A machine-learning and molecular-dynamics workflow predicts inelastic neutron scattering spectra that agree with measurements for silicon, benzene, and hydrogenated scandium-doped barium titanate.
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Predicting neutron experiments from first principles: A workflow powered by machine learning
A machine-learning and molecular-dynamics workflow predicts inelastic neutron scattering spectra that agree with measurements for silicon, benzene, and hydrogenated scandium-doped barium titanate.