Representation of molecular structures with persistent homology for machine learning applications in chemistry
The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N2 interaction energies at the density functional theory...
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| Auteurs principaux: | , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2020
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/5a638e6c37334f2eb4cc47e69d8c65d8 |
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