  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.
