Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like mo...

Full description

Bibliographic Details
Main Authors: Justin S. Smith, Benjamin T. Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian E. Roitberg
Format: Article
Language:English
Published: Nature Publishing Group 2019-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-10827-4
Description
Summary:Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like molecular torsions.
ISSN:2041-1723

Similar Items