The rapid evolution of computational materials science is marked by significant advances in high-throughput simulations, machine learning (ML) models, and the extensive use of large-scale databases. As researchers continue to push the boundaries of materials discovery and design, the need for robust, flexible, and scalable computational tools becomes increasingly apparent. Server-based platforms such as Google