Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities.

Exploring lead-free candidates and improving efficiency and stability remain the obstacle of hybrid organic-inorganic perovskite-based devices commercialization. Traditional trial-and-error methods seriously restrict the discovery especially for large search space, complex crystal structure and multi-objective properties. Here, the authors propose a multi-step and multi-stage screening scheme to accelerate the discovery of hybrid organic-inorganic perovskites A 2 BB'X 6 from a large number of candidates through combining machine learning with high-throughput calculations for pursuing excellent efficiency and thermal stability in solar cells. Followed by a series of screenings, the structure-property relationships mapping A 2 BB'X 6 properties are built and the predictions are close to reported experimental results. Successfully, four experimental-feasibly candidates with good stability, high Debye temperature and suitable band gap are screened out and further verified by density-functional theory calculations, in which the predicted efficiency for three lead-free candidates ((CH 3 NH 3 ) 2 AgGaBr 6 , (CH 3 NH 3 ) 2 AgInBr 6 and (C 2 NH 6 ) 2 AgInBr 6 ) achieves 20.6%, 19.9% and 27.6% due to ultrabroadband absorption region ranging from UVC to IRC with excitonic radiative combination rates as low as 10 ps, large or intermediate polarons form with properties similar to CH 3 NH 3 PbI 3 and the calculated thermal conductivities are 5.04, 4.39 and 5.16 Wm -1 K -1 , respectively, with Debye temperatures larger than 500 K, beneficial for suppression of both nonradiative combination and heat-induced degradation.