X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

Abstract

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning

“…Deep-learning algorithms have recently gained increasing popularity for assisting XRD interpretation, as they can effectively extract the latent information that is often hard to manually capture from the diffraction patterns. − Early pioneering studies have primarily focused on the autonomous inference of structural attributes from the XRD patterns, including lattice parameters, space group, − and crystallographic dimensionality . These attributes can be fed to the traditional rule-based approaches to speed up the process of XRD analysis.…”

Interpretable structure-property correlation in X-ray diffraction patterns of HfZrO thin films via machine learning

“…[8][9][10][11] These patterns are considered a unique fingerprint for materials characterization, critical for the advancement of materials science. 12) For example, in HfZrO films, the XRD peak at 28.3°typically indicates a monoclinic phase, while a peak at 30.2°is attributed to the polar orthorhombic phase, and a peak at 34°suggests a monoclinic/tetragonal phase mix. 13) Historically, the analysis of XRD data has relied heavily on expert interpretation and manual phase identification, which can be both time-consuming and susceptible to human error.…”