Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

Jordan Venderley, Krishnanand Mallayya, Michael Matty, Matthew Krogstad, Jacob Ruff, Geoff Pleiss, Varsha Kishore, David Mandrus, Daniel Phelan, Lekhanath Poudel, Andrew Gordon Wilson, Kilian Weinberger, Puspa Upreti, Michael Norman, Stephan Rosenkranz, Raymond Osborn, Eun Ah Kim

Research output: Contribution to journalArticlepeer-review


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 atmodern 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 datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures.We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (Cax Sr1-x )3Rh4Sn13, where a quantum critical point is observed as a function of Ca concentration.We apply X-TEC to XRD data on the pyrochlore metal, Cd2Re2O7, to investigate its two muchdebated structural phase transitions and uncover the Goldstone mode accompanying them.We demonstrate how unprecedented atomic-scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC-revealed selection rules that the Cd and Re displacements are approximately equal in amplitude but out of phase. This discovery reveals a previously unknown involvement of 5d2 Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on the fly.

Original languageEnglish (US)
Article numbere2109665119
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number24
StatePublished - Jun 14 2022


  • X-ray scattering
  • big data
  • machine learning

ASJC Scopus subject areas

  • General


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