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Zhang Y, Mesaros A, Fujita K, Edkins SD, Hamidian MH, Ch'ng K, Eisaki H, Uchida S, Davis JCS, Khatami E, Kim EA. Machine learning in electronic-quantum-matter imaging experiments. Nature 2019; 570:484-490. [PMID: 31217587 DOI: 10.1038/s41586-019-1319-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 04/08/2019] [Indexed: 11/09/2022]
Abstract
For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena1. 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 science2-5. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM)6-16, 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. Strong-coupling theories of electronic liquid crystals18,19 are consistent with these observations.
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Affiliation(s)
- Yi Zhang
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - A Mesaros
- Department of Physics, Cornell University, Ithaca, NY, USA.,Laboratoire de Physique des Solides, Université Paris-Sud, CNRS, Orsay, France
| | - K Fujita
- Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY, USA
| | - S D Edkins
- Department of Physics, Cornell University, Ithaca, NY, USA.,Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - M H Hamidian
- Department of Physics, Cornell University, Ithaca, NY, USA.,Department of Physics, Harvard University, Cambridge, MA, USA
| | - K Ch'ng
- Department of Physics and Astronomy, San Jose State University, San Jose, CA, USA
| | - H Eisaki
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - S Uchida
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.,Department of Physics, University of Tokyo, Tokyo, Japan
| | - J C Séamus Davis
- Department of Physics, Cornell University, Ithaca, NY, USA.,Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY, USA.,Department of Physics, University College Cork, Cork, Ireland.,Clarendon Laboratory, University of Oxford, Oxford, UK
| | - Ehsan Khatami
- Department of Physics and Astronomy, San Jose State University, San Jose, CA, USA
| | - Eun-Ah Kim
- Department of Physics, Cornell University, Ithaca, NY, USA.
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