2 papers
cond-mat.mtrl-sci2024
Benchmarking phonon anharmonicity in machine learning interatomic potentials
Sasaank Bandi, Chao Jiang, Chris A. Marianetti
Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, Machine learning interatomic…
cond-mat.mtrl-sci2024
Phonon thermal transport in UO via self-consistent perturbation theory
Shuxiang Zhou, Enda Xiao, Hao Ma +5
Computing thermal transport from first-principles in UO is complicated due to the challenges associated with Mott physics. Here we use irreducible derivative approaches to comp…