Machine learning-enabled forward prediction and inverse design of 4D-printed active plates
Abstract Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs. However, efficiently designing the mate...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-49775-z |