Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning"
https://doi.org/10.5281/ZENODO.15809229
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.zipÖppnas i en ny tabb – 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.zipÖppnas i en ny tabb – 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.zipÖppnas i en ny tabb – experimental inelastic neutron scattering data
Gå till källa för data
Öppnas i en ny tabbhttps://doi.org/10.5281/ZENODO.15809229
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Chalmers tekniska högskola