RAZOR machine-learns the work function and Born charges of electrified interfaces, enabling bias-dependent molecular dynamics that predicts a pH-driven OH adsorption site switch on Cu(100).
Towards First-principles Electrochemistry
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abstract
Chemisorbed molecules at a fuel cell electrode are a very sensitive probe of the surrounding electrochemical environment, and one that can be accurately monitored with different spectroscopic techniques. We develop a comprehensive electrochemical model to study molecular chemisorption at either constant charge or fixed applied voltage, and calculate from first principles the voltage dependence of vibrational frequencies -- the vibrational Stark effect -- for CO adsorbed on close-packed platinum electrodes. The predicted vibrational Stark slopes are found to be in very good agreement with experimental electrochemical spectroscopy data, thereby resolving previous controversies in the quantitative interpretation of in-situ experiments and elucidating the relation between canonical and grand-canonicaldescriptions of vibrational surface phenomena.
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Machine Learning the Energetics of Electrified Solid/Liquid Interfaces
RAZOR machine-learns the work function and Born charges of electrified interfaces, enabling bias-dependent molecular dynamics that predicts a pH-driven OH adsorption site switch on Cu(100).