Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Abstract
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 01, 2019
- Source ID
- 10.1038/s41467-019-10827-4
Entities
People
- Adrian Roitberg
- Ben Nebgen
- Christian Devereux
- Justin S Smith
- Kipton Barros
- Nicholas Lubbers
- Olexandr Isayev
- Roman I. Zubatyuk
- Sergei Tretiak
Organizations
- Division of Materials Research
- National Science Foundation
- Office of Naval Research