Tatiana G. Mitina

Bio: Tatiana G. Mitina is an academic researcher from Samara State University. The author has contributed to research in topics: Network topology & Coordination polymer. The author has an hindex of 2, co-authored 3 publications receiving 301 citations. Previous affiliations of Tatiana G. Mitina include King Abdulaziz University.

Entangled Two-Dimensional Coordination Networks: A General Survey

Abstract: Survey Lucia Carlucci,*,† Gianfranco Ciani,† Davide M. Proserpio,*,†,‡ Tatiana G. Mitina,‡ and Vladislav A. Blatov*,‡,§ †Dipartimento di Chimica, Universita ̀ degli Studi di Milano, Via C. Golgi 19, 20133 Milano, Italy ‡Samara Center for Theoretical Materials Science, Samara State University, Ac. Pavlov Street 1, Samara 443011, Russia Chemistry Department, Faculty of Science, King Abdulaziz University, Post Office Box 80203, Jeddah 21589, Saudi Arabia

Topology of 2-Periodic Coordination Networks: Toward Expert Systems in Crystal Design

Abstract: We have performed comprehensive topological analysis of 2-periodic coordination networks in 10 371 metal–organic compounds. Both local and overall topologies of complex groups were determined, classified, and stored in the electronic databases. Two plane nets, square lattice (sql) and honeycomb (hcb), were found to compose two-thirds of all the coordination networks. Strong correlations were found between local topological characteristics (coordination numbers of atoms or complex groups, coordination figures, formalized coordination modes of ligands and coordination formula) and the overall topology that in many cases allowed us to predict possible topological motifs from the data on chemical composition with high probability. The possibility to develop an expert system that could envisage local and overall topology of periodic coordination networks is discussed, and an example is given of how such a system can work.

Topological systematization of layered coordination compounds of Cu, Ag, Zn, and Cd

Abstract: The topological analysis and systematization of basis lattices in 1972 coordination compounds of Cu, Ag, Cd, and Zn containing organic ligands and layered complex groups (both isolated and interlaced with each other) are carried out by the TOPOS structural topological program package. The structures considered are most frequently based on the square (sql) or hexagonal (hcb) Shubnikov lattice (38.6 and 18.7%, respectively). The increased specificity of the sql and hcb lattices is due to specific features of coordination of the lattices facilitating their mutual penetration. Two types of interlacing of the layered groups (parallel and inclined) are revealed, and the former predominates.

Applied Topological Analysis of Crystal Structures with the Program Package ToposPro

Abstract: Basic concepts of computer topological analysis of crystal structures realized in the current version of the program package ToposPro are considered. Applications of the ToposPro methods to various classes of chemical compounds—coordination polymers, molecular crystals, supramolecular ensembles, inorganic ionic compounds, intermetallics, fast-ion conductors, microporous materials—are illustrated by many examples. It is shown that chemically and crystallographically different structures can be automatically treated in a similar way with the ToposPro approaches.

Recent advances and applications of machine learning in solid-state materials science

Abstract: One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

Universal fragment descriptors for predicting properties of inorganic crystals

Abstract: Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electro…

Recent progress in metal-organic frameworks as active materials for supercapacitors

Abstract: Metal-organic frameworks (MOFs), also quoted as porous coordination polymers (PCPs), are causing great concern in supercapacitors (SCs) field owing to their ultra-high surface-areas, tailorable pore-sizes and shapes, and diverse structural architectures. This review mainly focuses on the recent progress in various branches of MOFs materials including porous coordination networks, two-dimensional (2D) MOFs, entangled MOFs, polyoxometalate MOFs (POMOFs), heterometallic MOFs, and some new emerging MOFs, as well as their applications in SCs. The superiority and the deficiency of various MOF types were systematically introduced and summarized. Additionally, the challenges and perspectives relate to pristine MOFs and MOFs-based composites for the applications in SCs have also been discussed. We hope that our review could provide guiding frameworks to design and fabricate MOFs materials with more practical energy-storage applications.

Two-Dimensional Metal-Organic Framework Materials: Synthesis, Structures, Properties and Applications.

Abstract: Among the recent developments in metal-organic frameworks (MOFs), porous layered coordination polymers (CPs) have garnered attention due to their modular nature and tunable structures. These factors enable a number of properties and applications, including gas and guest sorption, storage and separation of gases and small molecules, catalysis, luminescence, sensing, magnetism, and energy storage and conversion. Among MOFs, two-dimensional (2D) compounds are also known as 2D CPs or 2D MOFs. Since the discovery of graphene in 2004, 2D materials have also been widely studied. Several 2D MOFs are suitable for exfoliation as ultrathin nanosheets similar to graphene and other 2D materials, making these layered structures useful and unique for various technological applications. Furthermore, these layered structures have fascinating topological networks and entanglements. This review provides an overview of different aspects of 2D MOF layered architectures such as topology, interpenetration, structural transformations, properties, and applications.