Experimental and theoretical thermodynamic studies in Ba2MgReO6-the ground state in the context of Jahn-Teller effect.

We address the degeneracy of the ground state multiplet on the 5d1Re6+ion in double perovskite Ba2MgReO6using a combination of specific heat measurements and density functional calculations. For Ba2MgReO6, two different ground state multiplets have previously been proposed-a quartet (with degeneracyN= 4) (Hirai and Hiroi 2019J. Phys. Soc. Japan88064712) and a doublet (N= 2) (Marjerrisonet al2016Inorg. Chem.5510701). Here we employ two independent methods for the estimation of phonon contribution in heat capacity data to obtain the magnetic entropySmag, which reflects the degeneracy of the ground state multipletNthroughSmag=RlnN. In both cases, we obtain that in the temperature range covering 2 to 120 K the released entropy is better described bySmag=Rln2. The detailed nature of the ground state multiplet in Ba2MgReO6remains an open question.

Finding the Next Superhard Material through Ensemble Learning.

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.

Identifying an efficient, thermally robust inorganic phosphor host via machine learning.

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.

Machine Learning Directed Search for Ultraincompressible, Superhard 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.

Predicting the Band Gaps of Inorganic Solids by Machine Learning.

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.

Determining a Structural Distortion and Anion Ordering in La2Si4N6C via Computation and Experiment.

A structural instability in the orthorhombic carbonitridosilicate La2Si4N6C arises when calculating the ab initio phonon dispersion curves. The presence of imaginary modes indicates the compound reported in space group Pnma is dynamically unstable with the eigenvectors showing a monoclinic distortion pathway leading to space group P21/c. Synthesizing La2Si4N6C using a high-temperature route and conducting a co-refinement with high-resolution synchrotron X-ray and neutron powder diffraction shows the predicted peak splitting confirming the predicted lower symmetry crystal structure. Further, the combination of ab initio computation, neutron diffraction, and a total scattering analysis based on a neutron pair distribution function analysis supports that the anions are fully ordered and that carbon is only found on the central position of a star-shaped C(SiN3)4 unit. These results illustrate the power of combining computation and experiment to unequivocally solve crystal structures from polycrystalline powders.