Vladislav A. Blatov

Electrides have been largely restricted to electron-rich compositions, leaving the applications of electron–neutral electrides scarcely explored and poorly understood. Here we report the discovery of an electron–neutral ternary electride, LiAlGe, which undergoes an unusual pressure-driven evolution: a semiconducting, nonelectride F4̅3m-LiAlGe phase at ambient conditions transforms into metallic electride phases, F4̅3m- (6.4–13.7 GPa) and Cmmm-LiAlGe phase (13.7–40.0 GPa). First-principles calculations further predict superconductivity in both high-pressure phases. To extend this concept, we combined high-throughput computation with machine learning (ML) to identify ten additional electron–neutral electrides adopting the LiAlGe-type topology. ML analysis reveals that the cooperative interplay between distinct cation species and their atomic radii is a key descriptor governing electride formation in ternary neutral systems. We synthesized LiAlGe and experimentally verified its high-pressure behavior. Synchrotron X-ray measurements confirmed the predicted F4̅3m to Cmmm transition at 14.3 GPa, in excellent agreement with the calculated 13.7 GPa. Importantly, both high-pressure phases were found to be superconducting under appropriate pressures, validating the theoretical predictions and establishing electron–neutral electrides as a previously overlooked class of superconducting materials.

We present a new algorithmic approach to systematically identify and topologically classify rod-shaped metal–organic frameworks (MOFs). We propose a formal definition of rod unit to be used for high-throughput large-scale computer analysis of MOFs. The definition is based on comparing the sizes of atomic cycles over the framework and discrimination of intra- and inter-rod connections. With this definition, we developed a workflow, which enabled us to identify 6419 rod MOFs in the Cambridge Structural Database. We propose a three-level hierarchical classification system, which operates the overall framework topology, the rod topology (both full and simplified), and the net topology for unidirectional rod packings. An examination of these levels identified 2149 framework topological types, 790 simplified rod topologies, and 68 rod net motifs. All topological classifications are available at our TopCryst Internet resource. Correlations between metal coordination centers, rod and framework topologies were revealed and described. These correlations can be used for the design of new MOF materials with desired structural and functional properties.

In this study, we applied the natural-tiling model to perform a systematic topological analysis of the free space in 898 highly porous metal–organic frameworks (MOFs), focusing on the classification of natural tiles as minimal closed cages in a framework. The universality of the model, in combination with a rigorous algorithm for the simplification of atomic nets, enabled us to unambiguously build natural tilings across chemically and structurally diverse crystal structures. We revealed that ligand coordination has a negligible effect on the tile volume but significantly influences the tile topological type and the geometry of tile rings. Furthermore, we introduced a stiffness scale of natural tiles based on the sphericity criterion (G3), which is also an integral parameter of tile shape. As a result, we created the Topological Types of Tiles (TTT) collection, a comprehensive database of complete and simplified forms of 2030 natural tiles arranged in 202 topological types. This data set provides a topological basis for reticular chemistry–driven design of novel porous materials. We demonstrated the universality of the tiling model by its application to 105 coordination frameworks with low porosity (0–20%). Overall, the unified formalism of the topological approach ensures algorithmic consistency and reproducibility in describing both atomic networks and their associated free space. Our work demonstrates that the automated processing of crystallographic information enables one to develop new kinds of data sets suitable for material discovery, including machine learning and artificial intelligence applications.

Potassium‐conducting chalcogenides are attracting increasing attention as solid‐state conducting materials for potassium‐ion batteries. In this study, Inorganic Crystal Structure Database screening was performed for the first time to select new promising potassium‐ion conductors among ternary and quaternary sulfides and selenides. From over 853 compounds studied, using a geometrical‐topological analysis of free crystal space, we harvested 541 structures previously unreported as conductive materials in which periodic 1D, 2D, or 3D potassium migration maps were realized. Subsequently, the migration energy of the working cations was calculated for these structures using the bond valence site energy method, which allowed the selection of 37 structures with migration barriers below 1 eV, enabling 2D or 3D migration, and characterized by high crystal chemical stability. Kinetic Monte Carlo modeling then discovered nine structures with ionic conductivity exceeding 10 ⁻⁶ S⋅cm ⁻¹ under standard conditions. For three ordered structures, K ₆ CdSe ₄ , K ₃ GeS ₃ , and K ₄ SnSe ₄ with the highest ionic conductivities, DFT nudged elastic band simulations revealed 3D conductivity maps with migration barriers below 0.35 eV. According to ab initio molecular dynamics simulations, all three compounds exhibited ionic conductivity exceeding 10 ⁻⁵ S⋅cm ⁻¹ at 300 K. Dependencies between migration barriers and parameters of conduction channels were also revealed in the series of isostructural compounds.

With our previously developed method, we generated triply periodic surfaces (TPSs) including 131 minimal surfaces (TPMSs) using crystal structures of 937 metal-organic frameworks (MOFs) as templates. Our approach is based on the topological representation of a MOF structure as a periodic net with the subsequent construction of the natural tiling for the net and generation of TPSs/TPMSs from the faces of the tiles. The TPMSs were classified into nine topological types, three of which were previously unreported. The surfaces were optimized through smoothing and finite-thickness procedures and deposited in a database together with their topological descriptors. Being designed for practical applications, the surfaces are provided in formats compatible with additive manufacturing and standard software tools. Based on some TPMSs we fabricated periodic porous samples and experimentally showed that they can possess better mechanical properties than the corresponding bulky material. This work expands the repertoire of known minimal surfaces and bridges the gap between micro- and macrostructures in materials design and fabrication. The relations between atomic crystalline architectures and materials with regular structure including metamaterials support the ideas of generalized crystallography disseminated by Alan Mackay long ago [1].

We extend the topological representation of crystal structures by introducing the concept of ‘color’ topological type and applied it to 5926 intermetallics. Applications to modeling structural disorder in multicomponent systems are also considered.