  Perovskite stability is of the core importance and difficulty in current
research and application of perovskite solar cells. Nevertheless, over the past
century, the formability and stability of perovskite still relied on simplified
factor based on human knowledge, such as the commonly used tolerance factor t.
Combining machine learning (ML) with first-principles density functional
calculations, we proposed a strategy to firstly calculate the decomposition
energies, considered to be closely related to thermodynamic stability, of 354
kinds halide perovskites, establish the machine learning relationship between
decomposition energy and compositional ionic radius and investigate the
stabilities of 14,190 halide double perovskites. The ML-predicted results
enable us to rediscover a series of stable rare earth metal halide perovskites
(up to ~1000 kinds), indicating the generalization of this model and further
provide elemental and concentration suggestion for improving the stability of
mixed perovskite.
