ArticleJournal of applied crystallography2026
Automation of Rietveld refinement through machine learning.
Article in Journal of applied crystallography, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Rietveld refinement is a widely used technique for determining crystal structures from powder X-ray diffraction data. Despite its broad applicability, the refinement process often requires substantial manual effort and expert knowledge, which limits the pace of materials research. Here, we introduce a methodology based on convolutional neural networks that enables automated Rietveld refinement. A systematically generated training dataset, which incorporates diverse structural and profile parameters, allows the network to capture complex pattern-structure relationships effectively. A refined crystal structure is directly extracted from the experimental powder X-ray diffraction pattern in a single inference step. The approach is validated using benchmark datasets of CeO
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