Within materials informatics, a rapidly developing subfield of materials research, past (curated) data are mined and learned for either discovering new materials or identifying new functionalities of known materials. This paper provides an example of this process. Starting from a recently developed (very diverse) dataset which includes 1346 hybrid organic-inorganic perovskites (HOIPs), we downselect a subset of 350 three dimensional HOIPs to a final set of four lead-free HOIPs, including CH3NH3SnI3, HC(NH2)2SnI3, NH2NH3SnI3, and NH2(CH2)3SnI3, in which the first two were experimentally synthesized and the others remain hypothetical. Using first-principles based computational methods, we show that these HOIPs have preferable electronic band structures and carrier effective mass, good optical properties, and high spectroscopic limited maximum efficiency. Compared to the experimental data, we find that state-of-the-art numerical methods can predict the electronic and optical properties fairly well, while the current model for the spectroscopic limited maximum efficiency is inadequate for capturing the power conversion efficiency of a solar absorber. We suggest that the HOIP dataset should be expanded to include larger structures of HOIPs, thereby being more useful for future data-mining and machine-learning approaches.

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