4.8 Article

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

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.2109665119

Keywords

machine learning; big data; X-ray scattering

Funding

  1. US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Division of Material Sciences and Engineering
  2. NSF HDR-DIRSE (Harnessing Data Revolution Data Intensive Research in Science and Education) [OAC-1934714]
  3. US DOE, Office of Basic Energy Sciences, Division of Materials Science and Engineering [DE-SC0018946]
  4. NSF (Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials) [DMR-1539918]
  5. Cornell Center for Materials Research
  6. NSF MRSEC (Materials Research Science and Engineering Centers) program [DMR-1719875]
  7. DOE Office of Science [DE-AC02-06CH11357]
  8. NSF [DMR-1332208, DMR-1829070]
  9. U.S. Department of Energy (DOE) [DE-SC0018946] Funding Source: U.S. Department of Energy (DOE)

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Researchers have developed an unsupervised machine learning method that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from X-ray diffraction measurements. The method has been successfully applied to different materials and has provided valuable insights when connected to physical principles.
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, (Ca-x Sr1-x)(3)Rh4Sn13, 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 much-debated 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 5d(2) 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.

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