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Machine learning in electronic-quantum-matter imaging experiments

Cornell Affiliated Author(s)


Y. Zhang
A. Mesaros
K. Fujita
S.D. Edkins
M.H. Hamidian
K. Ch’ng
H. Eisaki
S. Uchida
J.C.S. Davis
E. Khatami
Eun-Ah Kim


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. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.

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Group (Lab)

Eun-Ah Kim Group
J.C. Seamus Davis Group

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