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Hybrid organic-inorganic perovskites have drawn significant interest forapplications in optoelectronics over the last few years. Despite rapid progressin understanding the photophysics of perovskite, there remains a need forimproved understanding of the effect of microstructure on perovskitephotophysical processes. Here, we combine unsupervised machine learning andcathodoluminescence microscopy of a prototypical hybrid perovskite film todecode photophysical processes that are otherwise lost with conventionalGaussian image processing. Hyperspectral maps are decoded with non-negativematrix factorization, revealing components relating to primary band-edgeemission, photon recycling, and defect emission. A blind-spectral non-negativematrix factorization procedure provides additional understanding of changes inan intermediate perovskite phase under electron beam exposure and illustrateshow traditional Gaussian techniques may hide relevant emission features thatare critical to the development of environmentally robust perovskite devices

Hyperspectral nanoscale mapping of hybrid perovskite photophysics at the single grain level