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

Abstract

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. Wemore » 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.« less

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [1];  [1];  [1];  [3];  [2]; ORCiD logo [4];  [5];  [1];  [6]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [1]
+ Show Author Affiliations
  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)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States); Cornell Univ., Ithaca, NY (United States)
Sponsoring Org.:
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
OSTI Identifier:
1905312
Alternate Identifier(s):
OSTI ID: 2322542
Grant/Contract Number:  
AC02-06CH11357; SC0018946; OAC-1934714; DMR-1719875; DMR-1332208; DMR-1829070
Resource Type:
Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 119; Journal Issue: 24; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; X-ray scattering; big data; machine learning

Citation Formats

Venderley, Jordan, Mallayya, Krishnanand, Matty, Michael, Krogstad, Matthew, Ruff, Jacob, Pleiss, Geoff, Kishore, Varsha, Mandrus, David, Phelan, Daniel, Poudel, Lekhanath, Wilson, Andrew Gordon, Weinberger, Kilian, Upreti, Puspa, Norman, Michael, Rosenkranz, Stephan, Osborn, Raymond, and Kim, Eun-Ah. Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction. United States: N. p., 2022. Web. doi:10.1073/pnas.2109665119.
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Venderley, Jordan, Mallayya, Krishnanand, Matty, Michael, Krogstad, Matthew, Ruff, Jacob, Pleiss, Geoff, Kishore, Varsha, Mandrus, David, Phelan, Daniel, Poudel, Lekhanath, Wilson, Andrew Gordon, Weinberger, Kilian, Upreti, Puspa, Norman, Michael, Rosenkranz, Stephan, Osborn, Raymond, & Kim, Eun-Ah. Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction. United States. https://doi.org/10.1073/pnas.2109665119
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Venderley, Jordan, Mallayya, Krishnanand, Matty, Michael, Krogstad, Matthew, Ruff, Jacob, Pleiss, Geoff, Kishore, Varsha, Mandrus, David, Phelan, Daniel, Poudel, Lekhanath, Wilson, Andrew Gordon, Weinberger, Kilian, Upreti, Puspa, Norman, Michael, Rosenkranz, Stephan, Osborn, Raymond, and Kim, Eun-Ah. Thu . "Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction". United States. https://doi.org/10.1073/pnas.2109665119. https://www.osti.gov/servlets/purl/1905312.
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@article{osti_1905312,
title = {Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction},
author = {Venderley, Jordan and Mallayya, Krishnanand and Matty, Michael and Krogstad, Matthew and Ruff, Jacob and Pleiss, Geoff and Kishore, Varsha and Mandrus, David and Phelan, Daniel and Poudel, Lekhanath and Wilson, Andrew Gordon and Weinberger, Kilian and Upreti, Puspa and Norman, Michael and Rosenkranz, Stephan and Osborn, Raymond and Kim, Eun-Ah},
abstractNote = {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.},
doi = {10.1073/pnas.2109665119},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 24,
volume = 119,
place = {United States},
year = {Thu Jun 09 00:00:00 EDT 2022},
month = {Thu Jun 09 00:00:00 EDT 2022}
}
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https://doi.org/10.1073/pnas.2109665119
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