Long-Lived Hot Electron in a Metallic Particle for Plasmonics and Catalysis: <i>Ab Initio</i> Nonadiabatic Molecular Dynamics with Machine Learning.

Abstract

Multiple experiments provide evidence for photovoltaic, catalytic, optoelectronic, and plasmonic processes involving hot, <i>i</i>.<i>e</i>., high energy, electrons in nanoscale materials. However, the mechanisms of such processes remain elusive, because electrons rapidly lose energy by relaxation through dense manifolds of states. We demonstrate a long-lived hot electron state in a Pt nanocluster adsorbed on the MoS₂ substrate. For this purpose, we develop a simulation technique, combining classical molecular dynamics based on machine learning potentials with <i>ab initio</i> nonadiabatic molecular dynamics and real-time time-dependent density functional theory. Choosing Pt₂₀/MoS₂ as a prototypical system, we find frequent shifting of a top atom in the Pt particle occurring on a 50 ps time scale. The distortion breaks particle symmetry and creates unsaturated chemical bonds. The lifetime of the localized state associated with the broken bonds is enhanced by a factor of 3. Hot electrons aggregate near the shifted atom and form a catalytic reaction center. Our findings prove that distortion of even a single atom can have important implications for nanoscale catalysis and plasmonics and provide insights for utilizing machine learning potentials to accelerate <i>ab initio</i> investigations of excited state dynamics in condensed matter systems.