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arXiv · 2607.08890

Hybrid DiffractGPT-Rietveld Refinement Framework for Automated X-ray Diffraction Analysis

Charles Rhys Campbell·Justin Ely·Jaehyung Lee·Frank M. Abel·Kamal Choudhary
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Abstract

X-ray diffraction (XRD) is fundamental to structural materials characterization, yet transforming a raw powder pattern into a refined crystal structure still demands considerable domain expertise. We present AGAPI-XRD, a hybrid framework integrating DiffractGPT generative structure prediction, database pattern matching against JARVIS-DFT and COD, and automated Rietveld refinement and ALIGNN-FF relaxation through a unified API at https://atomgpt.org/xrd. First, we used the AGAPI-XRD pipeline to evaluate the crystal structure of a variety of minerals in the RRUFF database that were experimentally characterized using powder x-ray diffraction. Next, we benchmarked the lattice parameter prediction fidelity of the AGAPI-XRD pipeline using a subset of the Alexandria PBE-hull dataset and the subset of RRUFF minerals that have known lattice parameters. AGAPI-XRD returns valid lattice parameters for 79.7\% of the RRUFF benchmark minerals and for 94.8--98.1\% of the Alexandria subset, while identifying a candidate structure for 93.8\% of RRUFF minerals. For this benchmark, pattern matching delivers the highest accuracy for known phases, while DiffractGPT extends structure generation to complex materials absent from existing databases. Together, AGAPI-XRD advances accessible, end-to-end automated crystal structure determination from powder XRD data.

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Charles Rhys Campbell, Justin Ely, Jaehyung Lee, Frank M. Abel, Kamal Choudhary. 2026-07-09. Hybrid DiffractGPT-Rietveld Refinement Framework for Automated X-ray Diffraction Analysis. https://arxiv.org/abs/2607.08890

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