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Title: Predicting Chemical Reaction Barriers with a Machine Learning Model

Abstract

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.

Authors:
 [1];  [1];  [1];  [2]
  1. Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  2. Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1546783
Grant/Contract Number:  
AC02-76SF00515
Resource Type:
Accepted Manuscript
Journal Name:
Catalysis Letters
Additional Journal Information:
Journal Volume: 149; Journal Issue: 9; Journal ID: ISSN 1011-372X
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Singh, Aayush R., Rohr, Brian A., Gauthier, Joseph A., and Nørskov, Jens K. Predicting Chemical Reaction Barriers with a Machine Learning Model. United States: N. p., 2019. Web. doi:10.1007/s10562-019-02705-x.
Singh, Aayush R., Rohr, Brian A., Gauthier, Joseph A., & Nørskov, Jens K. Predicting Chemical Reaction Barriers with a Machine Learning Model. United States. doi:10.1007/s10562-019-02705-x.
Singh, Aayush R., Rohr, Brian A., Gauthier, Joseph A., and Nørskov, Jens K. Sat . "Predicting Chemical Reaction Barriers with a Machine Learning Model". United States. doi:10.1007/s10562-019-02705-x.
@article{osti_1546783,
title = {Predicting Chemical Reaction Barriers with a Machine Learning Model},
author = {Singh, Aayush R. and Rohr, Brian A. and Gauthier, Joseph A. and Nørskov, Jens K.},
abstractNote = {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.},
doi = {10.1007/s10562-019-02705-x},
journal = {Catalysis Letters},
number = 9,
volume = 149,
place = {United States},
year = {2019},
month = {3}
}

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Works referenced in this record:

Soft self-consistent pseudopotentials in a generalized eigenvalue formalism
journal, April 1990


A climbing image nudged elastic band method for finding saddle points and minimum energy paths
journal, December 2000

  • Henkelman, Graeme; Uberuaga, Blas P.; Jónsson, Hannes
  • The Journal of Chemical Physics, Vol. 113, Issue 22, p. 9901-9904
  • DOI: 10.1063/1.1329672

Theoretical surface science and catalysis—calculations and concepts
book, January 2000