Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks
Abstract
The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H2 → H2 + H, H + H2O → H2 + OH, and H + CH4 → H2 + CH3. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 10, 2016
- Source ID
- 10.1063/1.4953560
Entities
People
- Bin Jiang
- Bin Zhao
- Brian Kolb
- Hua Guo
- Jun Li
Organizations
- Air Force Office of Scientific Research
- Chongqing University
- Massachusetts Institute of Technology
- National Natural Science Foundation of China
- National Science Foundation
- University of New Mexico
- University of Science and Technology of China