Accelerated discovery of two crystal structure types in a complex inorganic phase field

Abstract

The discovery of new materials is hampered by the lack of efficient approaches to the exploration of both the large number of possible elemental compositions for such materials, and of the candidate structures at each composition. For example, the discovery of inorganic extended solid structures has relied on knowledge of crystal chemistry coupled with time-consuming materials synthesis with systematically varied elemental ratios. Computational methods have been developed to guide synthesis by predicting structures at specific compositions and predicting compositions for known crystal structures, with notable successes. However, the challenge of finding qualitatively new, experimentally realizable compounds, with crystal structures where the unit cell and the atom positions within it differ from known structures, remains for compositionally complex systems. Many valuable properties arise from substitution into known crystal structures, but materials discovery using this approach alone risks both missing best-in-class performance and attempting design with incomplete knowledge. Here we report the experimental discovery of two structure types by computational identification of the region of a complex inorganic phase field that contains them. This is achieved by computing probe structures that capture the chemical and structural diversity of the system and whose energies can be ranked against combinations of currently known materials. Subsequent experimental exploration of the lowest-energy regions of the computed phase diagram affords two materials with previously unreported crystal structures featuring unusual structural motifs. This approach will accelerate the systematic discovery of new materials in complex compositional spaces by efficiently guiding synthesis and enhancing the predictive power of the computational tools through expansion of the knowledge base underpinning them.

A map of the inorganic ternary metal nitrides

“…Using the multifeature distance matrix between the various elements, we calculate the linkages using the 'average' method (UPGMA algorithm) and construct a dendrogram with optimal leaf ordering. 23 We manually re-arranged the leaves of the dendrogram so as to order the elemental axes from left to right predominantly by Stable (Blue) → Metastable w.r.t binaries (Green) → Metastable w.r.t elements (Red). The resulting 1-D ordering of elements is set as the axis for the multi-feature clustermap.…”

Ca2PbGa8O15: Rational Design, Synthesis, and Structure Determination of a Purely Tetrahedra‐Based Intergrowth Oxide

“…Recently, Rosseinsky's group has developed an efficient strategy to explore new layered structures, including Y 2.24 Ba 2.28 Ca 3.48 Fe 7.44 Cu 0.56 O 21 , Y 0.038 Sr 0.320 Ca 0.848 Ga 0.794 O 2.416 , and (Y, Sr) 1.02 Ca 1.48 Ga 3 O 7 ) with the assistance of computational approaches. By using Extended Module Materials Assembly (EMMA) and/or Monte Carlo‐EMMA method, the most possible structures with normal chemical geometries could be obtained from a large number of hypothetical intergrowth structures constructed by several layered modules.…”

“…As shown in Figure c, Ca‐O distances are all in the normal range, but it is very rare that Ca 2+ is coordinated by only 5 oxygens in a distorted trigonal bi‐pyramidal cavity . Pb 2+ is also surrounded by 5 oxygens with three short and two long bond lengths, and locate in an obvious semispherical cavity due to the highly stereochemically active 6s 2 lone pair electrons.…”

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

“…To our knowledge, this is the largest set of molecules that has been subjected to CSP and prop- Figure 1: The known (1) and hypothetical (2-28) molecules studied. Symmetrical (C 2v ) isomers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) are colored in orange, to contrast with the asymmetric (C s ) isomers (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28).…”