Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning"
type: Dataset
distribution: Landing page for the dataset
description: 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
modified: 2026-03-03T00:00:00Z
issued: 2025-07-04T00:00:00Z
publisher: Chalmers University of Technology
access rights: PUBLIC
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Lindgren, Eric
    type: Agent
    account: https://orcid.org/0000-0002-8549-6839
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Jackson, Adam J.
    type: Agent
    account: https://orcid.org/0000-0001-5272-6530
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Fransson, Erik
    type: Agent
    account: https://orcid.org/0000-0001-5262-3339
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Berger, Esmée
    type: Agent
    account: https://orcid.org/0000-0002-5282-7726
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Rudic, Svemir
    type: Agent
    account: https://orcid.org/0000-0003-3023-8565
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Skoro, Goran
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Turanyi, Rastislav
    type: Agent
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Mukhopadhyay, Sanghamitra
    type: Agent
    account: https://orcid.org/0000-0002-6501-4089
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Erhart, Paul
    type: Agent
    account: https://orcid.org/0000-0002-2516-6061
    type: PrivateIndividual(s)
keyword: Neutron Diffraction
keyword: Neutron scattering
keyword: Inelastic neutron scattering
keyword: Machine Learning
keyword: Machine learned interatomic potential
keyword: Molecular dynamics
keyword: Molecular Dynamics Simulation

Landing page for the dataset
type: Distribution
title: Landningssida för datasetet @sv
access URL: Models and data supporting the paper "Predicting neutron experiments from first principles: A workflow powered by machine learning"
availability: stable

Chalmers University of Technology
type: Agent
name: Chalmers tekniska högskola @sv
type: Academia-ScientificOrganisation
source: 040wg7k59