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Essentials of the scientific discovery process have remained largelyunchanged for centuries: systematic human observation of natural phenomena isused to form hypotheses that, when validated through experimentation, aregeneralized into established scientific theory. Today, however, we face majorchallenges because automated instrumentation and large-scale data acquisitionare generating data sets of such volume and complexity as to defy humananalysis. Radically different scientific approaches are needed, with machinelearning (ML) showing great promise, not least for materials science research.Hence, given recent advances in ML analysis of synthetic data representingelectronic quantum matter (EQM), the next challenge is for ML to engageequivalently with experimental data. For example, atomic-scale visualization ofEQM yields arrays of complex electronic structure images, that frequently eludeeffective analyses. Here we report development and training of an array ofartificial neural networks (ANN) designed to recognize different types ofhypothesized order hidden in EQM image-arrays. These ANNs are used to analyzean experimentally-derived EQM image archive from carrier-doped cuprate Mottinsulators. Throughout these noisy and complex data, the ANNs discover theexistence of a lattice-commensurate, four-unit-cell periodic,translational-symmetry-breaking EQM state. Further, the ANNs find thesephenomena to be unidirectional, revealing a coincident nematic EQM state.Strong-coupling theories of electronic liquid crystals are congruent with allthese observations.

Machine Learning in Electronic Quantum Matter Imaging Experiments