Deep Learning of Perovskite Octahedral Rotations from Electron Microscopy

The interdependency between magnetic order, electronic phase transitions, and lattice distortions in strongly correlated systems is well-known. In particular, local distortions in epitaxial perovskites heterostructures, in the form of oxygen octahedral rotations, are of immense interest. Scanning Transmission Electron microscopy (STEM) is one of the few probes with atomic resolution of local distortions, yet determining the 3-dimensional octahedral configuration from a 2-dimensional STEM projection of the lattice is challenging. In this talk, we will present a deep learning model that accurately predicts the 3-dimensional octahedral configuration from STEM experimental studies of a variety of heterostructures such as rare-earth perovskite LaFeO₃/EuFeO₃ superlattices. Our approach consists of co-training deep convolutional neural networks and deep autoencoders on electron scattering simulations and experiments, respectively. This combination of supervised and unsupervised learning produces a robust and general model capable of extracting both the symmetry and magnitude of octahedral distortions from STEM data, unit cell-by-unit cell, unassisted, and in real time.