Large language models design sequence-defined macromolecules via evolutionary optimization
Abstract We demonstrate the ability of a large language model to perform evolutionary optimization for materials discovery. Anthropic’s Claude 3.5 model outperforms an active learning scheme with handcrafted surrogate models and an evolutionary algorithm in selecting monomer sequences to produce tar...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01449-6 |
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