Cargando…
Self-consistent determination of long-range electrostatics in neural network potentials
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cos...
Autores principales: | Gao, Ang, Remsing, Richard C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943018/ https://www.ncbi.nlm.nih.gov/pubmed/35322046 http://dx.doi.org/10.1038/s41467-022-29243-2 |
Ejemplares similares
-
Generalized Moment Correction for Long-Ranged Electrostatics
por: Stenqvist, Björn, et al.
Publicado: (2020) -
Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity
por: Dummer, Benjamin, et al.
Publicado: (2014) -
Long-range electrostatic interactions significantly modulate the affinity of dynein for microtubules
por: Pabbathi, Ashok, et al.
Publicado: (2022) -
Long- and Short-Range Electrostatic Fields in GFP Mutants: Implications for Spectral Tuning
por: Drobizhev, M., et al.
Publicado: (2015) -
The use of artificial neural networks in electrostatic force microscopy
por: Castellano-Hernández, Elena, et al.
Publicado: (2012)