Inverse Design of Hybrid Organic–Inorganic Perovskites with Suitable Bandgaps via Proactive Searching Progress

Hybrid organic-inorganic perovskites (HOIPs) have shown the encouraging development in solar cells that have achieved excellent device performance. One of the most important issues has been focused on finding Pb-free candidates with suitable bandgaps, which could accelerate the commercialization of environmentally friendly HOIP-based cells. Herein, we propose a new inverse design method, proactive searching progress (PSP), to efficiently discover potential HOIPs from universal chemical space by combining machine learning (ML) techniques. Compared to the pioneering work on this topic, we carried out our ML study based on 1201 collected HOIP samples with experimental bandgaps rather than theoretical properties. On the basis of 25 selected features, a weighted voting regressor ML model was constructed to predict bandgaps of HOIPs. The model comprehensively embedded four submodels and performed the coefficient determinations of 0.95 for leaving-one-out cross-validation and 0.91 for testing set. The feature analysis revealed that the tolerance factor ( t f ) below 0.971 and the new tolerance factor (τ f ) in 3.75-4.09 contributed to lower bandgaps and vice versa. By applying the PSP method, the Pb-free HOIPs with optimal bandgaps were successfully designed from a generated chemical space comprising over 8.20 × 10 18 combinations, which included 733848 candidates (e.g., Cs 0.334 FA 0.266 MA 0.400 Sn 0.769 Ge 0.003 Pd 0.228 Br 0.164 I 2.836 ) with an optimal bandgap of 1.34 eV for single junction solar cells, 1511073 large-bandgap candidates (e.g., Cs 0.392 FA 0.016 MA 0.592 Cr 0.383 Sr 0.347 Sn 0.270 Br 1.171 I 1.829 ) for top parts in tandem solar cells (TSCs), and 20242 low-bandgap ones (e.g., MA 0.815 FA 0.185 Sn 0.927 Ge 0.073 I 3 ) for bottom cells in TSCs. Finally, three new HOIPs were synthesized with an average bandgap error 0.07 eV between predictions and experiments. We are convinced that the proposed PSP method and ML progress could facilitate the discovery of new promising HOIPs for photovoltaic devices with the desired properties.