1f:T4aa,Predicting the stability of the perovskite structure remains a long-standing 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 ABX ₃ materials (X = O^2-, F^-, Cl^-, Br^-, 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 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites (A ₂ BB'X ₆) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists toward 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.20:T474,Measurements of the oxygen evolution reaction have been made on eighteen substituted perovskites containing first‐row transition metal ions. Rates are reported at equilibrium and at an overpotential of 0.3V. Electrode kinetic parameters are given, including roughness factors. The rate does not depend on semiconductor‐type properties. It increases as the pH of zero charge, at which the occupancy of OH− and H+ at the interface becomes equal, moves in an alkaline direction, with decrease of magnetic moment, with decrease of stability of the perovskite lattice, with decrease of the enthalpy of formation of the transition metal hydroxides, and with increase in the number of d‐electrons in the transition metal ion. The accuracy of the roughness factor measurements are affected by weakness in knowledge of true double layer capacities. The value assumed here, 60 μF cm−2, may be accurate to only ± 100%. Models are given which suggest that the pores are active throughout. The correlations between the rate and electronic properties are consistent with rate‐determining steps which involve desorption of OH radicals, e.g.,21:T403,Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.

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