New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

Abstract

Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, τ, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 $AB{X}_3$ materials ($\text{X}=$ $O^{2-}$, $F^{-}$, $C{l}^{-}$, $B{r}^{-}$, $I^{-}$) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). τ is shown to generalize outside the training set for 1,034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites ($A_2$$\text{BB’}$$X_6$) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists towards which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.

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• SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
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