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 |