Hybrid organic–inorganic perovskites are one of the promising candidates for the next‐generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non‐negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment‐dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses.

Gallium nitride (GaN) is a widely used semiconductor for high frequency and high power devices due to of its unique electrical properties: a wide band gap, high breakdown field, and high electron mobility. However, thermal management has become a limiting factor regarding efficiency, lifetime, and advancement of GaN devices and GaN-based applications. In this work, we study the thermal conductivity of beta-phase gallium oxide (β-Ga2O3) thin films, a component of typical gate oxides used in such devices. We use time domain thermoreflectance to measure the thermal conductivity of a variety of polycrystalline β-Ga2O3 films of different thicknesses grown via open atmosphere annealing of the surfaces of GaN films on sapphire substrates. We show that the measured effective thermal conductivity of these β-Ga2O3 films can span 1.5 orders of magnitude, increasing with an increased film thickness, which is indicative of the relatively large intrinsic thermal conductivity of the β-Ga2O3 grown via this technique (8.8 ± 3.4 W m−1 K−1) and large mean free paths compared to typical gate dielectrics commonly used in GaN device contacts. By conducting time domain thermoreflectance (TDTR) measurements with different metal transducers (Al, Au, and Au with a Ti wetting layer), we attribute this variation in effective thermal conductivity to a combination of size effects in the β-Ga2O3 film resulting from phonon scattering at the β-Ga2O3/GaN interface and thermal transport across the β-Ga2O3/GaN interface. The measured thermal properties of open atmosphere-grown β-Ga2O3 and its interface with GaN set the stage for thermal engineering of gate contacts in high frequency GaN-based devices.

Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO3 matrix, ferroelectric domains in BiFeO3, and topological defects in graphene. The code developed in this manuscript are open sourced and will be released at github.com/creangnc/AE_Workflows.

Predictability of a certain effect or phenomenon is often equated with the knowledge of relevant physical laws, typically understood as a functional or numerically-derived relationship between the observations and known states of the system. Correspondingly, observations inconsistent with prior knowledge can be used to derive new knowledge on the nature of the system or indicate the presence of yet unknown mechanisms. Here we explore the applicability of Gaussian Processing (GP) to establish predictability and uncertainty of local behaviors from multimodal observations, providing an alternative to this classical paradigm. Using atomic resolution Scanning Transmission Electron Microscopy (STEM) of multiferroic Sm-doped BiFeO3 across a broad composition range, *