Recent advances in machine learning (ML), artificial intelligence (AI), and data science are revolutionizing computational mechanics and materials modeling. This mini-symposium aims to provide a platform for researchers working at the intersection of AI and computational science, targeting applications in nano- and quantum materials, fluid dynamics, and complex thermophysical systems. Emphasis is placed on first-principles modeling augmented by data-driven approaches, as well as on emerging paradigms such as thermal reservoir computing, AI-accelerated multiscale modeling, and high-performance computing (HPC) for simulation-based design. Contributions involving novel algorithms, hybrid physics-AI models, and application-driven case studies are strongly encouraged. Topics of Interest Include (but are not limited to):  Machine learning for computational fluid dynamics (CFD)  Thermal reservoir computing and physical neural networks  AI-accelerated simulation of nano- and quantum materials  High-throughput first-principles and data-driven screening  Hybrid ML/physics-based models for multiphysics coupling  High-performance computing with AI for large-scale simulations  Graph neural networks and physics-informed neural networks (PINNs)  Generative models for material structure-property design