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

Abstract

Abstract 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 A2BB′X6 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 A2BB′X6 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 ((CH3NH3)2AgGaBr6, (CH3NH3)2AgInBr6 and (C2NH6)2AgInBr6) 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 CH3NH3PbI3 and the calculated thermal conductivities are 5.04, 4.39 and 5.16 Wm−1K−1, respectively, with Debye temperatures larger than 500 K, beneficial for suppression of both nonradiative combination and heat‐induced degradation.

Keywords

• density‐functional theory
• hybrid organic–inorganic perovskites
• lead‐free double perovskites
• machine learning
• photovoltaics