Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization
We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties, supporting in-silico screening for desired Rc values and significantly reducing reliance on time-consuming laboratory work. We compare results for features derived from easy-...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Journal of Materiomics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235284782400203X |
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