2 papers
cond-mat.mtrl-sci2026
Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric
Barbara Sumić, Ria Vasdev, Sudheesh Kumar Ethirajan +8
Accurate treatment of long-range interactions in machine learning interatomic potentials (MLIPs) is essential for electrochemical simulations. However, aggregate energy and force e…
cond-mat.mtrl-sci2025
Machine Learning the Energetics of Electrified Solid/Liquid Interfaces
Nicolas Bergmann, Nicéphore Bonnet, Nicola Marzari +2
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 firs…