Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields

TL;DR

This paper presents an automatic training protocol for machine learning force fields that accurately predicts energy barriers in catalytic reactions, validated on CO2 hydrogenation, with significant speedups and improved understanding of catalyst behavior.

Contribution

The study introduces a novel, automated protocol for developing ML force fields that accurately determine energy barriers in catalytic pathways, including finite-temperature effects and transferability.

Findings

Energy barriers within 0.05 eV of DFT

40% reduction in rate-limiting step

Validated transferability on new catalyst surface

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.