ArticleJournal of chemical information and modeling2024
Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence-driven magnetic property prediction and materials discovery for next-generation spintronics.RSC advances · 2026Review
- Broadening Hard-Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs.The journal of physical chemistry letters · 2025Article
- Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow.Journal of chemical information and modeling · 2025Article
- Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.Journal of chemical information and modeling · 2024Article
- Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning.Journal of chemical information and modeling · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (
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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.