ArticleResearch (Washington, D.C.)2023
Classification and Prediction of Skyrmion Material Based on Machine Learning.
Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Machine Learning Prediction of Poor Flexion-Extension Outcome After Open Elbow Arthrolysis: Identifying Individual Predisposition Using Clinical and Laboratory Indicators.Orthopaedic journal of sports medicine · 2025Article
- Advances in Catalysts for Magnesium-Based Hydrogen Storage Materials.Research (Washington, D.C.) · 2025Review
- Identification and experimental verification of immune-related hub genes in intervertebral disc degeneration.Heliyon · 2024Article
- Scientific Discovery Framework Accelerating Advanced Polymeric Materials Design.Research (Washington, D.C.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery and study of skyrmion materials play an important role in basic frontier physics research and future information technology. The database of 196 materials, including 64 skyrmions, was established and predicted based on machine learning. A variety of intrinsic features are classified to optimize the model, and more than a dozen methods had been used to estimate the existence of skyrmion in magnetic materials, such as support vector machines,
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.