Organizations: Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA · Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, The University of Osaka, Osaka, 560-8531, Japan · Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA · Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA · School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
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.
Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Physics and Astronomy, West Virginia University, Morgantown, WV 26506, USA
May 12, 2026·Mouyang Cheng, Bowen Yu, Chu-Liang Fu +14Materials Science
Quantum Measurement Group, MIT, Cambridge, MA 02139, USA · Center for Computational Science and Engineering, MIT, Cambridge, MA 02139, USA · Department of Materials Science and Engineering, MIT, Cambridge, MA 02139, USA +9
Materials Artificial Intelligence Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China · University of Chinese Academy of Sciences, Beijing 100049, China · Institute for Advanced Study, Chengdu University, Chengdu 610106, Sichuan, China. +1