A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by diffraction techniques. Compared to alternative approaches, this Heusler discovery engine performs exceptionally well, making fast and reliable predictions of the occurrence of Heusler vs non-Heusler compounds for an arbitrary combination of elements with no structural input on over 400,000 candidates. The model has a true positive rate of 0.94 (and false positive rate of 0.01). It is also valuable for data sanitizing, by flagging questionable entries in crystallographic databases. It was applied to screen candidates with the formula AB 2 C and predict the existence of 12 novel gallides MRu 2 Ga and RuM 2 Ga (M = Ti-Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu 2 Ga, exhibited diagnostic superstructure peaks that confirm the adoption of an ordered Heusler as opposed to a disordered CsCl-type structure.

In this work we present a data-driven approach to the rational design of battery materials based on both resource and performance considerations. A large database of Li-ion battery material has been created by abstracting information from over 200 publications. The database consists of over 16,000 data points from various classes of materials. In addition to reference information, key parameters and variables determining the performance of batteries were collected. This work also includes resource considerations such as crustal abundance and the Herfindahl-Hirschman index, a commonly used measure of market concentration. The data is organized into a free 10 web-based resource where battery researchers can employ a unique visualization method to plot database parameters against one another. This contribution is concerned with cathode and anode electrode materials. Cathode materials are mostly based on an intercalation mechanism, while anode materials are primarily based on conversion and alloying. Results indicate that cathode materials follow a common trend consistent with their crystal structure. On the other hand anode materials display similar behavior, based on elemental composition. Of particular interest is that high energy cathodes are scarcer than high power materials and high performance anode 15 materials are less available. More sustainable materials for both electrodes based on alternative compositions are identified.

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.