Transforming machine learning model knowledge into material insights for multi-principal-element superalloy phase design
Abstract Machine learning (ML) is a powerful tool for the accelerated design and development of various materials. However, the constructed ML models are often difficult to use by researchers other than the creator, that is, model sharing is a challenge. Here, we propose a method to avoid this issue...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01578-6 |
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