Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials
Abstract The rising demand for digital storage and environmental concerns necessitate ultra-high-density, energy-efficient solutions. Atomic-scale magnets (ASMs) based on transition metal (TM) dimers on defective graphene exhibit promising magnetic anisotropy energy (MAE) values, providing a robust...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01637-y |
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