Agentic AI can tackle domain-specific materials science by combining LLMs with specialized simulation tools and memory systems, enabling discovery of mechanistic insights that require both high-throughput screening and deep physical reasoning.
GBClaw is an AI framework that combines large language models with specialized tools to automatically investigate grain boundaries in materials. The system orchestrates complex simulations and analysis to understand how grain boundaries deform, discovering that defect structures significantly affect material strength—work that would be difficult for humans to do manually.