AI and the World's Hard Problems · Chemistry & materials · arXiv · 2026-10-21
GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery
General grain boundaries make up the vast majority of real interfaces in polycrystalline materials but remain understudied because of their structural complexity and interfacial mismatch. GBClaw integrates large language models into a modular architecture of an orchestrator, specialized agents, domain-specific tools, simulation interfaces, and hierarchical memory, using dynamic replanning to connect systematic simulations with mechanistic analysis. Applied to 14 general asymmetric tilt grain boundaries in nickel, it characterized interfacial defect structures and diverse deformation modes, and researcher-guided follow-up calculations traced an anomalously high shear resistance to metastable boundary structures.