ArticleiScience2022
Machine learning magnetism classifiers from atomic coordinates.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- CNN-based classifier for automated identification of magnetic states in spin dynamics simulations.Scientific reports · 2026Article
- Structural constraint integration in a generative model for the discovery of quantum materials.Nature materials · 2026Article
- The northeast materials database for magnetic materials.Nature communications · 2025Article
- AI-Driven Defect Engineering for Advanced Thermoelectric Materials.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides.Scientific reports · 2023Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and non-magnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8% and 73.6%. In particular, a 91% accuracy is reached when predicting no magnetic ordering even if the structure contains magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination.
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