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GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery

Yazhuo Liu, Kunqing Ding, Xiaoguo Gong, Zifeng Wang, Yichen Yang, Yifeng Che, Wen Chen, Josh Kacher, Olivier N. Pierron, Shigenobu Ogata, Ting Zhu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28629 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

代理人工智能(AI)系统具有巨大的潜力来自动化材料发现和设计中的复杂工作流程,但将其扩展到域密集的机械研究仍然是具有挑战性的。在这里,我们介绍了GBClaw,一个代理人工智能复杂的框架,用于调查一般谷物边界(GBs)的结构和变形。尽管一般GBs构成了多元化材料中的绝大多数真实界面,但它们仍然被低估,因为它们的结构复杂性和跨面结构不匹配使实验和模型研究变得复杂。GBClaw将大型语言模型(MLLs)整合到一个模块化的架构中,其中包括一个管制器,专门的材料代理,域特定的工具,模拟接口和层级记忆。通过动态复制和它反对物理工作流程的改造,该框架

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

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Topics: Workflow, Grain boundary, Modular design, Computer science, Domain (mathematical analysis), Deformation (meteorology)

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