Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning"

This record accompanies the publication "Predicting neutron experiments from first principles: A workflow powered by machine learning". It comprises the machine-learned interatomic potentials (MLIPs) constructed and employed in that work with their respective training data as well as the experimental inelastic neutron scattering data for crystalline benzene presented in the publication. Hydrogenated Sc-doped BaTiO3 nep-BaScTiOH.txt – MLIP based on the neuroevolution potential (NEP) form nep-BaScTiOH.zipOpens in a new tab – model ensemble with the underlying training and validation data BaScTiOH-R2SCAN.db – database with reference data, in sql-lite format, readable using the ase package Benzene nep-benzene.txt – MLIP based on the neuroevolution potential (NEP) form nep-benzene.zipOpens in a new tab – model ensemble with the underlying training and validation data benzene-CX.db – database with reference data, in sql-lite format, readable using the ase package reduced-benzene-tosca.zipOpens in a new tab – experimental inelastic neutron scattering data