GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery
[cond-mat.mtrl-sci] Autorzy: Yazhuo Liu, Kunqing Ding, Xiaoguo Gong i 8 więcej
Agentic artificial intelligence (AI) systems hold immense potential for automating complex workflows in materials discovery and design, but extending them to domain-intensive mechanistic investigations remains challenging. Here, we present GBClaw, an agentic AI framework for investigating the structure and deformation of general grain boundaries (GBs). Although general GBs constitute the vast majority of real-world interfaces in polycrystalline materials, they remain understudied because their structural complexity and interfacial mismatch complicate experimental and modeling research. GBClaw integrates large language models (LLMs) into a modular architecture comprising an orchestrator, specialized agents, domain-specific tools, simulation interfaces, and hierarchical memory. Through dynamic replanning and iterative workflow refinement, the framework effectively connects systematic simu…
- Kategoria arxiv
- cond-mat.mtrl-sci
- Kategoria
- cond-mat.mtrl-sci