The modification of boron-carbon nanotubes by functional groups is relevant in connection with the intensive development of the nano-industry, in particular, nano- and microelectronics. The thus modified nanotube can be used as an element of a sensor device for detecting micro amounts of various substances, for example metals included in salts and alkalis. The possibility of creating a highly effective sensor based on a single-layer boron-carbon ВС₅ nanotube with a surface modified functional amine group (—NH₂) is being discussed in this paper. Results of quantum-chemical studies showed that the functional amine group connecting to the boron-carbon nanotube (BCNT) type (6, 0) at a distance of 0.16 nm (when modified to both a surface carbon atom and a boron atom), and to BCNT type (6, 6) — at a distance of 0.16 nm when the group connecting to the carbon atom and 0.17 nm when connecting to the boron atom, which indicates the emergence of a chemical bond between the investigated BCNT and the amine group. The results of computer simulation of interaction between surface-modified ВС₅ nanotube and alkali metal atoms (lithium, sodium, potassium) to be initialized are presented. The sensory interaction of the modified boron-carbon nanosystem with metal atoms is investigated, at which the selected atoms are identified at a certain distance. When reacting with alkali metal atoms in the BC₅ + NH₂ complex, it increases the number of carriers due to the transfer of electron density from metal atoms to modified BCNT. The results presented in this paper were obtained within the molecular cluster model by quantum-chemical calculations using the calculating DFT method with exchange-correlation functionality B3LYP (valence-split basis set 6-31G). It has been shown that the amine group modified boron-carbon ВС₅ nanotube shows a sensory response to the above alkali metal atoms and can be used as an element of the sensor device. 

Triple FeCoNi nanoparticles distributed and stabilized in the carbon matrix of FeCoNi/C metal-carbon nanocomposites were synthesized. The synthesis of nanocomposites was carried out by controlled IR pyrolysis of precursors of the "polymer-metal nitrates" type, obtained by joint dissolution of the components with subsequent removal of the solvent. The effect of the synthesis temperature on the structure, composition, and electromagnetic properties of nanocomposites has been studied. It was shown by XRD that the formation of ternary FeCoNi nanoparticles occurs through the dissolution of Fe in the nanoparticles of the NiCo solid solution. With an increase in the synthesis temperature, the size of metal nanoparticles increases, which is determined by the processes of their agglomeration and coalescence during matrix rearrangement. Also, depending on the synthesis temperature, nanoparticles of a ternary alloy with different compositions can be formed, and the ratio of metals specified in the precursor is achieved at 700 °C. By Raman spectroscopy was shown  that, with an increase in the synthesis temperature, the degree of crystallinity of the carbon matrix of nanocomposites increases, and graphene structures consisting of several layers can be formed. The frequency dependences of the relative complex dielectric and magnetic permeabilities of nanocomposites in the range of 3–13 GHz were studied. It is shown that an increase in the synthesis temperature causes a significant increase in both dielectric and magnetic losses (~ 2 times). The former are associated with the formation of a complex nanostructure of the carbon matrix of the nanocomposite, while the latter are determined by an increase in the size of nanoparticles and a shift of the EFMR frequency to the low-frequency region. Reflection loss (RL) calculations were performed according to the standard procedure based on experimental data of the frequency dependences of the complex magnetic and dielectric permittivity. It was shown that control of the frequency range and absorption value of electromagnetic waves (from 50 to 94%) can be carried out by changing the temperature of synthesis of nanocomposites. 

The article discusses the main aspects of the significance of the development of a high-performance environment for scientific research in the conditions of digital transformation, used in solving the problems of synthesis of new materials with desired properties. Some historical facts are given that testify to the enormous role played by Soviet scientists in the creation of computer technology for scientific research. The analysis of the experience of the Federal Research Centre “Information and Control”, RAS in the creation of a modern high-performance platform and its use for scientific research is given. The necessity of its application for solving applied problems related to the selection of new materials in the field of microelectronics is substantiated.

The paper proposes design and circuitry solutions for the implementation of high-performance next generation computers. They are based on self-timed circuit design methodology and provide an increase in the tolerance of computing systems to soft errors resulting from induced noises and radiation exposure.

