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…
physics.chem-ph2025
Water structuring at stacked graphene interfaces unveiled by machine-learning molecular dynamics
Dianwei Hou, Yevhen Horbatenko, Stefan Ringe +1
The wettability of monolayer and multilayer graphene remains a topic of longstanding debate. Here, we combined first-principles molecular dynamics simulations accelerated with the…