Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 144, Issue 22, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.4953560
Keywords
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Funding
- US Air Force Office of Scientific Research [AFOSR-FA9550-15-1-0305]
- National Science Foundation [CHE-1462019]
- National Natural Science Foundation of China [21573027, 21573203]
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1462109] Funding Source: National Science Foundation
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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 + H-2 -> H-2 + H, H + H2O -> H-2 + OH, and H + CH4 -> H-2 + 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. Published by AIP Publishing.
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