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

Journal Article · 09 June 2022 · Proceedings of the National Academy of Sciences of the United States of America

DOI: https://doi.org/10.1073/pnas.2109665119 · OSTI ID:1905312

Venderley, Jordan ^[1]; Mallayya, Krishnanand  ^[1]; Matty, Michael ^[1]; Krogstad, Matthew ^[2]; Ruff, Jacob ^[1]; Pleiss, Geoff ^[1]; Kishore, Varsha ^[1]; Mandrus, David ^[3]; Phelan, Daniel ^[2]; Poudel, Lekhanath  ^[4]; Wilson, Andrew Gordon ^[5]; Weinberger, Kilian ^[1]; Upreti, Puspa ^[6]; Norman, Michael  ^[2]; Rosenkranz, Stephan  ^[2]; Osborn, Raymond  ^[2]; Kim, Eun-Ah  ^[1]

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1. Cornell Univ., Ithaca, NY (United States)
2. Argonne National Lab. (ANL), Lemont, IL (United States). Materials Science Division
3. Univ. of Tennessee, Knoxville, TN (United States)
4. Univ. of Maryland, College Park, MD (United States); National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
5. New York Univ. (NYU), NY (United States)
6. Argonne National Lab. (ANL), Lemont, IL (United States). Materials Science Division; Northern Illinois Univ., DeKalb, IL (United States)

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 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 Sr 1 − x ) 3 Rh 4 Sn 13 , where a quantum critical point is observed as a function of Ca concentration. We apply X-TEC to XRD data on the pyrochlore metal, Cd 2 Re 2 O 7 , 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 5 d 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.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States); Cornell Univ., Ithaca, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
• Grant/Contract Number: AC02-06CH11357; SC0018946; OAC-1934714; DMR-1719875; DMR-1332208; DMR-1829070
• OSTI ID: 1905312
• Alternate ID(s): OSTI ID: 2322542
• Journal Information: Proceedings of the National Academy of Sciences of the United States of America, Vol. 119, Issue 24; ISSN 0027-8424
• Publisher: National Academy of SciencesCopyright Statement
• Country of Publication: United States
• Language: English