[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121571-en":3,"doc-seo-121571-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121571,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning the Energetics of Electrified Solid-Liquid Interfaces","A response-augmented machine-learning approach models the energetics of electrified metal surfaces by learning the work function as a first-order energy change due to introduced bias charges, then stabilizing the learning using Born effective charges. This framework enables efficient extensions of ML interatomic potential architectures to finite bias effects up to second order. Applied to OH on Cu(100), it explains experimentally observed pH trends in preferred adsorption sites via a non-Nernstian charge-induced site switching mechanism.","Editors' Suggestion  \nMachine Learning the Energetics of Electrified Solid-Liquid Interfaces  \nNicolas Bergmann, 1 Nicphore Bonnet,2 Nicola Marzari,2 Karsten Reuter, 1 and Nicolas G. Hörmann1,*  \n1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany 2Theory and Simulation of Materials (THEOS), Ecole Polytechnique Fe´de´rale de Lausanne (EPFL), Lausanne 1015, Switzerland  (Received 13 May 2025; revised 9 August 2025; accepted 26 August 2025; published 29 September 2025)  \nWe 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.  \nDOI: 10. 1103/lm64-m3bn  \nPredictive-quality first-principles modeling and simulation has become indispensable for studying electrochemical interfaces. By accessing the detailed atomic structure and prevailing interactions, it provides deep mechanistic insight and generates ideas for the design of improved electrocatalysts [1–4] . Compared to its analog usage at solid-gas interfaces and thermal catalysis, first-principles modeling of electrified solid-liquid interfaces nevertheless still lags behind [5], largely because of difficulties to describe the extended double layer (DL) that builds up at biased conditions. Even within common approximations like implicit solvation [6–9], the description of complex and potential-dependent (near-surface) dynamics remains a challenging computational burden.  \nIn this respect, application ofmachine-learning interatomic potentials (MLIPs) [10–12] as fast surrogates to the firstprinciples calculations is highly appealing. MLIPs access longer time and larger length scales, a prerequisite to derive reliable macroscopic insights from the atomistic interfacial structures and reactions[13–16]. Notwithstanding, thenecessity to accurately capture the local fields that build up at the electrified interface prevents the straightforward usage of established short-range MLIP architectures [17]. To this end, an important strand of ongoing developments centers on incorporating long-range electrostatic interactions [18–25] . While this has led to a successful modeling of the atomistic structure of the interface at the potential of zero charge [26], there are still challenges at applied electrode potential  \n*Contact author: [hoermann@fhi.mpg.de](hoermann@fhi.mpg.de)  \nPublished 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.  \nconditions. For instance, underlying charge equilibration schemes can suffer from unphysical, partially metallic behavior of the electrolyte, leading to overpolarization in the presence of electric fields [27], while the need to solve for electrostatic potentials and atomic charges self-consistently generally increases the computational costs, thereby reducing the very efficiency gain sought with MLIPs. Explicit training of short-range MLIPs for different charge states in turn requires larger amounts of training data and is typically not size extensive [19,28] .  \nAs such, a complementary strand of developments appears promising: models incorporating the effects of electric fields by machine-learning relevant response effects. With previous works addressing isolated molecules [29–33], bulk solids [34–36], or liquid water [37–40], we here t","cbCaitviSnx788w8","https://ap.wps.com/l/cbCaitviSnx788w8","pdf",1942090,1,7,"English","en",105,"# Machine-learning approach for electrified interfaces\n## Learning work function from bias charges\n## RAZOR model and response analysis\n## Application to OH on Cu(100)\n## Explaining pH-dependent adsorption-site switching","[{\"question\":\"What is the core idea of the response-augmented machine-learning approach?\",\"answer\":\"It learns the work function as the first-order energy change caused by introduced bias charges and stabilizes the model using Born effective charges.\"},{\"question\":\"How does the method extend machine-learning interatomic potentials to electrified conditions?\",\"answer\":\"It provides an efficient way to include finite bias effects up to second order, enabling MLIP-based molecular dynamics under applied bias.\"},{\"question\":\"What does the OH on Cu(100) example demonstrate?\",\"answer\":\"It rationalizes the experimental pH dependence of the preferred adsorption site by showing a potential-dependent, non-Nernstian charge-induced switching behavior.\"}]","Machine Learning the Energetics of Electrified Solid-Liquid Interfaces | 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