Lead halide perovskite materials are thriving in optoelectronic applications due to their excellent properties, while their instability due to the fact that they are easily hydrolyzed is still a bottleneck for their potential application. In this work, water-resistant, monodispersed and stably luminescent cesium lead bromine perovskite nanocrystals coated with CsPb ₂ Br ₅ were obtained using a modified non-stoichiometric solution-phase method. CsPb ₂ Br ₅ 2D layers were coated on the surface of CsPbBr ₃ nanocrystals and formed a core–shell-like structure in the synthetic processes. The stability of the luminescence of the CsPbBr ₃ nanocrystals in water and ethanol atmosphere was greatly enhanced by the photoluminescence-inactive CsPb ₂ Br ₅ coating with a wide bandgap. The water-stable enhanced nanocrystals are suitable for long-term stable optoelectronic applications in the atmosphere.

Bandgap engineering of lead halide perovskite materials is critical to achieve highly efficient and stable perovskite solar cells and color tunable stable perovskite light-emitting diodes. Herein, we propose the use of machine learning as a tool to predict the bandgap of the perovskite materials from their compositions. By learning from the experimental results, machine learning algorithms present reliable performance in predicting the bandgap of the lead halide perovskites. The linear regression model can be used to manually predict the bandgap of the perovskite with the formula of Cs_aFA_bMA_(1−a−b)Pb(Cl_xBr_yI_(1−x−y))₃ (FA = formamidinium, MA = methylammonium). The neural network (NN) algorithm, which takes the interplay of cations and halide ions into account in predicting the bandgap, presents higher accuracy (with a RMSE of 0.05 eV and a Pearson coefficient larger than 0.99). Furthermore, the compositions of the mixed halide perovskites with desirable bandgaps and high iodide ratio for suppressing halide segregation are predicted by NN algorithm. These results highlight the power of machine learning in predicting the bandgap of the perovskites from their compositions and provide bandgap tuning directions for experiments.