Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning" doi-10-5281-zenodo-15809229-0 https://doi.org/10.5281/zenodo.15809229 Swedish National Data Service Svensk nationell datatjänst Landing page Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning" doi-10-5281-zenodo-15809229-0 https://doi.org/10.5281/zenodo.15809229 Lindgren, Eric Jackson, Adam J. Fransson, Erik Berger, Esmée Rudic, Svemir Skoro, Goran Turanyi, Rastislav Mukhopadhyay, Sanghamitra Erhart, Paul Swedish National Data Service Svensk nationell datatjänst Landing page 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.zip – 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.zip – 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.zip – experimental inelastic neutron scattering data Access to data through an external actor. Data are freely accessible. Åtkomst till data via extern aktör. Data är fritt tillgängliga. openAccess