Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/288855
COMPARTIR / EXPORTAR:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing

AutorCuevas-Zuviría, Bruno; Pacios, Luis F.
Palabras claveElectron
Machine learning
Physical phenomena
Fecha de publicación19-may-2021
EditorAmerican Chemical Society
CitaciónJournal of Chemical Information and Modeling 61(6): 2658-2666 (2021)
ResumenMachine 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.
DescripciónCentro de Biotecnología y Genómica de Plantas
Versión del editorhttps://doi.org/10.1021/acs.jcim.1c00227
URIhttp://hdl.handle.net/10261/288855
DOI10.1021/acs.jcim.1c00227
ISSN1549-9596
Aparece en las colecciones: (INIA) Artículos




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
oficial.pdfartículo59,24 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

PubMed Central
Citations

7
checked on 24-mar-2024

SCOPUSTM   
Citations

18
checked on 28-mar-2024

WEB OF SCIENCETM
Citations

15
checked on 23-feb-2024

Page view(s)

15
checked on 28-mar-2024

Download(s)

60
checked on 28-mar-2024

Google ScholarTM

Check

Altmetric

Altmetric


Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.