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The lack of reliable methods for identifying descriptors---the sets of parameters capturing the underlying mechanisms of a material's property---is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials' properties, within the framework of compressed-sensing-based dimensionality reduction. The sure independence screening and sparsifying operator (SISSO) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability is tested beyond the training data: It rediscovers the available pressure-induced insulator-to-metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.
A 2018 study studied this question.