Neural networks to approach potential energy surfaces: Application to a molecular dynamics simulation

2007 ◽  
Vol 107 (11) ◽  
pp. 2120-2132 ◽  
Author(s):  
Diogo A. R. S. Latino ◽  
Filomena F. M. Freitas ◽  
João Aires-De-Sousa ◽  
Fernando M. S. Silva Fernandes
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Dynamics Simulation ◽  
Energy Surfaces

Nonadiabatic molecular dynamics simulation for the ultrafast photoisomerization of dMe-OMe-NAIP based on TDDFT on-the-fly potential energy surfaces

2021 ◽  
Vol 23 (9) ◽  
pp. 5236-5243
Author(s):  
Ying Hu ◽  
Chao Xu ◽  
Linfeng Ye ◽  
Feng Long Gu ◽  
Chaoyuan Zhu
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Dynamics Simulation ◽  
Surface Hopping ◽  
Nonadiabatic Molecular Dynamics ◽  
Energy Surfaces

Global switching on-the-fly trajectory surface hopping molecular dynamics simulation was performed on the accurate TD-B3LYP/6-31G* potential energy surfaces for E-to-Z and Z-to-E photoisomerization of dMe-OMe-NAIP up to S1(ππ*) excitation.

scholarly journals Constraint Trajectory Surface-Hopping Molecular Dynamics Simulation of the Photoisomerization of Stilbene

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Yibo Lei ◽  
Shaomei Wu ◽  
Chaoyuan Zhu ◽  
Zhenyi Wen ◽  
Sheng-Hsien Lin
Keyword(s):  
Molecular Dynamics ◽  
Quantum Yield ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Branching Ratio ◽  
Dynamics Simulation ◽  
Conical Intersection ◽  
Surface Hopping ◽  
Internal Coordinates ◽  
Energy Surfaces

Combining trajectory surface hopping (TSH) method with constraint molecular dynamics, we have extended TSH method from full to flexible dimensional potential energy surfaces. Classical trajectories are carried out in Cartesian coordinates with constraints in internal coordinates, while nonadiabatic switching probabilities are calculated separately in free internal coordinates by Landau-Zener and Zhu-Nakamura formulas along the seam. Two-dimensional potential energy surfaces of groundS0and excitedS1states are constructed analytically in terms of torsion angle and one dihedral angle around the central ethylenic C=C bond, and the other internal coordinates are all fixed at configuration of the conical intersection. At this conical intersection, the branching ratio from the present simulation is 48 : 52 (33 : 67) initially starting from trans(cis)-Stilbene in comparison with experimental value 50 : 50. Quantum yield for trans-to-cis isomerization is estimated as 49% in very good agreement with experimental value of 55%, while quantum yield for cis-to-trans isomerization is estimated as 47% in comparison with experimental value of 35%.

scholarly journals Raman spectrum and polarizability of liquid water from deep neural networks

2020 ◽  
Vol 22 (19) ◽  
pp. 10592-10602 ◽  
Author(s):  
Grace M. Sommers ◽  
Marcos F. Calegari Andrade ◽  
Linfeng Zhang ◽  
Han Wang ◽  
Roberto Car
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Raman Spectrum ◽  
Potential Energy ◽  
Ab Initio ◽  
Liquid Water ◽  
Potential Energy Surfaces ◽  
Deep Neural Networks ◽  
Energy Surfaces

Using deep neural networks to model the polarizability and potential energy surfaces, we compute the Raman spectrum of liquid water at several temperatures with ab initio molecular dynamics accuracy.

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.

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