Oxide perovskites and their derivatives are attractive candidates for the diverse applications in renewable energy conversions due to their unique structural and compositional flexibility and high material stability.

Formability and stability issues are of core importance and difficulty in current research and applications of perovskites. Nevertheless, over the past century, determination of the formability and stability of perovskites has relied on semi empirical models derived from physics intuition, such as the commonly used Goldschmidt tolerance factor, t. Here, through high-throughput density functional theory (DFT) calculations, a database containing the decomposition energies, considered to be closely related to the thermodynamic stability of 354 halide perovskite candidates, is established. To map the underlying relationship between the structure and chemistry features and the decomposition energies, a well-functioned machine learning (ML) model is trained over this theory-based database and further validated by experimental observations of perovskite formability (F 1 score, 95.9%) of 246 A 2 B(I)B(III)X 6 compounds that are not present in the training database; the model performs a lot better than empirical descriptors such as tolerance factor t (F 1 score, 77.5%). This work demonstrates that the experimental engineering of stable perovskites by ML could solely rely on training data derived from high-throughput DFT computing, which is much more economical and efficient than experimental attempts at materials synthesis. demonstrate enhanced stability in comparison to their single perovskite counterparts. For example, although the photoactive perovskite phases of RbPbI 3 , CsPbI 3 , and FAPbI 3 are unstable at room temperature, the proper mixing of (Rb, Cs, and FA) on the A site can result in a stable mixed-A-site perovskite. [21,23] Currently, for the precise experimental control of stability, more mixing elements, such as triple-A (Cs, MA, FA)InBiBr 6 , [26] triple-A double-X Cs x (MA 0.17 FA 0.83 ) (1-x) Pb(I 0.83 Br 0.17 ) 3 , [20] and quadruple-A double-X Rb-FA 0.75 MA 0.15 Cs 0.1 PbI 2 Br, [27] are used, making the problem more complicated. Further insight is required to understand the effect of elemental mixing and provide guidance for stability engineering, especially relating to the type of mixing elements and their concentrations.

Although the power conversion efficiency (PCE) of CH3NH3PbI3-based solar cells has achieved 22.1%, which is comparable to commercialized thin-film CdTe and Cu(In,Ga)Se2 solar cells, the long-term stability is the main obstacle for the commercialization of perovskite solar cells. Recent efforts have been made to explore alternative inorganic perovskites, which were assumed to have better stability than organic-inorganic hybrid CH3NH3PbI3. In this short review, we will keep up with experiments and summarize recent progresses of inorganic double halide perovskite, in particular to Cs2AgBiBr6, Cs2AgInCl6, Cs2InBiBr6 and their family members. We will also share our opinions on the promise of such class of materials.

The competition between corner-, edge-, and face-sharing octahedral networks is a cause of phase inhomogeneity in metal halide perovskite thin-films. Here we probe the charged iodine vacancy distribution and transport at the junction between cubic and hexagonal polytypes of CsPbI3 from first-principles materials modeling. We predict a lower defect formation energy in the face-sharing regions, which correlates with a longer Pb–I bond length and causes a million-fold increase in local defect concentration. These defects are predicted to be more mobile in the face-sharing regions with a reduced activation energy for vacancy-mediated diffusion. We conclude that hexagonal phase inclusions or interfaces will act as defect sinks that could influence carrier dynamics in perovskite-based solar cells and electrical devices.