The paper deals with the formation of system interfaces of a digital platform for managing the process of providing scientific services, including the integration of scientific services for solving a complex research problem, for example, multiscale modeling of the properties of new materials or conducting interdisciplinary research. On the basis of the functional structure of the integration of scientific services of a digital platform (in relation to multiscale modeling) presented in the work, requirements for the system interface are formulated, and the architecture of the system interface for the integration of scientific services of a digital platform is proposed. The proposed model of a system interface for information interaction with services and datasets of a digital platform in management processes for the provision of scientific services is based on modern solutions for managing virtual infrastructure, based on container processing technologies and microservices, as well as container orchestration and communication (service mesh), flexible technologies (agile) integration. Taking into account that the main function of the digital platform is to provide processes for the preparation and conduct of research by formalizing the interaction scenarios of researchers, suppliers (sources) of initial data, consumers of the results, along with tools for supporting system interfaces to the catalogs of the digital platform, the project offers means for organizing the interaction of services, registered on the platform in order to ensure the execution of scientific research. Synchronization of the processes of providing services, ensuring the transfer of data between services and obtaining the final result are also ensured by implementing the control processes of the digital platform, which is based on the proposed system interface. The developed model of system interfaces is new in the work. The proposed interaction interface allows you to effectively consolidate high-performance computing resources and mathematical models based on digital platform technologies. This is especially important for organizing the solution of multiscale modeling problems as a complex of models, each of which operates on the same space-time scale.

The article is devoted to methods of calculation and evaluation of the efficiency of functioning of hybrid computing systems. Material science software systems demonstrate maximum efficiency when operating on hybrid computing systems when using graphics accelerators for calculations. Examples include the VASP (The Vienna Ab initio Simulation Package) and Quantum ESPRESSO software systems. These software systems are most efficient when using monopolistic computing resources: RAM, CPU, GPU.
When operating a hybrid high-performance cluster, the problem arises of resource management and their division between a group of users. Technologies need to be developed that ensure the allocation of resources to materials science applications for different users and research teams. The modern approach to organizing the computing process is the use of virtualization and cloud technologies. Cloud technologies enable the provision of SaaS and PaaS services to users. It is advisable to provide scientific teams with applied materials science systems as cloud services.
Such diverse approaches, when applied in a single computer complex, require the development of methods for optimizing the load on the resources of a high-performance complex, assessing the efficiency of using its computational capabilities, and developing methods for improving user programs.
Determining the quality of the complex loading is an important task when providing high-performance computing services to research teams performing interdisciplinary research in various fields of science and technology. The article proposes a method for calculating the value of the load value using the peak performance values of the complex. The results and performance quality of high performance computing cloud scientific services are analyzed using a Roofline model.

Software modules are presented that implement the indexing of scientific articles, subject-oriented search on them, a user interface, as well as a software interface for third-party consumers. A detailed description of the process of setting up full-text search to the level of subject-oriented is given.

In the process of modeling multilayer semiconductor nanostructures, it is important to quickly obtain accurate values the characteristics of the structure under consideration. One of these characteristics is the value of the interaction energy of atoms within the structure. The energy value is also important for obtaining other quantities, such as bulk modulus of the structure, shear modulus etc. The paper considers a machine learning based method for obtaining the interaction energy of two atoms. A model built on the basis of the Gaussian Approximation Potential (GAP) is trained on a previously prepared sample and allows predicting the energy values of atom pairs for test data. The values of the coordinates of the interacting atoms, the distance between the atoms, the value of the lattice constant of the structure, an indication of the type of interacting atoms, and also the value describing the environment of the atoms were used as features. The coordinates of the atoms, the distance between the atoms, the lattice constant of the structure, an indication of the type of interacting atoms, the value describing the environment of the atoms were used as features. The computational experiment was carried out with structures of Si, Ge and C. There were estimated the rate of obtaining the energy of interacting atoms and the accuracy of the obtained value. The characteristics of speed and accuracy were compared with the characteristics that were achieved using the many-particle interatomic potential — the Tersoff potential.

To solve the problems of materials science, including multiscale modeling for the synthesis of materials with specified properties, a modern digital platform for scientific research has been created at the FRC CSC RAS. The digital platform is a combination of a competence center, a high-performance computing complex and a set of scientific services that are provided to researchers in the form of traditional cloud services in software (SaaS), platform (PaaS) and infrastructure (IaaS) services, as well as using specific technologies for providing researchers scientific service as a service (RaaS, Research as a Service).
Other examples of scientific fields for which scientific services are used in conjunction with high-performance computing services are: biomedical chemistry, crystallography, computational linguistics, artificial intelligence.
The article describes the information and computing environment of the sharing research facilities Center for Collective Use “Informatics”, which forms the basis of the instrumental and technological infrastructure for prototyping, as well as the layout of the control system for deterministic scientific services of the digital platform.
The article presents the results of experimental studies carried out in relation to algorithms for the transfer and intermediate storage of initial data, data exchange services when interacting with a platform user, cloud scientific high-performance computing services, algorithms for the interaction of data exchange adapters when ensuring interaction between the platform, which are relevant in solving problems of multiscale modeling for the synthesis of materials with desired properties.
The results obtained allow us to evaluate the practical aspects of the functioning of a digital platform for scientific research, designed for the effective organization of scientific research and management of the scientific instrument base in the interests of a wide range of research teams and industrial consumers.

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