Newest papers

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

Korean

에이전트 인공 지능 (AI) 시스템은 재료 발견 및 설계에서 복잡한 작업 흐름을 자동화 할 수있는 엄청난 잠재력을 보유하지만 도메인 집중적 인 기계적 조사에 그들을 확장하는 것은 여전히 도전입니다. 여기, 우리는 GBClaw, 일반 곡물 경계 (GBs)의 구조 및 변형을 조사하기위한 에이전트 인공 지능 (AI) 프레임 워크를 제시합니다. 일반 GBs는 polycrystalline 재료의 실제 세계 인터페이스의 대다수를 구성하지만, 그들은 구조적 복잡성과 인터페이스 구조적 불일치가 실험 및 모델링 연구를 복잡하게하기 때문에 여전히 과소 연구되고 있습니다. GBClaw는 대형 언어 모델 (MLLs)을 모듈러 아키텍처

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange