI ID: 1898919

Journal Information:: ChemCatChem, Journal Name: ChemCatChem Journal Issue: 24 Vol. 14; ISSN 1867-3880

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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