ArticleNature communications2025
Powder diffraction crystal structure determination using generative models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Visualizing interfacial thermal resistance in two-dimensional material-polymer composites from measurement to mechanistic understanding.Science advances · 2026Review
- Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science.Journal of applied crystallography · 2026Article
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X‑ray Diffraction.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Powder diffraction crystal structure determination using generative models.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining crystal structures from powder X-ray diffraction (PXRD) data is traditionally a labor-intensive process that demands substantial expertise. Here we introduce PXRDGen, an end-to-end neural network that determines crystal structures by learning joint structural distributions from experimentally stable crystals and their PXRD, producing atomically accurate structures refined through PXRD data. PXRDGen integrates a pretrained XRD encoder, a diffusion/flow-based structure generator, and a Rietveld refinement module, solving structures with unparalleled accuracy in seconds. Evaluation on MP-20 dataset reveals a record high matching rate of 82% (1-sample) and 96% (20-samples) for valid compounds, with Root Mean Square Error (RMSE) approaching the precision limits of Rietveld refinement. PXRDGen effectively tackles key challenges in PXRD, such as the resolution of overlapping peaks, localization of light atoms, and differentiation of neighboring elements.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.