Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning" - Lindgren, Eric; Jackson, Adam J.; Fransson, Erik; Berger, Esmée; Rudic, Svemir; Skoro, Goran; Turanyi, Rastislav; Mukhopadhyay, Sanghamitra; Erhart, Paul (Chalmers University of Technology, 2025)
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
Keywords: Neutron Diffraction; Neutron scattering; Inelastic neutron scattering; Machine Learning; Machine learned interatomic potential; Molecular dynamics; Molecular Dynamics Simulation; Physical Sciences; Fysik; Computer Sciences; Datavetenskap (datalogi)