Cargando…
Teaching a neural network to attach and detach electrons from molecules
Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were par...
Autores principales: | Zubatyuk, Roman, Smith, Justin S., Nebgen, Benjamin T., Tretiak, Sergei, Isayev, Olexandr |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357920/ https://www.ncbi.nlm.nih.gov/pubmed/34381051 http://dx.doi.org/10.1038/s41467-021-24904-0 |
Ejemplares similares
-
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
por: Smith, Justin S., et al.
Publicado: (2019) -
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
por: Smith, Justin S., et al.
Publicado: (2020) -
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
por: Zubatyuk, Roman, et al.
Publicado: (2019) -
Artificial intelligence-enhanced quantum chemical method with broad applicability
por: Zheng, Peikun, et al.
Publicado: (2021) -
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
por: Smith, Justin S., et al.
Publicado: (2017)