(10e) New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for photovoltaics, fuel cells, and many other applications. Using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator), an accurate, physically interpretable, and one-dimensional tolerance factor, Ï„, is developed that correctly classifies 92% of compounds as perovskite or nonperovskite for an experimental dataset containing 576 ABX₃ materials (X = O^2-, F^-, Cl^-, Br^-, I^-). In comparison, the widely used Goldschmidt tolerance factor, t, achieves a maximum accuracy of only 74% for the same set of materials. The probability of forming stable perovskites is mapped continuously as a function of the sizes of the A, B, and X ions revealing physical insights into how these relative sizes yield stable and unstable perovskite structures. Additionally, the new tolerance factor is shown to compare well with DFT-calculated decomposition enthalpies of single and double perovskite oxides and chalcogenides. Ï„ is applied to identify more than a thousand inorganic (Cs₂BB’Cl₆) and hybrid organic-inorganic (MA₂BB’Br₆) double perovskites that are predicted to be stable.