Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI
Abstract A computational workflow integrating a stacked ensemble machine learning (SEML) model and a convolutional neural network (CNN) model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies....
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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/s41524-025-01600-x |
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