Machine learning on neutron and x-ray scattering and spectroscopies

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 Page 2 of 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.

Prediction of the photoelectrochemical performance of hematite electrodes using analytical data

“…It was demonstrated that X-ray analytical data could be converted to a crystalline structure. 19 For example, XANES spectra were used to obtain the atomic arrangement. 20 This indicates that the analytical data could include the structural and physical features of materials.…”

Beyond structural insight: a deep neural network for the prediction of Pt L2/3-edge X-ray absorption spectra

“…This has led to the development of a number of approaches aimed at exploiting machine learning for data reduction, prediction, and/or analysis, in addition to accelerating theoretical calculations of electronically-excited states and spectra. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] In particular, and the focus of this Article, the X-ray regime provides valuable element-and site-specific information on the geometric, electronic, and spin structure of matter and, consequently, a number of recent works have addressed directly the development of models for the prediction and analysis of X-ray absorption spectra. These approaches can be subdivided into two categories: (i) ''forward'' (i.e.…”

Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions

“…The paper by [14] provides an overview of machine learning activities for neutron scattering at ORNL, which hosts the SNS. Similarly, machine learning applications for neutron and x-ray scattering and spectroscopy research were highlighted by [15]. Lastly, the recent review paper by [16] highlights the machine learning research on the broader field of nuclear physics, which includes a section on surrogate modeling with machine learning algorithms.…”