While machine learning has been making enormous strides in many technical areas, it is still massively underused in transmission electron microscopy. To address this, a convolutional neural network model was developed for reliable classification of crystal structures from small numbers of electron images and diffraction patterns with no preferred orientation. Diffraction data containing 571,340 individual crystals divided among seven families, 32 genera, and 230 space groups were used to train the network. Despite the highly imbalanced dataset, the network narrows down the space groups to the top two with over 70% confidence in the worst case and up to 95% in the common cases. As examples, we benchmarked against alloys to two-dimensional materials to cross-validate our deep-learning model against high-resolution transmission electron images and diffraction patterns. We present this result both as a research tool and deep-learning application for diffraction analysis.

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A major challenge in the application of nanostructured electrolytes in solid oxide electrochemical cells is grain boundary blocking originated from unsatisfied atomic bonding and coordination. The resulting increase in grain boundary resistivity works against the expected benefits from the enhanced ion exchange rates enabled by the extensive interfacial network in nanocrystalline materials. This study addresses this challenge by demonstrating that a reduction in the grain boundary excess energies increases the net ionic conductivity as directly measured by impedance electrical spectroscopy in nanocrystalline yttria-stabilized zirconia. The reduced grain boundary energy was designed by doping the system with lanthanum, leading to local excess energy reduction due to segregation of La to boundaries as observed by scanning transmission electron microscopy-based energy-dispersive spectroscopy. The results suggest rare-earth ions with favorable grain boundary segregation enthalpy can smooth out the energy landscape across grain boundaries and thus facilitate ion mobility in the nanocrystalline electrolyte.