Predicting Experimental Formability of Hybrid Organic-Inorganic Perovskites via Imbalanced Learning

Hybrid organic-inorganic perovskites (HOIPs) have gained lots of attention in the photovoltaic field, but their further development is restrained by contaminant and stability. More potential HOIPs should be explored for photovoltaic devices. In this work, we collected 539 HOIPs and 24 non-HOIPs experimentally synthesized to explore novel compositions of HOIPs. An imbalanced learning was carried out, and the best classification model achieved a leaving-one-out cross-validation accuracy of 100.0% and a test accuracy of 96.1%. The A site atomic radii (AR_A), A site ionic radius (IR_A), and tolerance factor (t_f) were identified as the most important features. AR_A < 2.72 Å, IR_A < 2.65 Å, and t_f < 1.01 contributed to perovskite formability, and the formability possibilities of the corresponding samples were over 90.0%. Potential A site organic fragments were identified for perovskite solar cells, such as dimethylamine, hydroxylamine, hydrazine, etc. Finally, three new Sn-Ge mixed systems of HOIPs were successfully synthesized, which was consistent with the model predictions.