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:

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

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+ Show Author Affiliations

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:

Sat Mar 16 00:00:00 EDT 2019

Research Org.:

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

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES)

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