Automating the composition of ML interatomic potentials in Julia
Scaling up atomistic simulation models is hampered by expensive calculations of interatomic forces. Machine learning potentials address this challenge and promise the accuracy of first-principles methods at a lower computational cost. This talk presents, as part of the research activities of the CESMIX project, how Julia is used to facilitate automating the composition of a novel neural potential based on the Atomic Cluster Expansion.
