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- W3100203441 abstract "Artificial neural networks are used to fit a potential energy surface (PES). We demonstrate the benefits of using not only energies but also their first and second derivatives as training data for the neural network. This ensures smooth and accurate Hessian surfaces, which are required for rate constant calculations using instanton theory. Our aim was a local, accurate fit rather than a global PES because instanton theory requires information on the potential only in the close vicinity of the main tunneling path. Elongations along vibrational normal modes at the transition state are used as coordinates for the neural network. The method is applied to the hydrogen abstraction reaction from methanol, calculated on a coupled-cluster level of theory. The reaction is essential in astrochemistry to explain the deuteration of methanol in the interstellar medium." @default.
- W3100203441 created "2020-11-23" @default.
- W3100203441 creator A5006545290 @default.
- W3100203441 creator A5056979833 @default.
- W3100203441 creator A5064481133 @default.
- W3100203441 date "2018-03-01" @default.
- W3100203441 modified "2023-10-09" @default.
- W3100203441 title "Potential energy surface interpolation with neural networks for instanton rate calculations" @default.
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- W3100203441 doi "https://doi.org/10.1063/1.5015950" @default.
- W3100203441 hasPublicationYear "2018" @default.
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