Machine learning in X-ray diffraction for materials discovery and characterization

Machine learning (ML) is a promising analytical method for large high-throughput, in situ, and operando X-ray diffraction (XRD) datasets. However, ML methods are, by default, physics agnostic and must therefore be interpreted carefully. In this review, we survey how supervised ML methods are used to predict symmetries and phases in pure and mixed-composition materials, and we highlight challenges related to experimental artifacts and model interpretation. We also review recent uses of unsupervised ML methods in the extraction of patterns hidden in high-dimensional data, such as in in situ and microscopic studies. Finally, we discuss the importance of problem formulation, data transferability, and reporting, leveraging examples from the literature, and we provide various resources throughout to expedite the learning curve for readers new to XRD or ML. We advocate for greater scrutiny of ML methods and how they are reported in the literature, and we explain how to conduct data-driven research responsibly.