%0 Generic %1 zhang2018machine %A Zhang, Yi %A Mesaros, A. %A Fujita, K. %A Edkins, S. D. %A Hamidian, M. H. %A Ch'ng, K. %A Eisaki, H. %A Uchida, S. %A Davis, J. C. Séamus %A Khatami, E. %A Kim, Eun-Ah %D 2018 %K machinelearn %T Machine Learning in Electronic Quantum Matter Imaging Experiments %U http://arxiv.org/abs/1808.00479 %X Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout 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 find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations.

@misc{zhang2018machine, abstract = {Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout 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 find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations.}, added-at = {2019-04-07T23:10:15.000+0200}, author = {Zhang, Yi and Mesaros, A. and Fujita, K. 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, E. and Kim, Eun-Ah}, biburl = {https://www.bibsonomy.org/bibtex/2b3aff26358784462fb4668f78532e901/cmcneile}, description = {Machine Learning in Electronic Quantum Matter Imaging Experiments}, interhash = {62e29346f1a22c9e278ebe219113326c}, intrahash = {b3aff26358784462fb4668f78532e901}, keywords = {machinelearn}, note = {cite arxiv:1808.00479Comment: 44 pages, 15 figures}, timestamp = {2019-04-07T23:10:15.000+0200}, title = {Machine Learning in Electronic Quantum Matter Imaging Experiments}, url = {http://arxiv.org/abs/1808.00479}, year = 2018 }