ArticleNature communications2025
CrystalFlow: a flow-based generative model for crystalline materials.
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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- CrystalGRW: generative modeling of crystal structures with targeted crystallographic properties via geodesic random walks.Scientific reports · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Siamese foundation models for crystal structure prediction.Nature communications · 2026Article
- Continuous discovery of novel 2D materials via dual active learning-driven generative models.National science review · 2026Article
- CrystalFlow: a flow-based generative model for crystalline materials.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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning-based generative models hold significant promise for exploring the configuration space of crystalline materials, though their application remains in its early stages. In this study, we present CrystalFlow, a flow-based generative model designed to address the unique challenges of this domain. By combining Continuous Normalizing Flows and Conditional Flow Matching with a graph-based equivariant neural network and symmetry-aware data representations, CrystalFlow efficiently models lattice parameters, atomic coordinates, and atom types. This architecture enables data-efficient learning and the generation of high-quality crystal structures. Our results indicate that CrystalFlow achieves performance comparable to state-of-the-art models on established benchmarks while exhibiting versatile conditional generation capabilities (e.g., predicting structures under specific pressures or material properties), and is approximately an order of magnitude more efficient than diffusion-based models in terms of integration steps.
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.