PGAI-AAAI-20

Abstract: Rich functionalities of quantum and strongly correlated materials emerge from the interplay between the electronic, orbital, lattice, and spin degrees of freedom that often lead to complex structural and electronic phenomena spanning atomic to mesoscopic scales. In many cases, these phenomena are associated with translational symmetry breaking, local frozen disorder, or strongly correlated disorder. However, the relevant mechanisms and roles of individual subsystems often remain unknown. Over the last decade, Scanning Transmission Electron Microscopy has emerged as a powerful quantitative probe of materials structure on the atomic level, providing high veracity information on local chemical bonding, composition, and symmetry breaking distortions. We aim to harness the power of machine learning methods to build a comprehensive picture of the chemistry and physics of quantum materials from these observations.