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The article discusses methods of consolidating scientific services of a digital platform for integrating a set of scientific services for various fields of science for conducting interdisciplinary research. Solutions for creating consolidated services can be widely used for multilevel, multiscale modeling in the field of materials science, which provides complex modeling at several levels of the hierarchy. Currently, this problem is being solved by creating multicomponent hierarchical

This article discusses a methodology for assessing the effectiveness of a high-performance research platform. The assessment is carried out for the example of the "Informatika" Center for Collective Use (CCU) established at the Federal Research Center of the Institute of Management of the Russian Academy of Sciences, for solving new materials synthesis problems. The main objective of the "Informatika" Center for Collective Use is to conduct research using the software and hardware of the data center of the FRC IU RAS, including for the benefit of third-party organizations and research teams. The general characteristics of the "Informatika" Center for Collective Use are presented, including the main characteristics of its scientific equipment, work organization and capabilities. The hybrid high-performance computing cluster of the FRC CSC RAS (HHPCC) is part of the data center of the FRC IU RAS and also part of the “Informatika” Center for Collective Use. HHPCC provides computing resources in the form of cloud services as software (SaaS) and platform (PaaS) services. With the aid of special technologies, scientific services are delivered to researchers in the form of subject-oriented applications. Based on the analysis of the structure and operation principles of the Informatika Center, key performance indicators of the Center have been developed taking into account its specific tasks in order to characterize its various activity aspects (development, activities and performance). CCU efficiency evaluation implies calculation, on the basis of the developed indicators, of overall (generalized) indicators that characterize the CCU operation efficiency in various areas. An integral indicator is also calculated showing the overall CCU efficiency. To develop the overall performance indicators and the integral performance indicator, it is suggested to use the methods of weighted average and analysis of hierarchies. The procedure of determining partial performance indicators has been considered. Specific features of the choice of CCU performance indicators for solving new materials synthesis problems have been identified that characterize computing complex capabilities in the creation of a virtualization environment (peak performance of a computing system, real performance of a computing system on specialized tests, equipment loading with applied tasks and program code efficiency).

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