Predicting Chemical Reaction Barriers with a Machine Learning Model

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In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. Graphical Abstract: [Figure not available: see fulltext.].

Original languageEnglish
JournalCatalysis Letters
Volume149
Issue number9
Pages (from-to)2347-2354
Number of pages8
ISSN1011-372X
DOIs
Publication statusPublished - 2019
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Density functional theory, Kinetic modeling, Machine learning

ID: 187389055