Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces
Author:
Affiliation:
1. Department of Biosystems Engineering, The University of Arizona, 1177 E. Fourth St., Tucson, Arizona 85719, United States
2. ENSL, CNRS, Laboratoire de Chimie UMR 5182, 46 Allée d’Italie, F69364 Lyon, France
Funder
College of Art Agriculture and Life Science, University of Arizona
Publisher
American Chemical Society (ACS)
Subject
Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00740
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1. From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis
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