[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119381-en":3,"doc-seo-119381-105":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":4,"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},119381,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response","Understanding electrochemical interfaces at the microscopic level is crucial for clarifying key electrochemical processes in electrocatalysis, batteries, and corrosion. Ab initio simulations can provide insight but their high computational cost restricts practical, complex systems. Machine learning potentials offer acceleration, yet dielectric response in both conductors and insulators is difficult to treat consistently. This work introduces a hybrid ML-potential framework unifying local electronic polarization in electrolytes with non-local charge transfer in metal electrodes, validating against bell-shaped differential Helmholtz capacitance at Pt(111)/electrolyte and enabling dielectric profile analysis for new interface polarization insights.","arXiv :2407 . 17740v1 [physics .chem-ph] 25 Jul 2024  \nMachine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response  \nJia-Xin Zhu∗ ,† and Jun Cheng∗ ,†,‡,¶  \n†State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China ‡Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen 361005, China ¶Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China  \nE-mail: [jiaxinzhu@stu.xmu.edu.cn](jiaxinzhu@stu.xmu.edu.cn) ; [chengjun@xmu.edu.cn](chengjun@xmu.edu.cn)  \nAbstract  \nUnderstanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries and corrosion. While ab initio simulations have provided valuable insights into model systems, the high computational cost limits their use in tackling complex systems of relevance to practical applications. Machine learning potentials offer a solution, but their application in electrochemistry remains challenging due to the difficulty in treating the dielectric response of electronic conductors and insulators simultaneously. In this work, we propose a hybrid framework of machine learning potentials that is capable of simulating metal/electrolyte interfaces by unifying the interfacial dielectric response accounting for local electronic polarisation in electrolytes and non-local charge transfer in metal electrodes. We validate our method by reproducing the bell-shaped differential Helmholtz capacitance at the Pt(111)/electrolyte interface. Furthermore, we apply the machine  \nlearning potential to calculate the dielectric profile at the interface, providing new insights into electronic polarisation effects. Our work lays the foundation for atomistic modelling of complex, realistic electrochemical interfaces using machine learning potential at ab initio accuracy.  \n1 Introduction  \nIn modern society, electrochemistry plays an increasingly important role in many areas including material synthesis, 1,2 energy conversion, 3,4 and energy storage. 5 However, our understanding of the structures and activity of electrochemical interfaces, where the electrochemical reactions happen, is far short of expectation. 6 Even for platinum, one of the most important catalysts in electrochemistry, 7–9 the understanding of how the interfacial structures vary with electrode potentials and electrolyte compositions is still lacking, although it has been widely observed and currently under active investigations that the interfacial structures can be tuned by protons, ions and additives, thus affecting the activity. 10–14 Indeed, the electrochemical characterisations, such as the cyclic voltammograms 15–17 and the electrochemical impedance spectroscopy, 18,19 provide initial but valuable insights into interfacial electrochemistry. Development and application of the in-situ spectroscopic 20–22 and scanning probe microscopic 23,24 techniques make it possible to investigate the microscopic structures of interfaces. On the other hand, computation and simulation complement experimental measurements in recent decades, 3,10,25 helping interpret the spectra and elucidate the interfacial processes at the atomic level.  \nAlthough atomistic modelling can offer detailed microscopic information on electrochemical interfaces, it often faces a dilemma between accuracy and efficiency. For example, the hydrogen bonding network has been proposed to play an important role in the thermodynamics and the kinetics of the interfacial processes. 26–29 While the atomic-level picture of the hydrogen bonding network is difficult to probe in experiment, it can be obtained from molecular dynamics (MD) simulations. 27,30 Notably, electronic structures should be included  \nin simulation to describe the potential-dependent hydrogen bonding network accurately in many important systems. For instance, it h","cbCailNsccL4un87","https://ap.wps.com/l/cbCailNsccL4un87","pdf",2162472,1,36,"English","en",105,"# Introduction\n## Motivation and challenge in modeling electrochemical interfaces\n## Limits of ab initio simulations\n## Promise and difficulty of machine learning potentials\n## Focus on dielectric response and interface electroneutrality","[{\"question\":\"Why are electrochemical interfaces important to study at the microscopic level?\",\"answer\":\"Electrochemical reactions occur at interfaces, so microscopic understanding is essential for explaining processes in electrocatalysis, batteries, and corrosion.\"},{\"question\":\"What main limitation of ab initio simulations motivates the use of machine learning potentials?\",\"answer\":\"Ab initio calculations are computationally expensive, limiting accessible system sizes and timescales for realistic electrochemical conditions.\"},{\"question\":\"What specific difficulty does this work address for machine learning potentials in electrochemistry?\",\"answer\":\"It addresses how to treat the dielectric response of electronic conductors and insulators simultaneously within machine learning potentials.\"}]","Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response | 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are electrochemical interfaces important to study at the microscopic level?","Question",{"text":75,"@type":76},"Electrochemical reactions occur at interfaces, so microscopic understanding is essential for explaining processes in electrocatalysis, batteries, and corrosion.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What main limitation of ab initio simulations motivates the use of machine learning potentials?",{"text":80,"@type":76},"Ab initio calculations are computationally expensive, limiting accessible system sizes and timescales for realistic electrochemical conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"What specific difficulty does this work address for machine learning potentials in electrochemistry?",{"text":84,"@type":76},"It addresses how to treat the dielectric response of electronic conductors and insulators simultaneously within machine learning 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