Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study
This study develops a machine-learned interatomic potential for cerium hydride using active learning to investigate how increasing hydrogen content (H/Ce ratios from 2.0 to 3.0) drives lattice contraction and densification through stronger binding induced by octahedral atoms, enabling the simulation of properties like melting and diffusion that are inaccessible to ab initio methods.