Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
oleh: Justin S. Smith, Benjamin T. Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian E. Roitberg
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2019-07-01 |
Deskripsi
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.