Artificial Intelligence (AI) is becoming a foundational enabler in computational mechanics, offering transformative capabilities for the design, analysis, and optimization of materials and structures across multiple length and time scales. This symposium aims to highlight cutting- edge developments in integrating AI into computational workflows, from atomistic modeling and mesoscale simulations to continuum mechanics and system-level behavior. By leveraging data-driven models, machine learning, and physics-informed approaches, researchers are accelerating the discovery of novel materials, enabling the inverse design of complex structures, and bridging gaps between simulation and experiment. The rise of autonomous design loops, uncertainty-aware models, and real-time data assimilation is also paving the way for the development of digital twins, virtual replicas of materials and structures that evolve with their physical counterparts. Contributions are invited across a broad range of topics including surrogate modeling, multiscale and multiphysics learning, high-throughput simulation, generative design, and AI-enhanced optimization. The symposium welcomes theoretical advances, application-driven studies, and infrastructure tools that push the boundaries of how AI can inform, accelerate, and transform computational solid/structural mechanics and materials design. Interdisciplinary work that connects materials science, mechanics, data science, and applied mathematics is particularly encouraged.