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.

Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks

2019 ◽  
Vol 21 (36) ◽  
pp. 20372-20383 ◽  
Author(s):  
Zhengxi Yin ◽  
Yafu Guan ◽  
Bina Fu ◽  
Dong H. Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Conical Intersections ◽  
Fitting Procedure ◽  
Energy Surfaces

A neural network-fitting procedure based on nonadiabatic couplings is proposed to generate two-state diabatic PESs with conical intersections.

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