Machine Learning the Energetics of Electrified Solid/Liquid Interfaces

2025/05/26 by Nicolas Bergmann, Bergmann, Nicolas, Nicéphore Bonnet +7 · 2 citations
Materials Science · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2505.19745

openalex publication_date 2025/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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