Séamus Davis: Machine learning in electronic-quantum-matter imaging experiments
Channel: Oxford ML and Physics Seminars
Date: 2022-06-03

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. With his team, Séamus Davis 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.