Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning

TL;DR

This paper introduces a machine learning framework using variational autoencoders to uncover obscured phonon dynamics from powder inelastic neutron scattering spectra, overcoming traditional analysis limitations and enabling better understanding of material properties.

Contribution

The study presents a novel ML approach that extracts phonon information from powder spectra, effective on experimental data, and adaptable through fine-tuning, advancing phonon analysis techniques.

Findings

Successfully extracted force constants for phonon dispersion reconstruction.

Model trained on simulations generalizes well to experimental data.

Two-stage framework facilitates development of universal feature extractors.

Abstract

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model's versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework's two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.