Machine Learning the Energetics of Electrified Solid-Liquid Interfaces

作者：Nicolas Bergmann, Nicéphore Bonnet, Nicola Marzari, Karsten Reuter, Nicolas G. Hörmann · 发表于：Physical Review Letters · 年份：2025 · DOI：10.1103/lm64-m3bn · 被引用次数：10 · 研究领域：Machine Learning in Materials Science、Electrochemical Analysis and Applications、Advanced Memory and Neural Computing

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.