Machine Learning on Neutron and X-Ray Scattering

TL;DR

This paper reviews recent advances in applying machine learning to neutron and X-ray scattering techniques, highlighting how ML enhances data analysis, modeling, and interpretation in materials characterization.

Contribution

It provides a comprehensive overview of integrating machine learning into scattering workflows, addressing challenges like limited data, noise reduction, and complex system modeling.

Findings

ML improves spectral noise mitigation

ML enables modeling of complex materials with limited data

ML integration enhances scattering data interpretation

Abstract

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.