3 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.stat-mech2026
Divergent Fluctuations from a 2D Infrared Catastrophe
Richard G. Hennig, Clotilde S. Cucinotta
Molecular simulations of interfacial polar media routinely employ periodic boundary conditions parallel to the interface. We show that this lateral periodicity introduces a spatial…
cond-mat.mtrl-sci2025
Machine-Learned Interatomic Potential for Predictive Simulation of MoS2 Epitaxy
Emir Bilgili, Nicholas Taormina, Richard Hennig +2
A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultra-fast force field (UF3) framework. The UF3 MLIP reproduces key properties in strong…