Work

Decoding diffraction and spectroscopy data with machine learning: A tutorial

Year: 2025

Type: article

Abstract: This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

Source: Journal of Applied Physics

DOI: 10.1063/5.0255593

Authors: D. Vizoso, R. Dingreville

Institutions: Sandia National Laboratories, Center for Integrated Nanotechnologies

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funder: Sandia National Laboratories