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cond-mat.mtrl-sci2026
Understanding compression-extension error asymmetry in universal machine-learning interatomic potentials
Hongwei Du, Dingyang Lv, Boyan Zhang +11
Universal machine-learning interatomic potentials are increasingly used across diverse materials, yet their reliability under directional deformation remains unclear. We compare co…
cond-mat.mtrl-sci2026
A Transferable Machine-Learned Potential Enabling Large-Scale Copper-Carbon Simulations from Equilibrium to Extreme Conditions
Feiyu Zhou, Wei Qiu, Yan Chen +4
Cu-C materials and interfaces are central to diamond thermal management, metallization, graphene growth and carbon-coating technologies, but atomistic simulations across these appl…