AI framework accelerates discovery of antiferromagnets for next‑gen spintronics
Researchers from China’s Hangzhou Dianzi University have developed an artificial intelligence-driven framework that could accelerate the discovery of antiferromagnetic materials for spintronics. Antiferromagnets (AFMs) are prized in advanced electronics because their alternating spin orientations cancel stray magnetic fields, creating fast, stable, and densely packable devices. However, their complex magnetic interactions and vast chemical possibilities have made systematic design extremely challenging.
The team’s approach combines a crystal diffusion variational autoencoder with data augmentation (CDVAE-DA), crystal graph convolutional neural networks (CGCNNs), a genetic algorithm (GA), and density functional theory (DFT) validation into a single, integrated pipeline. CDVAE-DA learns from tens of thousands of known crystal structures and then fine tunes its predictions on an AFM-specific dataset, producing novel, chemically valid candidates with over 90% composition accuracy. These structures are rapidly screened by CGCNN models, which assess three key properties: formation energy, total magnetic moment, and electronic band gap. Candidates meeting AFM-friendly criteria—stable energy, low net magnetization, and a targeted band gap range—are passed to the optimization stage.
Source: https://www.spintronics-info.com/ai-framework-accelerates-discovery-antiferromagnets-next-gen-spintronics
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