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Perovskite stability is of the core importance and difficulty in currentresearch and application of perovskite solar cells. Nevertheless, over the pastcentury, the formability and stability of perovskite still relied on simplifiedfactor based on human knowledge, such as the commonly used tolerance factor t.Combining machine learning (ML) with first-principles density functionalcalculations, we proposed a strategy to firstly calculate the decompositionenergies, considered to be closely related to thermodynamic stability, of 354kinds halide perovskites, establish the machine learning relationship betweendecomposition energy and compositional ionic radius and investigate thestabilities of 14,190 halide double perovskites. The ML-predicted resultsenable us to rediscover a series of stable rare earth metal halide perovskites(up to ~1000 kinds), indicating the generalization of this model and furtherprovide elemental and concentration suggestion for improving the stability ofmixed perovskite.

Stability Engineering of Halide Perovskite via Machine Learning