Issue 48, 2023

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Abstract

The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to identify physical and structural models and extract information efficiently and accurately from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis.

Graphical abstract: Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Article information

Article type
Perspective
Submitted
27 sen 2023
Accepted
20 noy 2023
First published
22 noy 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2023,14, 14003-14019

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

A. S. Anker, K. T. Butler, R. Selvan and K. M. Ø. Jensen, Chem. Sci., 2023, 14, 14003 DOI: 10.1039/D3SC05081E

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