A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference. The final model prediction sensitivity, specificity, and accuracy were 97.3%, 93.9%, and 96.9%, respectively, establishing that this method is capable of reliably predicting the crystal structure given only its composition. The power of CR-FS and SVM classification is further demonstrated by segregating the crystal structure of polymorphs, specifically to examine polymorphism in TiNiSi- and ZrNiAl-type structures. Analyzing 19 compositions that are experimentally reported in both structure types, this machine-learning model correctly identifies, with high confidence (>0.7), the low-temperature polymorph from its high-temperature form. Interestingly, machine learning also reveals that certain compositions cannot be clearly differentiated and lie in a "confused" region (0.3-0.7 confidence), suggesting that both polymorphs may be observed in a single sample at certain experimental conditions. The ensuing synthesis and characterization of TiFeP adopting both TiNiSi- and ZrNiAl-type structures in a single sample, even after long annealing times (3 months), validate the occurrence of the region of structural uncertainty predicted by machine learning.

Mo2–x W x BC is suggested to be one of the only exceptionally high hardness, transition-metal-rich materials that also shows moderate ductility and compositional sustainability. This is demonstrated here through the synthesis of the Mo2–x W x BC (x = 1.1, 0.75, 0.5, 0.25, 0) solid solution and structural characterization using X-ray diffraction, electron microscopy, and density functional theory. All compounds crystallize in the orthorhombic space group, Cmcm, and follow Vegard’s law. Vickers microindentations show a decrease in hardness as tungsten is substituted by molybdenum owing to changes in the crystal chemistry and the loss of electron density. Calculating Pugh’s ratio based on the values derived from density functional perturbation theory reveals that these materials are surprisingly ductile throughout the solid solution, providing the potential to manipulate the hardness and ductility. Controlling this relationship is of great technological interest as most hard materials suffer from brittleness. Moreover, evaluating the elemental scarcity and economic indicators such as the Herfindahl–Hirschman index demonstrates the sustainability of the solid solution relative to other high-hardness, transition-metal-rich materials. The ability to fine-tune the mechanical properties for any application by varying the ratio of the transition metals while optimizing their sustainability is undoubtedly of significant industrial interest.

The first strontium borosulfate Sr[B2O(SO4)3] and a novel lead borosulfate Pb[B2O(SO4)3] were obtained by solvothermal reaction of the respective anhydrous metal chlorides MCl2 (M = Sr, Pb) with H[B(HSO4)4] at 300 °C. The crystal structure of Sr[B2O(SO4)3] [Pnma, Z = 4, a = 1657.38(27) pm, b = 1203.68(19) pm, c = 439.484(8) pm] is isotypic with Ba[B2O(SO4)3] and consists of chains, built up by three membered rings of two borate tetrahedra and a sulfate tetrahedron. These rings are further connected via corner‐sharing sulfate tetrahedra and hence can be classified as loop branched zweier double chains. Pb[B2O(SO4)3] crystallizes in a new structure type [P21/m, Z = 2, a = 440.00(2) pm, b = 1210.19(5) pm, c = 860.43(4) pm, β = 103.587(2) °] closely related to Sr[B2O(SO4)3]. Both structures share the common supergroup Pnmm and basically differ by the orientation of adjacent anionic chains. The coordination surrounding of Pb2+ indicates a lone pair activity and DFT calculations confirmed a weak polarizability. Moreover, the compounds were characterized by electrostatic calculations, vibrational spectroscopy and thermal analysis and broaden the structural and chemical diversity of borosulfates.

Intermetallics adopt an array of crystal structures, boast diverse chemical compositions, and possess exotic physical properties that have led to a wide range of applications from the biomedical to aerospace industries. Despite a long history of intermetallic synthesis and crystal structure analysis, identifying new intermetallic phases has remained challenging due to the prolonged nature of experimental phase space searching or the need for fortuitous discovery. In this Minireview, new approaches that build on the traditional methods for materials synthesis and characterization are discussed with a specific focus on realizing novel intermetallics. Indeed, advances in the computational modeling of solids using density functional theory in combination with structure prediction algorithms have led to new high‐pressure phases, functional intermetallics, and aided experimental efforts. Furthermore, the advent of data‐centered methodologies has provided new opportunities to rapidly predict crystal structures, physical properties, and the existence of unknown compounds. Describing the research results for each of these examples in depth while also highlighting the numerous opportunities to merge traditional intermetallic synthesis and characterization with computation and informatics provides insight that is essential to advance the discovery of metal‐rich solids.

This research combined machine-learning methodology, first-principles calculations, and solid-state synthesis to discover novel inorganic compounds. A machine-learning model was developed to predict the DFT-calculated formation energy of compounds as an essential factor in their thermodynamical stability. This approach was then validated by studying four ternary composition diagrams, Y-Ag-Tr (Tr = B, Al, Ga, In), leading to the discovery of YAg 0.65 In 1.35 . The success of this work is to accelerate materials discovery by directing synthesis efforts.