Transferring chemical and energetic knowledge between molecular systems with machine learning
Machine learning algorithms are widely employed for molecular simulations, but there are likely many yet unexplored routes for the prediction of structural and energetic properties of biologically relevant systems. Here, the authors develop a hypergraph representation and message passing method for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-01-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-022-00790-5 |
| Summary: | Machine learning algorithms are widely employed for molecular simulations, but there are likely many yet unexplored routes for the prediction of structural and energetic properties of biologically relevant systems. Here, the authors develop a hypergraph representation and message passing method for transferring knowledge obtained from simple molecular systems onto more complex ones, demonstrated by transfer learning from tri-alanine to the deca-alanine system. |
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| ISSN: | 2399-3669 |