EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys
Abstract Using machine learning to predict and design materials is an important mean of accelerating material development. One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors. However, the complexity of computing material structures limi...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01548-y |
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