Machine Learning the Energetics of Electrified Solid/Liquid Interfaces
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909623187931136 |
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| author | Bergmann, Nicolas Bonnet, Nicéphore Marzari, Nicola Reuter, Karsten Hörmann, Nicolas G. |
| author_facet | Bergmann, Nicolas Bonnet, Nicéphore Marzari, Nicola Reuter, Karsten Hörmann, Nicolas G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19745 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning the Energetics of Electrified Solid/Liquid Interfaces Bergmann, Nicolas Bonnet, Nicéphore Marzari, Nicola Reuter, Karsten Hörmann, Nicolas G. Materials Science 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. |
| title | Machine Learning the Energetics of Electrified Solid/Liquid Interfaces |
| topic | Materials Science |
| url | https://arxiv.org/abs/2505.19745 |