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Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields

Accepted version
Peer-reviewed

Type

Article

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Abstract

In this study, we introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide. With the help of active learning, the final force field obtains energy barriers within 0.05 eV of Density Functional Theory. Thanks to the computational speedup, not only do we reduce the cost of routine in-silico catalytic tasks, but also find a 40% reduction in the previously established rate-limiting step. Furthermore, we illustrate the importance of finite-temperature effects and compute free energy barriers. The transferability of the protocol is demonstrated on the experimentally relevant, yet unexplored, top-layer reduced indium oxide surface. The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-intio simulations.

Description

Keywords

34 Chemical Sciences, 3407 Theoretical and Computational Chemistry, 7 Affordable and Clean Energy

Journal Title

npj Computational Materials

Conference Name

Journal ISSN

2057-3960
2057-3960

Volume Title

Publisher

Nature Portfolio
Sponsorship
EPSRC (2468419)
Engineering and Physical Sciences Research Council (EP/S024220/1)
Engineering and Physical Sciences Research Council (EP/X035891/1)
We are grateful for computational support from the UK national high-performance computing service, ARCHER2, for which access was obtained via the UKCP consortium and funded by EPSRC grant reference EP/P022065/1 and EP/X035891/1. Additionally, LS acknowledges support from the EPSRC Centre for Doctoral Training in Chemical Synthesis with grant reference EP/S024220/1 and corporate funding from BASF SE.
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