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This work investigates the impact of anisotropic electronic friction on laser-driven hydrogen recombination on Cu(111) using machine-learning-accelerated simulations. They compare isotropic and anisotropic electronic friction models, finding that anisotropy significantly affects energy transfer rates and reaction probability fluence dependence. However, the final energy distributions of the desorbed H2 are primarily determined by the potential energy surface, not the friction model.
Anisotropic electronic friction dictates energy transfer rates in surface reactions, but the potential energy surface alone governs the final energy distribution of product molecules.
Ultrafast light-driven chemical dynamics at surfaces are governed by energy transfer from excited electrons to vibrational degrees of freedom. When this nonadiabatic energy transfer is anisotropic, it can lead to dynamical steering effects that affect reaction probabilities or non-thermal final energy distributions in molecules. Here, we use a machine-learning-enabled simulation framework to compare isotropic and anisotropic models of electronic friction during laser-driven hydrogen evolution on the (111) facet of copper. While anisotropic friction strongly determines the rate of energy transfer into the adsorbate and the fluence dependence of reaction probabilities, it has little effect on final translational, vibrational and rotational energy distributions as these are mainly governed by the potential energy landscape at the barrier.
