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 further introduce XRDBench with two complementary tracks. XRDBench-QA contains 100 bounded diagno...
| Authors | Yuetong Wu, Maojun Sun |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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