Mixed double halide organic-inorganic perovskites (MDHOIPs) have exhibited both good stability and high power conversion efficiency and have been regarded as attractive photovoltaic materials. Nevertheless, due to the complexity of structures, large-scale screening from thousands of possible candidates remains a great challenge. In this work, advanced machine learning (ML) techniques and first-principles calculations are combined to achieve a rapid screening on MDHOIPs for solar cells. Successfully, 204 stable lead-free MDHOIPs with optimal bandgaps are selected out from 11370 candidates. The accuracy of ML models for perovskite structure formability and bandgap is over 94% and 97%, respectively. Moreover, representative MDHOIP candidates, MA2GeSnI4Br2 and MA2InBiI2Br4, stand out with suitable direct bandgaps, light carrier effective masses, small exciton binding energies, strong visible light absorption, and good stability against decomposition.

Accelerated design of promising mixed lead-free double halide organic-inorganic perovskites for photovoltaics using machine learning

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