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AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis
Powder X-ray diffraction (XRD) is central to materials characterization, yet reliable end-to-end automation remains challenging. An XRD agent must interpret diffraction evidence, operate refinement software, manage coupled parameters in a defensible order, and distinguish numerical improvement from physical validity. In this paper, we propose AutoXRD, an autonomous large language model (LLM) agent framework that organizes powder-XRD analysis as stepwise refinement, grounds actions in observed evidence, and applies deterministic crystallographic and physical checks before accepting results. We
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-30T18:05:21.000Z
First collected: 2026-09-21T07:22:03.933Z. This is not the publication date.