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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 40: Focus Session: Novel Approaches to Surface Plasmon Generated Charges and Heat for Photocatalysis II (joint session O/CPP)

CPP 40.8: Talk

Thursday, March 21, 2024, 12:30–12:45, MA 141

Light-driven hydrogen evolution at copper surfaces: machine-learning accelerated nonadiabatic dynamics light the way — •Alexander Spears, Wojciech Stark, and Reinhard J. Maurer — University of Warwick, Coventry, United Kingdom

Light-induced ultrafast dynamics entail the energy transfer between light, electrons, and phonons at interfaces. Whether or not this energy transfer can be harnessed to selectively drive photocatalytic processes remains an open question. Machine-learning interatomic potentials based on electronic structure theory enable the efficient statistical sampling of chemical dynamics at metal surfaces. Molecular dynamics with electronic friction (MDEF) provide a fully anharmonic mixed quantum-classical description of electron-phonon coupling, whereby electronic friction is usually approximated with a free electron gas model. This method has previously been used to model state-resolved scattering experiments or in the calculation of vibrational lifetimes after femtosecond laser pulses, though its validity has yet to be fully assessed. Herein, we present ML-enabled MDEF simulations of thermal and light-driven hydrogen recombination and desorption probabilities from various copper surface facets. We compare results based on electronic friction based on first-order response theory and the free electron gas model and analyse the ability of different approximations to accurately describe the non-adiabatic energy transfer between the surface and adsorbates. Our approach also allows us to study the role of quantum nuclear effects on the dynamics of light-driven associative desorption.

Keywords: electronic friction; nonadiabatic dynamics; photocatalysis; machine learning; plasmonic catalysis