Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning
Machine learning models have proven to be powerful tools to discover links between microstructure and properties of materials, but the black box nature of the models limits the physical insights one might gain from them. Here, we study the relationship between the atomic structure and the elastic an...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-06-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0242318 |
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