Machine Learning the Energetics of Electrified Solid-Liquid Interfaces

We present a response-augmented machine-learning (ML) approach to the energetics of electrified metal surfaces. We leverage local descriptors to learn the work function as the first-order energy change to introduced bias charges and stabilize this learning through Born effective charges. This permits the efficient extension of ML interatomic potential architectures to include finite bias effects up to second order. Application to OH at Cu(100) rationalizes the experimentally observed <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mrow><a:mi>p</a:mi><a:mi mathvariant="normal">H</a:mi></a:mrow></a:math> dependence of the preferred adsorption site in terms of a non-Nernstian charge-induced site switching.