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

Key points

• 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.1
• Applied to 14 general asymmetric tilt GBs in nickel, GBClaw characterized interfacial defect structures and identified diverse deformation modes across these incommensurate boundaries.1
• 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.1
• This work demonstrates that combining agentic AI with domain expertise bridges high-throughput computational screening with deep physical understanding of complex material interfaces.1

Sources (1)

• [1]GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:59 PM

  Here, we present GBClaw, an agentic AI framework for investigating the structure and deformation of general grain boundaries (GBs).

  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.

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