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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]
+ Show Author Affiliations
  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)
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.
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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. https://doi.org/10.1038/s41586-019-1319-8
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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. https://doi.org/10.1038/s41586-019-1319-8. https://www.osti.gov/servlets/purl/1546043.
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@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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https://doi.org/10.1038/s41586-019-1319-8
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Works referenced in this record:

Machine learning modeling of superconducting critical temperature
journal, June 2018

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
journal, August 2017

  • Rosenbrock, Conrad W.; Homer, Eric R.; Csányi, Gábor
  • npj Computational Materials, Vol. 3, Issue 1
  • DOI: 10.1038/s41524-017-0027-x

Machine learning phases of matter
journal, February 2017

  • Carrasquilla, Juan; Melko, Roger G.
  • Nature Physics, Vol. 13, Issue 5
  • DOI: 10.1038/nphys4035

Solving the quantum many-body problem with artificial neural networks
journal, February 2017

Neural Decoder for Topological Codes
journal, July 2017

Learning phase transitions by confusion
journal, February 2017

  • van Nieuwenburg, Evert P. L.; Liu, Ye-Hua; Huber, Sebastian D.
  • Nature Physics, Vol. 13, Issue 5
  • DOI: 10.1038/nphys4037

Machine learning quantum phases of matter beyond the fermion sign problem
journal, August 2017

Quantum Loop Topography for Machine Learning
journal, May 2017

Probing many-body localization with neural networks
journal, June 2017

Neural-network quantum state tomography
journal, February 2018

Electronic liquid-crystal phases of a doped Mott insulator
journal, June 1998

  • Kivelson, S. A.; Fradkin, E.; Emery, V. J.
  • Nature, Vol. 393, Issue 6685
  • DOI: 10.1038/31177

SUPERCONDUCTIVITY:Self-Organized One Dimensionality
journal, October 1999

From quantum matter to high-temperature superconductivity in copper oxides
journal, February 2015

  • Keimer, B.; Kivelson, S. A.; Norman, M. R.
  • Nature, Vol. 518, Issue 7538
  • DOI: 10.1038/nature14165

The Electron-Pairing Mechanism of Iron-Based Superconductors
journal, April 2011

Resonant X-Ray Scattering Studies of Charge Order in Cuprates
journal, March 2016

Nematic Fermi Fluids in Condensed Matter Physics
journal, August 2010

Colloquium : Theory of intertwined orders in high temperature superconductors
journal, May 2015

  • Fradkin, Eduardo; Kivelson, Steven A.; Tranquada, John M.
  • Reviews of Modern Physics, Vol. 87, Issue 2
  • DOI: 10.1103/RevModPhys.87.457

Atomic-scale electronic structure of the cuprate d-symmetry form factor density wave state
journal, October 2015

  • Hamidian, M. H.; Edkins, S. D.; Kim, Chung Koo
  • Nature Physics, Vol. 12, Issue 2
  • DOI: 10.1038/nphys3519

Distinguishing patterns of charge order: Stripes or checkerboards
journal, October 2006

From stripe to checkerboard ordering of charge-density waves on the square lattice in the presence of quenched disorder
journal, July 2006

Commensurate 4 a 0 -period charge density modulations throughout the Bi 2 Sr 2 CaCu 2 O 8+x pseudogap regime
journal, October 2016

  • Mesaros, Andrej; Fujita, Kazuhiro; Edkins, Stephen D.
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 45
  • DOI: 10.1073/pnas.1614247113

Learning representations by back-propagating errors
journal, October 1986

  • Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
  • Nature, Vol. 323, Issue 6088
  • DOI: 10.1038/323533a0

Quenched disorder and vestigial nematicity in the pseudogap regime of the cuprates
journal, May 2014

  • Nie, L.; Tarjus, G.; Kivelson, S. A.
  • Proceedings of the National Academy of Sciences, Vol. 111, Issue 22
  • DOI: 10.1073/pnas.1406019111

Approximation by superpositions of a sigmoidal function
journal, December 1989

  • Cybenko, G.
  • Mathematics of Control, Signals, and Systems, Vol. 2, Issue 4
  • DOI: 10.1007/BF02551274

Approximation by superpositions of a sigmoidal function
journal, December 1992

  • Cybenko, G.
  • Mathematics of Control, Signals, and Systems, Vol. 5, Issue 4
  • DOI: 10.1007/bf02134016

Discovering the building blocks of atomic systems using machine learning: Application to grain boundaries
text, January 2017

  • Rosenbrock, Cw; Homer, Er; Csányi, G.
  • Apollo - University of Cambridge Repository
  • DOI: 10.17863/cam.34298
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    [ × clear filter / sort ]

    Works referencing / citing this record:

    A Critical Review of Machine Learning of Energy Materials
    journal, January 2020

    Revealing ferroelectric switching character using deep recurrent neural networks
    journal, October 2019

    Identifying quantum phase transitions using artificial neural networks on experimental data
    journal, July 2019

    Classifying snapshots of the doped Hubbard model with machine learning
    journal, July 2019

    Heterogeneity at multiple length scales in halide perovskite semiconductors
    journal, July 2019

    • Tennyson, Elizabeth M.; Doherty, Tiarnan A. S.; Stranks, Samuel D.
    • Nature Reviews Materials, Vol. 4, Issue 9
    • DOI: 10.1038/s41578-019-0125-0

    Embedding human heuristics in machine-learning-enabled probe microscopy
    journal, February 2020

    • Gordon, Oliver M.; Junqueira, Filipe L. Q.; Moriarty, Philip J.
    • Machine Learning: Science and Technology, Vol. 1, Issue 1
    • DOI: 10.1088/2632-2153/ab42ec

    Accelerating lattice quantum Monte Carlo simulations using artificial neural networks: Application to the Holstein model
    journal, July 2019

    Two-dimensional frustrated J 1 − J 2 model studied with neural network quantum states
    journal, September 2019

    Revealing quantum chaos with machine learning
    journal, February 2020

    Machine learning and the physical sciences
    journal, December 2019

    Heterogeneity at multiple length scales in halide perovskite semiconductors
    text, January 2019

    • Tennyson, Beth; Doherty, Tas; Stranks, Samuel
    • Apollo - University of Cambridge Repository
    • DOI: 10.17863/cam.41839
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