Nonadiabatic reactive scattering of hydrogen on different surface facets of copper

TL;DR

This study develops machine learning models to simulate nonadiabatic hydrogen scattering on copper surfaces, revealing that electronic excitations have a minor effect compared to the potential energy surface shape.

Contribution

It introduces full-dimensional machine learning surrogate models of electronic friction tensors for reactive hydrogen scattering on copper surfaces, enabling accurate quantum-state-resolved simulations.

Findings

Electronic excitations weakly influence scattering dynamics.

Dissociative adsorption mainly depends on potential energy surface shape.

Machine learning models achieve high accuracy in simulating H₂ on copper.

Abstract

Dissociative chemisorption is a key process in hydrogen-metal surface chemistry, where nonadiabatic effects due to low-lying electron-hole-pair excitations may affect reaction outcomes. Molecular dynamics with electronic friction simulations can capture weak nonadiabatic effects at metal surfaces, but require as input energy landscapes and electronic friction tensors. Here, we present full-dimensional machine learning surrogate models of the electronic friction tensor to study reactive hydrogen chemistry at the low-index surface facets Cu(100), Cu(110), Cu(111), and Cu(211). We combine these surrogate models with machine learning interatomic potentials to simulate quantum-state-resolved H$_2$ reactive scattering on pristine copper surfaces. The predicted sticking coefficient and survival probabilities are in excellent agreement with experiment. Comparison between adiabatic and nonadiabatic simulations reveals that the influence of electron-hole pair excitations on the scattering dynamics is weak and that the probability for dissociative adsorption is dominated by the shape of the underlying potential energy surface and the initial vibrational quantum state. Nonadiabatic effects only lead to subtle changes in rovibrationally inelastic state-to-state scattering probabilities. The differences between jellium-based isotropic local density friction and full ab-initio response-theory-based orbital-dependent friction are even more subtle. The presented machine learning models represent the most accurate publicly available, full-dimensional models for H$_2$ on copper to date.