ArticleScientific data2025
A new benchmark for machine learning applied to powder X-ray diffraction.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although crystal parameter prediction from powder X-ray diffraction has recently attracted the interest of the machine learning community, most existing datasets for this task are private and lack structural diversity. Here, we introduce the Simulated Powder X-ray Diffraction Open Database (SIMPOD), a new dataset that is public and structurally varied. This new benchmark includes 467,861 crystal structures from the Crystallography Open Database (COD) and their powder X-ray diffraction patterns. SIMPOD presents simulated one-dimensional powder X-ray diffractograms and derived two-dimensional radial images to facilitate the adoption of computer vision models for this task. We hope SIMPOD contributes to developing models that improve materials analysis from powder X-ray diffraction.
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.