Session

DescriptionAdvances in agentic AI—autonomous, tool-using systems that plan, call simulators, reason over uncertainty, and adapt—are poised to transform how we explore vast chemical and materials design spaces. This minisymposium will showcase state-of-the-art methods that couple agentic decision-making with scalable HPC workflows to accelerate hypothesis generation, simulation throughput, and autonomy while strengthening scientific trust. Topics span agent-driven hypothesis generation and assessment, automatic execution of large computational campaigns based on different simulation scales, and workflow/runtime systems that autonomously schedule thousands to millions of tasks across heterogeneous supercomputers with robust provenance and reproducibility. A central thread is Building Trust in Science through HPC Co-Design: contributors will detail how agents, numerical methods, software stacks, data services, are co-designed to deliver validated, reproducible, and/or auditable results using autonomous loops.