Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering

Aiden Sable; Bander Linjawi; Kyle Bradbury; Jordan M. Malof; Olivier Delaire

doi:10.1039/d6dd00008h

January 1, 2025Digital DiscoveryOpen Access

Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering

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Key Points

This research aims to utilize machine learning to extract interatomic force constants from inelastic neutron scattering data.
Applied machine learning algorithms to analyze inelastic neutron scattering results
Focused on mapping atomic vibrations in single-crystal materials
Compared computational results with experimental data
Successfully inverted interatomic force constants using machine learning techniques
Demonstrated strong correlation between predicted and experimental phonon dispersions
Highlighted improvements in material property predictions

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Implication

Machine learning uncovers atomic vibrations in materials, indicating new ways to analyze crystal properties.

Abstract

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals,...

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Cite This Study

Sable et al. (2025) studied this question.

synapsesocial.com/papers/69bb9247496e729e6297f7b7 https://doi.org/10.1039/d6dd00008h

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Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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