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Machine Learning the Energetics of Electrified Solid-Liquid Interfaces

Nicolas Bergmann1, Nicéphore Bonnet2, Nicola Marzari2, Karsten Reuter1, and Nicolas G. Hörmann1,*

  • *Contact author: hoermann@fhi.mpg.de
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Phys. Rev. Lett. 135, 146201 – Published 29 September, 2025

DOI: https://doi.org/10.1103/lm64-m3bn

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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Phys. Rev. Lett. 135, 146201– Published 29 September, 2025
  • Received 13 May 2025
  • Revised 9 August 2025
  • Accepted 26 August 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society.

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