Calculations of the active mode and energetic barrier to electron attachment to CF3 and comparison with kinetic modeling of experimental results

2016 ◽  
Vol 18 (45) ◽  
pp. 31064-31071 ◽  
Author(s):  
Huixian Han ◽  
Benjamin Alday ◽  
Nicholas S. Shuman ◽  
Justin P. Wiens ◽  
Jürgen Troe ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Kinetic Modeling ◽  
Potential Energy Surfaces ◽  
Electron Attachment ◽  
Invariant Polynomial ◽  
Active Mode ◽  
Polynomial Neural Network ◽  
Energy Surfaces

Six-dimensional potential energy surfaces of both CF3 and CF3− were developed by fitting ∼3000 ab initio points using the permutation invariant polynomial-neural network (PIP-NN) approach.

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

2017 ◽  
Vol 16 (05) ◽  
pp. 1730001 ◽  
Author(s):  
Alex Brown ◽  
E. Pradhan
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
The Neural Network ◽  
Sum Of Products ◽  
High Level ◽  
Energy Surfaces ◽  
Dynamics Problems

In this paper, the use of the neural network (NN) method with exponential neurons for directly fitting ab initio data to generate potential energy surfaces (PESs) in sum-of-product form will be discussed. The utility of the approach will be highlighted using fits of CS2, HFCO, and HONO ground state PESs based upon high-level ab initio data. Using a generic interface between the neural network PES fitting, which is performed in MATLAB, and the Heidelberg multi-configuration time-dependent Hartree (MCTDH) software package, the PESs have been tested via comparison of vibrational energies to experimental measurements. The review demonstrates the potential of the PES fitting method, combined with MCTDH, to tackle high-dimensional quantum dynamics problems.

An accurate full-dimensional potential energy surface for the reaction OH + SO → H + SO2

2021 ◽  
Vol 23 (1) ◽  
pp. 487-497
Author(s):  
Jie Qin ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network

An accurate full-dimensional PES for the OH + SO ↔ H + SO2 reaction is developed by the permutation invariant polynomial-neural network approach.

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