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 cons
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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 cons
This paper demonstrates a practical application of agentic AI for complex scientific discovery, moving beyond general-purpose tasks to domain-intensive mechanistic investigations. It highlights how LLMs can be integrated into modular architectures with specialized tools and memory to automate workflows in fields like materials science, which is a growing area for AI application.