91̽»¨

Today, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Machine learning (ML) shows great promise for research fields such as quantum materials science. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM), the next challenge was to apply this approach to experimental data—for example, to the arrays of complex electronic-structure images obtained from atomic-scale visualization of EQM. We developed and trained 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 crystals are consistent with these discoveries.

³§Ã©²¹³¾³Ü²õ Davis is a professor at the University College Cork, and 91̽»¨. Prior, he was a professor of physics at University of California Berkeley, Cornell University, and St Andrews University. His research focuses on the fundamental physics of exotic states of electronic, magnetic, atomic and space-time quantum matter. His group’s specialty is the development of innovative instrumentation to allow direct atomic-scale visualization or perception of the quantum many-body phenomena that are characteristic of these states. He is a Fellow of the Institute of Physics (UK), the American Physical Society (USA), the Max Planck Gesellschaft (DE), the Royal Irish Academy, of the American Association for the Advancement of Science, and a Member of the US National Academy of Sciences.