Unified and transferable description of dynamics of H2 dissociative adsorption on multiple copper surfaces via machine learning

Abstract

Dynamics of gas-surface reactions is of fundamental importance to various interfacial problems. Accurate modeling of gas-surface reaction dynamics requires a globally accurate reactive potential energy surface (PES), typically specialized for one molecule–surface system with no transferability even from one to another surface. As a proof of concept, we report a novel machine learned PES for H₂ reactive scattering from multiple low-index copper surfaces. Trained with limited data, this PES enables a uniformly and chemically accurate description of dissociative adsorption of H₂/D₂ on Cu(111)/Cu(100)/Cu(110) and offers quantitative insights to the remarkable surface temperature effect. More impressively, this PES is also transferable to describe the dynamics of H₂ dissociation on Cu(211) without learning any data on that stepped surface, which can be further improved when adding only a small amount of points. Our work opens a new avenue for studying the dynamics of the structure or step density-sensitive gas-surface reactions relevant to heterogeneous catalysis.