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Title: Machine learning in electronic-quantum-matter imaging experiments

Abstract

For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena. Today, however, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is to apply this approach to experimental data—for example, to the arrays of complex electronic-structure images17 obtained from atomic-scale visualization of EQM. Here we report the development and training of a suite of artificial neural networks (ANNs) designed to recognize different types of order hidden in such EQM image arrays. These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier-doped copper oxide Mott insulators. In these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs determine that this state is unidirectional, revealing a coincident nematic EQM state. Here, strong-coupling theories of electronic liquid crystals are consistent with these observations.

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [6];  [1]
  1. Cornell Univ., Ithaca, NY (United States)
  2. Cornell Univ., Ithaca, NY (United States); Univ. Paris-Sud, Orsay (France)
  3. Brookhaven National Lab. (BNL), Upton, NY (United States)
  4. Cornell Univ., Ithaca, NY (United States); Stanford Univ., Stanford, CA (United States)
  5. Cornell Univ., Ithaca, NY (United States); Harvard Univ., Cambridge, MA (United States)
  6. San Jose State Univ., San Jose, CA (United States)
  7. National Institute of Advanced Industrial Science and Technology, Tsukuba (Japan)
  8. National Institute of Advanced Industrial Science and Technology, Tsukuba (Japan); Univ. of Tokyo, Tokyo (Japan)
  9. Cornell Univ., Ithaca, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States); Univ. College Cork, Cork (Ireland); Univ. of Oxford, Oxford (United Kingdom)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1546043
Report Number(s):
BNL-211896-2019-JAAM
Journal ID: ISSN 0028-0836
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Nature (London)
Additional Journal Information:
Journal Name: Nature (London); Journal Volume: 570; Journal Issue: 7762; Journal ID: ISSN 0028-0836
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY

Citation Formats

Zhang, Yi, Mesaros, A., Fujita, Kazuhiro, Edkins, S. D., Hamidian, M. H., Ch’ng, K., Eisaki, H., Uchida, S., Davis, J. C. Séamus, Khatami, Ehsan, and Kim, Eun -Ah. Machine learning in electronic-quantum-matter imaging experiments. United States: N. p., 2019. Web. doi:10.1038/s41586-019-1319-8.
Zhang, Yi, Mesaros, A., Fujita, Kazuhiro, Edkins, S. D., Hamidian, M. H., Ch’ng, K., Eisaki, H., Uchida, S., Davis, J. C. Séamus, Khatami, Ehsan, & Kim, Eun -Ah. Machine learning in electronic-quantum-matter imaging experiments. United States. doi:10.1038/s41586-019-1319-8.
Zhang, Yi, Mesaros, A., Fujita, Kazuhiro, Edkins, S. D., Hamidian, M. H., Ch’ng, K., Eisaki, H., Uchida, S., Davis, J. C. Séamus, Khatami, Ehsan, and Kim, Eun -Ah. Wed . "Machine learning in electronic-quantum-matter imaging experiments". United States. doi:10.1038/s41586-019-1319-8.
@article{osti_1546043,
title = {Machine learning in electronic-quantum-matter imaging experiments},
author = {Zhang, Yi and Mesaros, A. and Fujita, Kazuhiro and Edkins, S. D. and Hamidian, M. H. and Ch’ng, K. and Eisaki, H. and Uchida, S. and Davis, J. C. Séamus and Khatami, Ehsan and Kim, Eun -Ah},
abstractNote = {For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena. Today, however, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is to apply this approach to experimental data—for example, to the arrays of complex electronic-structure images17 obtained from atomic-scale visualization of EQM. Here we report the development and training of a suite of artificial neural networks (ANNs) designed to recognize different types of order hidden in such EQM image arrays. These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier-doped copper oxide Mott insulators. In these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs determine that this state is unidirectional, revealing a coincident nematic EQM state. Here, strong-coupling theories of electronic liquid crystals are consistent with these observations.},
doi = {10.1038/s41586-019-1319-8},
journal = {Nature (London)},
number = 7762,
volume = 570,
place = {United States},
year = {2019},
month = {6}
}

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