2 papers
cond-mat.mtrl-sci2026
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
MikoÅaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
cond-mat.mtrl-sci2025
Efficient training of machine learning potentials for metallic glasses: CuZrAl validation
Antoni Wadowski, Anshul D. S. Parmar, Filip KaÅkosz +4
Interatomic potentials are key to uncovering microscopic structure-property relationships, essential for multiscale simulations and high-throughput experiments. For metallic glasse…