Understanding High-Temperature Chemical Reactions on Metal Surfaces

TL;DR

This study uses machine learning-enhanced molecular dynamics to explore high-temperature surface reactions on copper, revealing effects like surface atom mobility and thermal expansion that influence catalytic processes near melting conditions.

Contribution

It introduces a novel approach combining machine learning potentials with molecular dynamics to analyze high-temperature surface reactions, uncovering effects not well understood before.

Findings

Surface atom mobility increases at high temperatures.

Thermal expansion of the substrate affects reaction pathways.

Local chemical environment influences reaction dynamics.

Abstract

Chemical reactions on metal surfaces are important in various processes such as heterogeneous catalysis and nanostructure growth. At moderate or lower temperatures, these reactions generally follow the minimum energy path and temperature effects can be reasonably described by a harmonic oscillator model. At a high temperature approaching the melting point of the substrate, general behaviors of surface reactions remain elusive. In this study, by taking hydrocarbon species adsorbed on Cu(111) as a model system and performing extensive molecular dynamics simulations powered by machine learning potentials, we identify several important high-temperature effects, including local chemical environment, surface atom mobility, and substrate thermal expansion. They affect different aspects of a high-temperature surface reaction in different ways. These results deepen our understanding of high-temperature reactions.