A machine-learning model is developed that can accurately predict the band gap of inorganic solids based only on composition. This method uses support vector classification to first separate metals from nonmetals, followed by quantitatively predicting the band gap of the nonmetals using support vector regression. The superb accuracy of the regression model is obtained by using a training set composed entirely of experimentally measured band gaps and utilizing only compositional descriptors. In fact, because of the unique training set of experimental data, the machine learning predicted band gaps are significantly closer to the experimentally reported values than DFT (PBE-level) calculated band gaps. Not only does this resulting tool provide the ability to accurately predict the band gap for any composition but also the versatility and speed of the prediction based only on composition will make this a great resource to screen inorganic phase space and direct the development of functional inorganic materials.

In the pursuit of materials with exceptional mechanical properties, a machine-learning model is developed to direct the synthetic efforts toward compounds with high hardness by predicting the elastic moduli as a proxy. This approach screens 118 287 compounds compiled in crystal structure databases for the materials with the highest bulk and shear moduli determined by support vector machine regression. Following these models, a ternary rhenium tungsten carbide and a quaternary molybdenum tungsten borocarbide are selected and synthesized at ambient pressure. High-pressure diamond anvil cell measurements corroborate the machine-learning prediction of the bulk modulus with less than 10% error, as well as confirm the ultraincompressible nature of both compounds. Subsequent Vickers microhardness measurements reveal that each compound also has an extremely high hardness exceeding the superhard threshold of 40 GPa at low loads (0.49 N). These results show the effectiveness of materials development through state-of-the-art machine-learning techniques by identifying functional inorganic materials.

Rare-earth substituted inorganic phosphors are critical for solid state lighting. New phosphors are traditionally identified through chemical intuition or trial and error synthesis, inhibiting the discovery of potential high-performance materials. Here, we merge a support vector machine regression model to predict a phosphor host crystal structure’s Debye temperature, which is a proxy for photoluminescent quantum yield, with high-throughput density functional theory calculations to evaluate the band gap. This platform allows the identification of phosphors that may have otherwise been overlooked. Among the compounds with the highest Debye temperature and largest band gap, NaBaB9O15 shows outstanding potential. Following its synthesis and structural characterization, the structural rigidity is confirmed to stem from a unique corner sharing [B3O7]5– polyanionic backbone. Substituting this material with Eu2+ yields UV excitation bands and a narrow violet emission at 416 nm with a full-width at half-maximum of 34.5 nm. More importantly, NaBaB9O15:Eu2+ possesses a quantum yield of 95% and excellent thermal stability.

An ensemble machine‐learning method is demonstrated to be capable of finding superhard materials by directly predicting the load‐dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load‐dependent Vickers hardness data are extracted from the literature and used to train a supervised machine‐learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model is then tested by synthesizing and measuring the load‐dependent hardness of several unreported disilicides and analyzing the predicted hardness of several classic superhard materials. The trained ensemble method is then employed to screen for superhard materials by examining more than 66 000 compounds in crystal structure databases, which show that 68 known materials have a Vickers hardness ≥40 GPa at 0.5 N (applied force) and only 10 exceed this mark at 5 N. The hardness model is then combined with the data‐driven phase diagram generation tool to expand the limited number of reported high hardness compounds. Eleven ternary borocarbide phase spaces are studied, and more than ten thermodynamically favorable compositions with a hardness above 40 GPa (at 0.5 N) are identified, proving this ensemble model's ability to find previously unknown materials with outstanding mechanical properties.

The development of superhard materials is focused on two very different classes of compounds. The first contains only light, inexpensive main group elements and requires high pressures and temperatures for preparation whereas the second class combines a transition metal with light main group elements and in general tends to only need high reaction temperatures. Although the preparation conditions are simpler, the second class of compounds suffers from the transition metals used being expensive and exceedingly scarce. Thus, in the search for novel superhard compounds, synthetic accessibility, resource considerations, and material response must be balanced. The research presented here develops high-information density plots drawn from high-throughput first-principle calculations and data mining to reveal the optimal composition space to synthesize new materials. This contribution includes analysis of the experimentally known Vickers hardness for materials as well as screening over 1100 compounds from first-principle calculations to predict their intrinsic hardness. Both data sets are analyzed not only for their mechanical performance but also the compositional scarcity, and Herfindahl-Hirschman index is calculated. Following this methodology, it is possible to ensure targeted materials are not only sustainable and accessible but that they will also have superb mechanical response.