Compositional engineering of perovskites has enabled the precise control of material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free and inorganic perovskites have recently demonstrated potential for resolving such issues but their composition space is gigantic, making it difficult to discover promising candidates even using high‐throughput methods. A machine learning approach employing a generalized element‐agnostic fingerprint is shown to rapidly and accurately predict key properties using a new database of 344 perovskites generated with density functional theory. Bandgap, formation energy, and convex hull distance are predicted using validation subsets to within 146 meV, 15 meV per atom, and 11 meV per atom, respectively. The resulting model is used to predict trends in entirely different chemical spaces, and perform rapid composition and configuration space sampling without the need for expensive ab initio simulations.

Infrared vibrational nano-spectroscopy based on scattering scanning near-field optical microscopy (s-SNOM) provides intrinsic chemical specificity with nanometer spatial resolution. Here we use incoherent infrared radiation from a 1400 K thermal blackbody emitter for broadband infrared (IR) nano-spectroscopy. With optimized interferometric heterodyne signal amplification we achieve few-monolayer sensitivity in phonon polariton spectroscopy and attomolar molecular vibrational spectroscopy. Near-field localization and nanoscale spatial resolution is demonstrated in imaging flakes of hexagonal boron nitride (hBN) and determination of its phonon polariton dispersion relation. The signal-to-noise ratio calculations and analysis for different samples and illumination sources provide a reference for irradiance requirements and the attainable near-field signal levels in s-SNOM in general. The use of a thermal emitter as an IR source thus opens s-SNOM for routine chemical FTIR nano-spectroscopy.

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