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: Jacob Townsend, Cassie Putman Micucci, John H. Hymel, Vasileios Maroulas, Konstantinos D. Vogiatzis
Format: article
Langue:EN
Publié: Nature Portfolio 2020
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Accès en ligne:https://doaj.org/article/5a638e6c37334f2eb4cc47e69d8c65d8
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