Abstract: Among oxides, bismuthates have been gaining much interest due to their unique features. In addition to their superconducting properties, they show potential for applications as topological insulators and as possible spin-to-charge converters. After being first investigated in their bulk form in the 1980s, bismuthates have been successfully grown as thin films. However, most efforts have focused on BaBiO₃, with SrBiO₃receiving only little attention. Here, we report the growth of epitaxial films of SrBiO₃on both TiO₂-terminated SrTiO₃and NdO-terminated NdScO₃substrates by pulsed laser deposition. SrBiO₃has a pseudocubic lattice constant of ∼4.25 Å and grows relaxed on NdScO₃. Counter-intuitively, it grows with a slight tensile strain on SrTiO₃despite a large lattice mismatch, which should induce compressive strain. High-resolution transmission electron microscopy reveals that this occurs as a consequence of structural domain matching, with blocks of 10 SrBiO₃unit planes matching blocks of 11 SrTiO₃unit planes. This work provides a framework for the synthesis of high quality perovskite bismuthates films and for the understanding of their interface interactions with homostructural substrates.

Abstract: Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping.