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.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
Neutron Diffraction
Neutron scattering
Inelastic neutron scattering
Machine Learning
Machine learned interatomic potential
Molecular dynamics
Molecular Dynamics Simulation
Landing page for the dataset
Landningssida för datasetet
Chalmers University of Technology
Chalmers tekniska högskola