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Metal–insulator transition (MIT) compounds are materials that may exhibit metallic or insulating behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. An important subset of MIT materials are those with a transition driven by temperature. The number of thermally driven MIT materials, however, is scarce, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Most research that addresses thermal MITs is limited by the domain knowledge of the scientists to a subset of MIT materials and is often focused on a limited subset of possible features. Here, using a combination of domain knowledge and natural language processing (NLP) searches, we have built a material database comprising thermally driven MITs as well as metals and insulators with similar chemical composition and stoichiometries to the MIT compounds. We featurized this data set using a wide variety of compositional, structural, and energetic descriptors, including two MIT relevant energy scales, the estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then performed supervised classification on this data set, constructing three electronic-state classifiers: metal vs nonmetal (M), insulator vs noninsulator (I), and MIT vs non-MIT (T). This classification allows us to identify new features separating MIT materials from non-MIT materials. These include the 2D feature space consisting of the average deviation of the covalent radius, the range of the Mendeleev number and Ewald energy. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. We then elaborate on other features (GII and Ewald energy) and examine how they affect the classification of binary vanadium and titanium oxides. Last, we implement an online and publicly accessible version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file. The broad accessibility of our database, newly identified features, and user-friendly classifier models will aid in accelerating the discovery of MIT materials.

We study the problem of determining the maximum size of a spherical two-distance set with two fixed angles (one acute and one obtuse) in high dimensions. Let N α,β (d) denote the maximum number of unit vectors in R d where all pairwise inner products lie in {α, β}. For fixed −1 ≤ β < 0 ≤ α < 1, we propose a conjecture for the limit of N α,β (d)/d as d → ∞ in terms of eigenvalue multiplicities of signed graphs. We determine this limit when α + 2β < 0 or. Our work builds on our recent resolution of the problem in the case of α = −β (corresponding to equiangular lines). It is the first determination of lim d→∞ N α,β (d)/d for any nontrivial fixed values of α and β outside of the equiangular lines setting.

We present an innovative fault-tree analysis tool that we developed using a novel software architectural style that we call package-oriented programming (POP). The tool is largely implemented using multiple, tightly integrated, shrink-wrapped software packages as components. These packages are integrated using custom-coded integration mediators into a cohesive superstructure that provides much of the functioning required of many sophisticated modeling and analysis tools. In addition to serving as a proof of concept, the tool provides a test-bed for further research on the approach. PACUGE-OmNTEDPROG-G

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