Recently there are a lot of effective and successful applications of artificial intelligence (AI) methods in materials science for analysis and characterization of materials properties, microstructure analysis, building of processing-structure-properties-performance relationships, development of new materials, etc. The most recent research activities in this direction have been reported during the first TMS World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022) which took place in April 2022 in USA. Despite numerous successful AI-applications in material science and engineering, where most of them are available in form of separated Python/R/Matlab scripts on local PCs, servers, GitHub repository of each single research group, there is a need for the integration of existing data and developed AI-methods into an information system for further materials analysis and design. Such an approach for the management and analysis of expensive and time consuming experimental and simulation data would not only optimize a collaborative work between research groups and their partners, but also to establish an important basis for effective knowledge management. Moreover, an application of integrated information system would guarantee to industrial research partners a desirable security access to their experimental data. In this work we will present a solution for the integration of the developed AI-driven microstructure-property-relations into EDA information system and demonstrate its usage in two running research projects.