Predicting Chemical Reaction Barriers with a Machine Learning Model

Singh, Aayush R.; Rohr, Brian A.; Gauthier, Joseph A.; Nørskov, Jens K.

doi:10.1007/s10562-019-02705-x

Predicting Chemical Reaction Barriers with a Machine Learning Model

Journal Article · Sat Mar 16 00:00:00 EDT 2019 · Catalysis Letters

DOI:https://doi.org/10.1007/s10562-019-02705-x· OSTI ID:1546783

Singh, Aayush R. ^[1]; Rohr, Brian A. ^[1]; Gauthier, Joseph A. ^[1]; Nørskov, Jens K. ^[2]

Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark)

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. Here in our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1546783

Journal Information:: Catalysis Letters, Journal Name: Catalysis Letters Journal Issue: 9 Vol. 149; ISSN 1011-372X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Machine Learning in Catalysis, From Proposal to Practicing Yang, Wenhong; Fidelis, Timothy Tizhe; Sun, Wen-Hua ACS Omega, Vol. 5, Issue 1 https://doi.org/10.1021/acsomega.9b03673	journal	December 2019
Machine learning for the modeling of interfaces in energy storage and conversion materials Artrith, Nongnuch Journal of Physics: Energy, Vol. 1, Issue 3 https://doi.org/10.1088/2515-7655/ab2060	journal	July 2019

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