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Perovskite oxides such as LaFeO3 are a well-studied family of materials that possess a wide range of useful and novel properties. Successfully synthesizing perovskite oxide samples usually requires a significant number of growth attempts and a detailed film characterization on each sample to find the optimal growth window of a material. The most common real-time in situ diagnostic technique available during molecular beam epitaxy (MBE) synthesis is reflection high-energy electron diffraction (RHEED). Conventional use of RHEED allows a highly experienced operator to determine growth rate by monitoring intensity oscillations and make some qualitative observations during growth, such as recognizing the sample has become amorphous or recognizing that large islands have formed on the surface. However, due to a lack of theoretical understanding of the diffraction patterns, finer, more precise levels of observations are challenging. To address these limitations, we implement new data analytics techniques in the growth of three LaFeO3 samples on Nb-doped SrTiO3 by MBE. These techniques improve our ability to perform unsupervised machine learning using principal component analysis (PCA) and k-means clustering by using drift correction to overcome sample or stage motion during growth and intensity transformations that highlight more subtle features in the images such as Kikuchi bands. With this approach, we enable the first demonstration of PCA and k-means across multiple samples, allowing for quantitative comparison of RHEED videos for two LaFeO3 film samples. These capabilities set the stage for real-time processing of RHEED data during growth to enable machine learning-accelerated film synthesis. 

Perovskite oxide heterostructures host a large number of interesting phenomena such as ferroelectricity, which are often driven by octahedral distortions in the crystal that may induce polarization. SrHfO3 (SHO) is a perovskite oxide with a pseudocubic lattice parameter of 4.08 Å that previous density functional theory (DFT) calculations suggest can be stabilized in a ferroelectric P4mm phase when stabilized with sufficient compressive strain. Additionally, it is insulating and possesses a large band gap and a high dielectric constant, making it an ideal candidate for oxide electronic devices. To test the viability of epitaxial strain as a driver of ferroic phase transitions, SHO films were grown by hybrid molecular beam epitaxy (hMBE) with a tetrakis(ethylmethylamino)hafnium(IV) source on GdScO3 and TbScO3 substrates. Strained SHO phases were characterized using X-ray diffraction, X-ray absorption spectroscopy, and scanning transmission electron microscopy to determine the space group of the strained films, with the results compared to those of DFT-optimized models of phase stability versus strain. Contrary to past reports, we find that compressively strained SrHfO3 undergoes octahedral tilt distortions without associated ferroelectric polarization and most likely takes on the I4/mcm phase with the a0a0c– tilt pattern. 

Abstract Emergent behavior at oxide interfaces has driven research in complex oxide films for the past 20 years. Interfaces have been engineered for applications in spintronics, topological quantum computing, and high-speed electronics with properties not observed in bulk materials. Advances in synthesis have made the growth of these interfaces possible, while X-ray photoelectron spectroscopy (XPS) studies have often explained the observed interfacial phenomena. This review discusses leading recent research, focusing on key results and the XPS studies that enabled them. We describe how the in situ integration of synthesis and spectroscopy improves the growth process and accelerates scientific discovery. Specific techniques include determination of interfacial intermixing, valence band alignment, and interfacial charge transfer. A recurring theme is the role that atmospheric exposure plays on material properties, which we highlight in several material systems. We demonstrate how synchrotron studies have answered questions that are impossible in lab-based systems and how to improve such experiments in the future.