Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction

Understand

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account.

• In the past decade, improvements in source brightness and detector technology at modern x-ray facilities have allowed a dramatically increased fraction of this information to be captured.
• Now, the primary challenge is to understand and discover scientific principles from big data sets when a comprehensive analysis is beyond human reach.
• We report the development of a novel unsupervised machine learning approach, XRD Temperature Clustering (X-TEC), that can automatically extract charge density wave (CDW) order parameters and detect intra-unit cell (IUC) ordering and its fluctuations from a series of high-volume X-ray diffraction (XRD) measurements taken at multiple temperatures.