Machine learning in electronic-quantum-matter imaging experiments

Yi Zhang, A. Mesaros, K. Fujita, S. D. Edkins, M. H. Hamidian, K. Ch’ng, H. Eisaki, S. Uchida, J. C. Séamus Davis, Ehsan Khatami and Eun-Ah Kim ()
Additional contact information
Yi Zhang: Cornell University
A. Mesaros: Cornell University
K. Fujita: Brookhaven National Laboratory
S. D. Edkins: Cornell University
M. H. Hamidian: Cornell University
K. Ch’ng: San Jose State University
H. Eisaki: National Institute of Advanced Industrial Science and Technology
S. Uchida: National Institute of Advanced Industrial Science and Technology
J. C. Séamus Davis: Cornell University
Ehsan Khatami: San Jose State University
Eun-Ah Kim: Cornell University

Nature, 2019, vol. 570, issue 7762, 484-490

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41586-019-1319-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:570:y:2019:i:7762:d:10.1038_s41586-019-1319-8

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-019-1319-8

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().