Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions

2014 ◽  
Vol 141 (3) ◽  
pp. 034109 ◽  
Author(s):  
Bin Jiang ◽  
Hua Guo
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Surface Interactions ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network ◽  
Energy Surfaces ◽  
Molecule Surface

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.

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.

scholarly journals Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH

2019 ◽  
Vol 21 (26) ◽  
pp. 14205-14213 ◽  
Author(s):  
Yafu Guan ◽  
Dong H. Zhang ◽  
Hua Guo ◽  
David R. Yarkony
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Adiabatic Potential ◽  
General Algorithm ◽  
Energy Surfaces

A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.

Sign in / Sign up

Export Citation Format

Share Document