[SY-D2] Symposium D-2

[SY-D2] Stability Engineering of Halide Perovskite via Machine Learning

^○Wan-Jian YIN, Wanjian YIN (Soochow University, China)

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 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 stability of 14190 halide double perovskites. The ML-predicted results lead us to rediscover a series of stable rare earth metal halide perovskites (up to ~10³ kinds), indicating the generalization of this model and further provide elemental and concentration suggestion for improving the stability of mixed perovskite.