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.