Machine learning potentials for electrified interfaces

RSC Applied Interfaces 2026 Open access

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Authors: Adem R.N. Aouichaoui, Davide Tisi, Ivano E. Castelli, Arghya Bhowmik
DOI: 10.1039/d6lf00149a
Cited by: 1
References: 118

Abstract

Embedding the applied potential into machine-learning surrogates and reusing abundant zero-potential data offers a data-efficient and scalable route to simulating electrochemical interfaces under bias.

Fields of Study & Subjects

Machine Learning in Materials Science Electrocatalysts for Energy Conversion CO2 Reduction Techniques and Catalysts

Publication details

Journal / source
RSC Applied Interfaces
Publisher
Royal Society of Chemistry (RSC)
Publication date
1 January 2026
Language
EN