Advanced materials such as composites, metamaterials, soft materials, and architected materials are inherently heterogeneous and multiscale in nature. Currently, multiscale modeling serves as the most effective approach for analyzing and designing these materials. However, the growing complexity of microstructural features and macroscopic structural configurations presents significant challenges to achieving both computational efficiency and predictive accuracy. While emerging machine learning (ML) models offer a cost-effective alternative, their effectiveness is often limited by the lack of high-quality training data in many real-world engineering applications. Moreover, advanced ML techniques are still not routinely incorporated into traditional mechanics or materials engineering curricula. To address these challenges and support both research and education in multiscale material and structural modeling, this project supports research that develops a cloud-based cyberinfrastructure that integrates new multiscale modeling theory with multi-fidelity ML models. This platform seeks to provide open-access tools, curated datasets, and comprehensive training resources to advance materials science, enable efficient structural analysis and design, and support workforce development in ML-assisted material and structural modeling. The goal of this project is to develop a cloud-based, open-source multiscale modeling software called OpenMSG, providing an ultra-efficient prediction to