scholarly journals Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance

2020 ◽  
Vol 124 (28) ◽  
pp. 5737-5745 ◽  
Author(s):  
Dandan Lu ◽  
Jörg Behler ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Permutation Invariance ◽  
Energy Surfaces

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.

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.

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