Predicting Chemical Reaction Barriers with a Machine Learning Model

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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Predicting Chemical Reaction Barriers with a Machine Learning Model. / Singh, Aayush R.; Rohr, Brian A.; Gauthier, Joseph A.; Nørskov, Jens K.

In: Catalysis Letters, Vol. 149, No. 9, 2019, p. 2347-2354.

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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Singh, Aayush R. ; Rohr, Brian A. ; Gauthier, Joseph A. ; Nørskov, Jens K. / Predicting Chemical Reaction Barriers with a Machine Learning Model. In: Catalysis Letters. 2019 ; Vol. 149, No. 9. pp. 2347-2354.

Bibtex

@article{08dba7a351e847d9ae2e48ff6260250f,
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. 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.].",
keywords = "Density functional theory, Kinetic modeling, Machine learning",
author = "Singh, {Aayush R.} and Rohr, {Brian A.} and Gauthier, {Joseph A.} and N{\o}rskov, {Jens K.}",
year = "2019",
doi = "10.1007/s10562-019-02705-x",
language = "English",
volume = "149",
pages = "2347--2354",
journal = "Catalysis Letters",
issn = "1011-372X",
publisher = "Springer New York",
number = "9",

}

RIS

TY - JOUR

T1 - Predicting Chemical Reaction Barriers with a Machine Learning Model

AU - Singh, Aayush R.

AU - Rohr, Brian A.

AU - Gauthier, Joseph A.

AU - Nørskov, Jens K.

PY - 2019

Y1 - 2019

N2 - 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.].

AB - 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.].

KW - Density functional theory

KW - Kinetic modeling

KW - Machine learning

U2 - 10.1007/s10562-019-02705-x

DO - 10.1007/s10562-019-02705-x

M3 - Journal article

VL - 149

SP - 2347

EP - 2354

JO - Catalysis Letters

JF - Catalysis Letters

SN - 1011-372X

IS - 9

ER -