Digital Twin-Based Module for the Analysis of the Mechanical Response of Crystalline Materials

This paper presents the development of a digital module designed for the storage and analysis of mechanical response characteristics in crystalline materials. The module processes the stiffness tensor obtained from structural models derived through quantum-chemical methods, incorporating periodic boundary conditions. It includes comprehensive data on the mechanical properties of organic crystalline compounds, highlighting significant features such as elastic moduli, directions of maximum and minimum compressibility, and negative linear compressibility. The collected data undergoes thorough processing and analysis, focusing on the distribution of critical elastic modulus values. This dataset is prepared for future processing using artificial intelligence techniques, enabling the construction of predictive models and enhancing the exploration of “structure-property” relationships. Additionally, an algorithm for visualizing spatial dependencies of Young's modulus, linear compressibility, linear stiffness, and uniaxial compressibility has been implemented, allowing for adjustable resolution. This visualization is integrated with atomic-molecular systems, including periodic structures, to elucidate the relationship between mechanical response and crystal structure. A user-friendly interface has also been developed for loading and storing raw data, facilitating the analysis of mechanical response characteristics derived from quantum-chemical calculations. Configuration files for the web server container have been created to support continuous software integration and deployment on new platforms. This innovative digital module represents a significant advancement in the field of materials science, contributing to improved understanding and optimization of crystalline materials for various applications.