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Metal halide perovskite (MHP) solar cells are promising aerospace power sources given their potential as inexpensive, lightweight, and resilient solar electricity generators. Herein, the intrinsic radiation tolerance of unencapsulated methylammonium lead iodide/chloride (CH3NH3PbI3-xClx) films was isolated. Spatially resolved photoluminescence (PL) spectroscopy and confocal microscopy revealed the fundamental defect physics through optical changes as films were irradiated with 4.5 MeV neutrons and 20 keV protons at fluences between 5×1010 and 1×1016 p+/cm2. As proton radiation increased beyond 1×1013 p+/cm2, defects formed in the film, causing both a decrease in photoluminescence intensity and a 30% increase in surface darkening. All proton irradiated films additionally exhibited continuous increase of energy bandgaps and decreasing charge recombination lifetimes with increasing proton fluences. These optical changes in the absorber layer precede performance declines detectable in standard current-voltage measurements of complete solar cell devices and therefore have the potential of serving as early indicators of radiation tolerance. 

Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. 

Abstract Boron nitride nanotubes (BNNTs) have attracted attention for their predicted extraordinary properties; yet, challenges in synthesis and processing have stifled progress on macroscopic materials. Recent advances have led to the production of highly pure BNNTs. Here we report that neat BNNTs dissolve in chlorosulfonic acid (CSA) and form birefringent liquid crystal domains at concentrations above 170 ppmw. These tactoidal domains merge into millimeter-sized regions upon light sonication in capillaries. Cryogenic electron microscopy directly shows nematic alignment of BNNTs in solution. BNNT liquid crystals can be processed into aligned films and extruded into neat BNNT fibers. This study of nematic liquid crystals of BNNTs demonstrates their ability to form macroscopic materials to be used in high-performance applications.