Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning
Abstract
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.
- Authors:
-
- 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
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1898916
- Alternate Identifier(s):
- OSTI ID: 1898919
- Resource Type:
- Published Article
- Journal Name:
- ChemCatChem
- Additional Journal Information:
- Journal Name: ChemCatChem Journal Volume: 14 Journal Issue: 24; Journal ID: ISSN 1867-3880
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- Germany
- Language:
- English
Citation Formats
Göltl, Florian, and Mavrikakis, Manos. Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning. Germany: N. p., 2022.
Web. doi:10.1002/cctc.202201108.
Göltl, Florian, & Mavrikakis, Manos. Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning. Germany. https://doi.org/10.1002/cctc.202201108
Göltl, Florian, and Mavrikakis, Manos. Fri .
"Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning". Germany. https://doi.org/10.1002/cctc.202201108.
@article{osti_1898916,
title = {Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning},
author = {Göltl, Florian and Mavrikakis, Manos},
abstractNote = {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.},
doi = {10.1002/cctc.202201108},
journal = {ChemCatChem},
number = 24,
volume = 14,
place = {Germany},
year = {Fri Nov 18 00:00:00 EST 2022},
month = {Fri Nov 18 00:00:00 EST 2022}
}
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