Machine Learning the Energetics of Electrified Solid-Liquid Interfaces
Nicolas Bergmann, Nicéphore Bonnet, Nicola Marzari, Karsten Reuter, Nicolas G. Hörmann
Abstract
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 pH dependence of the preferred adsorption site in terms of a non-Nernstian charge-induced site switching.
Topics & Concepts
EnergeticsLeverage (statistics)Computer scienceWork (physics)Machine learningArtificial intelligenceAdsorptionEnergy (signal processing)Function (biology)Potential energyWork functionStatistical physicsMaterials scienceInteratomic potentialExtension (predicate logic)Chemical physicsElectrostaticsNanotechnologyPhysicsMachine Learning in Materials ScienceElectrochemical Analysis and ApplicationsAdvanced Memory and Neural Computing