GBClaw：面向原子尺度晶界研究的智能体框架
GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery
一个能把 LLM 和材料模拟串起来干活的智能体框架，在镍的 14 种晶界上自己规划模拟、找缺陷、分析变形机制，做材料计算的朋友可以看看思路。
arXiv 论文提出 GBClaw，一个用于研究一般晶界结构与变形的智能体框架，将 LLM 集成到包含编排器、专用 agents、领域工具和分层记忆的模块化架构中。该框架通过动态重规划把系统模拟与机理分析连接起来。在镍的 14 个一般非对称倾斜晶界上，GBClaw 刻画了界面缺陷结构并识别出多种变形模式，发现有无二次晶界失配位错的边界滑移在剪切强度上差异显著。研究者引导的后续计算还将异常高的剪切阻力追溯到亚稳边界结构。
GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery
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 simulations with targeted mechanistic analysis. Applied to 14 general asymmetric tilt GBs in nickel, GBClaw characterized interfacial defect structures and identified diverse deformation modes across these incommensurate boundaries. Comparing boundary sliding with and without secondary GB misfit dislocations revealed substantial differences in shear strength, underscoring the critical role of defect hierarchies in sliding resistance. Furthermore, researcher-guided follow-up calculations traced an anomalously high shear resistance to metastable boundary structures. This work demonstrates that combining agentic AI with domain expertise bridges high-throughput computational screening with deep physical understanding of complex material interfaces.
