Simple and interpretable data-driven descriptor accurately predicts the synthesizability of single and double perovskites.

The lack of reliable methods for identifying descriptors -the sets of parameters capturing the underlying mechanisms of a materials 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. SISSO (sure independence screening and sparsifying operator) 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 are tested beyond the training data: It rediscovers the available pressure-induced insulator→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.

Understanding Ostwald ripening and disintegration of supported metal particles under operating conditions has been of central importance in the study of sintering and dispersion of heterogeneous catalysts for long-term industrial implementation. To achieve a quantitative description of these complicated processes, an atomistic and generic theory taking into account the reaction environment, particle size and morphology, and metal− support interaction is developed. It includes (1) energetics of supported metal particles, (2) formation of monomers (both the metal adatoms and metal−reactant complexes) on supports, and (3) corresponding sintering rate equations and total activation energies, in the presence of reactants at arbitrary temperature and pressure. The thermodynamic criteria for the reactant assisted Ostwald ripening and induced disintegration are formulated, and the influence of reactants on sintering kinetics and redispersion are mapped out. Most energetics and kinetics barriers in the theory can be obtained conveniently by first-principles theory calculations. This allows for the rapid exploration of sintering and disintegration of supported metal particles in huge phase space of structures and compositions under various reaction environments. General strategies of suppressing the sintering of the supported metal particles and facilitating the redispersions of the low surface area catalysts are proposed. The theory is applied to TiO 2 (110) supported Rh particles in the presence of carbon monoxide, and reproduces well the broad temperature, pressure, and particle size range over which the sintering and redispersion occurred in such experiments. The result also highlights the importance of the metal−carbonyl complexes as monomers for Ostwald ripening and disintegration of supported metal catalysts in the presence of CO.

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.