Search

Summary This work presents an approach to aid the discovery of inorganic solids by highlighting regions of underexplored yet likely productive composition space using machine learning. A support vector regression algorithm was constructed to determine a compound's formation energy based solely on chemical composition using 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies were then used to construct zero-kelvin convex hull diagrams and identify compositions on the hull and +50 meV above the convex hull. Using this methodology, Y-Ag-Tr (Tr = B, Al, Ga, In) ternary diagrams were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation of the predictions. A particularly promising but unexplored region in the Y-Ag-In diagram was identified, and the ensuing solid-state synthesis produced YAg0.65In1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In and confirm the crystal structure solution.

Polar intermetallics are an intriguing class of compounds with complex relationships between composition and structure that are not fully understood. This work reports a systematic study of the underexplored ternary composition space RE–Au–Tt (RE = La, Ce, Pr, Nd, Ho; Tt = Ge, Sn, Pb) to expand our knowledge of the intriguing chemistry and diversity achievable with these metallic constituents. These composition spaces are particularly interesting because of the potential to find Au-bearing, highly polar intermetallic compounds. The elements were first reacted through arc welding under an inert atmosphere followed by annealing at 850°C. X-ray diffraction of the products identified unreported eight compounds ranging from the simple NaTl-type compounds La1.5Au2Pb0.5, Nd2-xAu2Pbx, and Ho2-xAu2Snx, to the more structurally complex La5AuPb3 in the Hf5CuSn3-type structure and Pu3Pd4-type RE3Au3Ge (RE = La, Ce, Pr, Nd). First-principles electronic structure calculations revealed that a combination of Fermi surface-Brillouin zone interactions, electrostatic interactions, and delocalized metallic bonding contributes to the formation of these phases. These calculations show that a mixture of electrostatic and metallic bonding plays a dominant role in these phases. The RE–Au–Tt composition space remains full of potential for discovering materials with relevant magnetic and quantum properties, provided the crystal chemistry can be comprehended.

Mo2–xWxBC 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–xWxBC (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. More...

The existence of two compounds with the formula A3Ir (A = Rb, Cs) were predicted to form at high pressure by constructing convex hulls for the Rb−Ir and Cs−Ir binary systems. These phases were identified using a nonbiased automatic structure search method based on Density Functional Theory accompanied by a particle swarm optimization (PSO) algorithm. Increasing the applied pressure above ≈10 GPa indicated a favorable formation enthalpy ( Δ H ) for Rb3Ir with the Cu3Ti structure type (space group Pmnm; No. 59) and Cs3Ir in the Ni3Ta structure type (space group P21/m; No. 11). Phonon calculations showed both compositions are dynamically stable in these structure types. Electronic structure calculations revealed that Rb3Ir is a semiconductor with a ≈1.3 eV band gap, calculated using a hybrid exchange-correlation functional, whereas the Fermi level for Cs3Ir falls into a deep pseudogap, suggesting poor metallic character. The chemical bonding in both phases is dominated by the A–Ir (A = Rb, Cs) interactions with the contacts more significant in Cs3Ir than in the Rb3Ir crystal structure. These high-pressure phases lastly showed unusual oxidation states on the transition metal. The charge on Ir is calculated to surpass −1 due to charge transfer between the alkali metal and iridium, implying the presence of an iridide anion at high pressure.

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 work presents an approach to aid the discovery of novel inorganic solids by highlighting regions of underexplored, yet likely productive composition space using machine learning. A support vector regression (SVR) algorithm was constructed first to determine a compound’s formation energy (∆𝐸𝑓,SVR) based solely on chemical composition using data from 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies (r2 = 0.94; MAE = 8.5 meV/atom) were then used to construct zero-kelvin convex hull diagrams and identify compositions immediately on the hull, as well as +50 meV above the convex hull to capture potential compounds that are considered energetically unfavorable but that are still experimentally accessible. Using this methodology, four ternary composition diagrams, Y−Ag−Tr (Tr = B, Al, Ga, In), were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation for the predictions. A particularly promising but unexplored region in the Y−Ag−In diagram was identified, and the ensuing solid-state high-temperature synthesis produced YAg0.65In1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In as well as confirm the crystal structure solution. Our combination of machine learning, inorganic synthesis, and computational modeling describes a new avenue where data-centric models and computation play a critical role in supporting the experimental examination of unexplored phase diagrams.

During a systematic search of the RE-Au-Sn (RE = La, Ce, Pr, Nd) ternary phase space, a series of compounds with the general formula REAu1.5Sn0.5 have been identified. These phases can be synthesized by arc melting the elemental metals, followed by annealing. The crystal structures were solved using single-crystal X-ray diffraction, with the composition confirmed by energy-dispersive X-ray spectroscopy. All four compounds crystallize in orthorhombic space group Imma with the CeCu2-type structure. Most notable in these compounds is the polyanionic backbone composed of a single statistically mixed Au/Sn position, which creates a puckered hexagonal bonding network separated by the rare-earth atoms. Electronic structure calculations indicate that the Au 5d bands are dominant in the density of states, while the crystal orbital Hamilton population (-COHP) curves demonstrate Au-Au and Au-Sn interactions, which stabilize the crystal structure. Likewise, a qualitative electron localization function analysis supports the existence of a polyanionic network, and a Bader charge analysis implies anionic character on Au and Sn. The preference for these compounds to adopt the simple CeCu2-type structure is also determined using density functional theory calculations and compared to related compounds to establish a better picture of the unusual behavior of Au in polar intermetallic compounds.

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.

During a systematic search of the RE-Au-Sn (RE = La, Ce, Pr, Nd) ternary phase space, a series of compounds with the general formula REAu