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Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction
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 frac...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214512/ https://www.ncbi.nlm.nih.gov/pubmed/35679347 http://dx.doi.org/10.1073/pnas.2109665119 |
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author | 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 |
author_facet | 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 |
author_sort | Venderley, Jordan |
collection | PubMed |
description | 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 [Formula: see text])(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 [Formula: see text] 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. |
format | Online Article Text |
id | pubmed-9214512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-92145122022-06-23 Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction 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 Proc Natl Acad Sci U S A Physical Sciences 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 [Formula: see text])(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 [Formula: see text] 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. National Academy of Sciences 2022-06-09 2022-06-14 /pmc/articles/PMC9214512/ /pubmed/35679347 http://dx.doi.org/10.1073/pnas.2109665119 Text en Copyright © 2022 the Author(s). Published by PNAS https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Physical Sciences 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 |
title | Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction |
title_full | Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction |
title_fullStr | Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction |
title_full_unstemmed | Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction |
title_short | Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction |
title_sort | harnessing interpretable and unsupervised machine learning to address big data from modern x-ray diffraction |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214512/ https://www.ncbi.nlm.nih.gov/pubmed/35679347 http://dx.doi.org/10.1073/pnas.2109665119 |
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