TY - JOUR
T1 - Machine learning in electronic-quantum-matter imaging experiments
AU - Zhang, Yi
AU - Mesaros, A.
AU - Fujita, K.
AU - Edkins, S. D.
AU - Hamidian, M. H.
AU - Ch’ng, K.
AU - Eisaki, H.
AU - Uchida, S.
AU - Davis, J. C.Séamus
AU - Khatami, Ehsan
AU - Kim, Eun Ah
N1 - Funding Information:
Acknowledgements We thank P. Ginsparg, J. Hoffman, S. Kivelson, R. Melko, A. Millis, M. Stoudenmire, K. Weinberger and J. Zaanen for discussions and communications. A.M. and Y.Z. acknowledge support from DOE DESC0010313; Y.Z. acknowledges support from DOE DE-SC0018946; E.-A.K. and J.C.S.D. acknowledge support by the Cornell Center for Materials Research with funding from the NSF MRSEC programme (DMR-1719875); E.K. and K.C. acknowledge support from the NSF through grant number DMR-1609560. E.-A.K. and E.K. acknowledge support from the Kavli Institute for Theoretical Physics (where initial discussions about the project took place), which is supported in part by the NSF under grant number PHY-1748958. S.U. and H.E. acknowledge support from a Grant-in-aid for Scientific Research from the Ministry of Science and Education of Japan and the Global Centers of Excellence Program of the Japan Society for the Promotion of Science. K.F. and J.C.S.D. acknowledge support from the US Department of Energy, Office of Basic Energy Sciences, under contract number DEAC02-98CH10886; S.D.E, M.H.H. and J.C.S.D. acknowledge support from the Moore Foundation’s EPiQS Initiative through grant GBMF4544; J.C.S.D. acknowledges support from Science Foundation Ireland under award SFI 17/RP/5445 and from the European Research Council (ERC) under award number DLV-788932.
Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2019/6/27
Y1 - 2019/6/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068080545&partnerID=8YFLogxK
U2 - 10.1038/s41586-019-1319-8
DO - 10.1038/s41586-019-1319-8
M3 - Article
C2 - 31217587
AN - SCOPUS:85068080545
SN - 0028-0836
VL - 570
SP - 484
EP - 490
JO - Nature
JF - Nature
IS - 7762
ER -