ArticleScientific reports2026
A crystal graph to vector approach for predicting magnetic properties.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
3 authors.
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Abstract
We introduce CG-Vec, a crystal graph-to-vector framework that replaces iterative message passing with compact, interpretable descriptors coupled to conventional machine learning. Across diverse datasets, CG-Vec matches the accuracy of deep graph networks on large datasets but substantially outperforms them in data-scarce regimes. The advantage is most pronounced for magnetic properties, where existing approaches have struggled: CG-Vec delivers reliable predictions of magnetization in both ferromagnetic and ferrimagnetic systems and of Curie temperature. Beyond magnetism, CG-Vec performs competitively for formation energy and band gap, demonstrating broad applicability. These results establish vectorized representations as a practical and scalable alternative to deep architectures, enabling efficient and interpretable modeling of complex material properties.
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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.