ArticleNature materials2025
Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models.
Article in Nature materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- CrystalGRW: generative modeling of crystal structures with targeted crystallographic properties via geodesic random walks.Scientific reports · 2026Article
- Spectroscopy-informed XANES-PXRD framework for multi-property prediction and structure inference.Chemical science · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Equivariant diffusion solution for inorganic crystal structure determination from powder X-ray diffraction data.Nature communications · 2026Article
- Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X‑ray Diffraction.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- The dynamic frontier of artificial intelligence.Nature materials · 2026Article
- Artificial intelligence in action: building simulation and analysis tools for powder diffraction.Acta crystallographica. Section A, Foundations and advances · 2025Article
- Powder diffraction crystal structure determination using generative models.Nature communications · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
A major challenge in materials science is the determination of the structure of nanometre-sized objects. Here we present an approach that uses a generative machine learning model based on diffusion processes that are trained on 45,229 known structures. The model factors measured the diffraction pattern as well as the relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-sized broadened powder diffraction pattern, we find that our model, PXRDnet, can successfully solve the simulated nanocrystals as small as 10 Å across 200 materials of varying symmetries and complexities, including structures from all seven crystal systems. We show that our model can successfully and verifiably determine structural candidates four out of five times, with an average error among these candidates being only 7% (as measured by the post-Rietveld refinement R-factor). Furthermore, PXRDnet is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data-driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nanomaterials.
Identifiers
40295751What 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.