Matches in SemOpenAlex for { <https://semopenalex.org/work/W3161704673> ?p ?o ?g. }
- W3161704673 endingPage "2666" @default.
- W3161704673 startingPage "2658" @default.
- W3161704673 abstract "Machine learning milestones in computational chemistry are overshadowed by their unaccountability and the overwhelming zoo of tools for each specific task. A promising path to tackle these problems is using machine learning to reproduce physical magnitudes as a basis to derive many other properties. By using a model of the electron density consisting of an analytical expansion on a linear set of isotropic and anisotropic functions, we implemented in this work a message-passing neural network able to reproduce electron density in molecules with just a 2.5% absolute error in complex cases. We also adapted our methodology to describe electron density in large biomolecules (proteins) and to obtain atomic charges, interaction energies, and DFT energies. We show that electron density learning is a new promising avenue with a variety of forthcoming applications." @default.
- W3161704673 created "2021-05-24" @default.
- W3161704673 creator A5011557509 @default.
- W3161704673 creator A5037033191 @default.
- W3161704673 date "2021-05-19" @default.
- W3161704673 modified "2023-09-30" @default.
- W3161704673 title "Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing" @default.
- W3161704673 cites W1757990252 @default.
- W3161704673 cites W1985583109 @default.
- W3161704673 cites W2018120102 @default.
- W3161704673 cites W2020786104 @default.
- W3161704673 cites W2020885528 @default.
- W3161704673 cites W2021268632 @default.
- W3161704673 cites W2080635178 @default.
- W3161704673 cites W2101204604 @default.
- W3161704673 cites W2114982976 @default.
- W3161704673 cites W2405861245 @default.
- W3161704673 cites W2527189750 @default.
- W3161704673 cites W2549829487 @default.
- W3161704673 cites W2567896191 @default.
- W3161704673 cites W2585152223 @default.
- W3161704673 cites W2594183968 @default.
- W3161704673 cites W2620687153 @default.
- W3161704673 cites W2789706299 @default.
- W3161704673 cites W2885841934 @default.
- W3161704673 cites W2889703828 @default.
- W3161704673 cites W2963071675 @default.
- W3161704673 cites W2972006524 @default.
- W3161704673 cites W2975697068 @default.
- W3161704673 cites W2984234582 @default.
- W3161704673 cites W3000478925 @default.
- W3161704673 cites W3009761974 @default.
- W3161704673 cites W3010488723 @default.
- W3161704673 cites W3023856540 @default.
- W3161704673 cites W3023962374 @default.
- W3161704673 cites W3028529071 @default.
- W3161704673 cites W3034310241 @default.
- W3161704673 cites W3043479795 @default.
- W3161704673 cites W3047387521 @default.
- W3161704673 cites W3048497553 @default.
- W3161704673 cites W3093036756 @default.
- W3161704673 cites W3098321015 @default.
- W3161704673 cites W3099813870 @default.
- W3161704673 cites W3099950071 @default.
- W3161704673 cites W3100344218 @default.
- W3161704673 cites W3101643101 @default.
- W3161704673 cites W3103080741 @default.
- W3161704673 cites W3103736477 @default.
- W3161704673 cites W3106310231 @default.
- W3161704673 cites W3112590057 @default.
- W3161704673 cites W3188900288 @default.
- W3161704673 doi "https://doi.org/10.1021/acs.jcim.1c00227" @default.
- W3161704673 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34009970" @default.
- W3161704673 hasPublicationYear "2021" @default.
- W3161704673 type Work @default.
- W3161704673 sameAs 3161704673 @default.
- W3161704673 citedByCount "16" @default.
- W3161704673 countsByYear W31617046732021 @default.
- W3161704673 countsByYear W31617046732022 @default.
- W3161704673 countsByYear W31617046732023 @default.
- W3161704673 crossrefType "journal-article" @default.
- W3161704673 hasAuthorship W3161704673A5011557509 @default.
- W3161704673 hasAuthorship W3161704673A5037033191 @default.
- W3161704673 hasBestOaLocation W31617046732 @default.
- W3161704673 hasConcept C121332964 @default.
- W3161704673 hasConcept C121864883 @default.
- W3161704673 hasConcept C12426560 @default.
- W3161704673 hasConcept C125485243 @default.
- W3161704673 hasConcept C147120987 @default.
- W3161704673 hasConcept C147597530 @default.
- W3161704673 hasConcept C152365726 @default.
- W3161704673 hasConcept C154945302 @default.
- W3161704673 hasConcept C159467904 @default.
- W3161704673 hasConcept C171250308 @default.
- W3161704673 hasConcept C177264268 @default.
- W3161704673 hasConcept C184050105 @default.
- W3161704673 hasConcept C185592680 @default.
- W3161704673 hasConcept C186060115 @default.
- W3161704673 hasConcept C192562407 @default.
- W3161704673 hasConcept C199360897 @default.
- W3161704673 hasConcept C2524010 @default.
- W3161704673 hasConcept C2777735758 @default.
- W3161704673 hasConcept C33923547 @default.
- W3161704673 hasConcept C41008148 @default.
- W3161704673 hasConcept C49853544 @default.
- W3161704673 hasConcept C50644808 @default.
- W3161704673 hasConcept C62520636 @default.
- W3161704673 hasConcept C86803240 @default.
- W3161704673 hasConceptScore W3161704673C121332964 @default.
- W3161704673 hasConceptScore W3161704673C121864883 @default.
- W3161704673 hasConceptScore W3161704673C12426560 @default.
- W3161704673 hasConceptScore W3161704673C125485243 @default.
- W3161704673 hasConceptScore W3161704673C147120987 @default.
- W3161704673 hasConceptScore W3161704673C147597530 @default.
- W3161704673 hasConceptScore W3161704673C152365726 @default.
- W3161704673 hasConceptScore W3161704673C154945302 @default.
- W3161704673 hasConceptScore W3161704673C159467904 @default.
- W3161704673 hasConceptScore W3161704673C171250308 @default.