Machine Learning Potentials for Heterogeneous Catalysis

TL;DR

This paper reviews how machine learning potentials are revolutionizing heterogeneous catalysis simulations by providing ab initio accuracy at reduced computational costs, thus enabling more detailed atomic-level insights.

Contribution

It offers an overview of current machine learning potentials in catalysis and discusses future prospects for their application in the field.

Findings

MLPs enable large-scale, accurate simulations of catalytic systems.

Recent advances have significantly reduced computational costs.

MLPs are poised to transform catalysis research and design.

Abstract

The sustainable production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms at the atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions in operando, but in order to achieve a comprehensive understanding, additional information from computer simulations is indispensable in many cases. In particular, ab initio molecular dynamics (AIMD) has become an important tool to explicitly address the atomistic level structure, dynamics, and reactivity of interfacial systems, but the high computational costs limit applications to systems consisting of at most a few hundred atoms for simulation times of up to tens of picoseconds. Rapid advances in the development of modern machine learning potentials (MLP) now offer a new approach to bridge this gap, enabling simulations of complex catalytic reactions with ab initio accuracy at a small fraction of the computational costs. In this perspective, we provide an overview of the current state of the art of applying MLPs to systems relevant for heterogeneous catalysis along with a discussion of the prospects for the use of MLPs in catalysis science in the years to come.