Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks.
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.
Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States.
Brian
B
Kolb
Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States.
Bin
B
Zhao
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China.
Jun
J
Li
Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Bin
B
Jiang
Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States.
Hua
H
Guo