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

TL;DR

This paper introduces a new one-dimensional tolerance factor, { au}, that accurately predicts the stability of perovskite structures, aiding the discovery of new materials for photovoltaics and catalysis.

Contribution

The authors developed a novel, interpretable tolerance factor using SISSO that predicts perovskite stability with high accuracy and generalizes well beyond the training data.

Findings

92% accuracy on experimental dataset

91% accuracy on unseen perovskites

Identified 23,314 potential stable double perovskites

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, {\tau}, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 $ABX_3$ materials ($\textit{X} =$ $O^{2-}$, $F^-$, $Cl^-$, $Br^-$, $I^-$) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). {\tau} 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$$\textit{BB'}$$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.