scholarly journals Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

2018 ◽  
Vol 9 (11) ◽  
pp. 2725-2732 ◽  
Author(s):  
Deping Hu ◽  
Yu Xie ◽  
Xusong Li ◽  
Lingyue Li ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Potential Energy ◽  
Ridge Regression ◽  
Potential Energy Surfaces ◽  
Dynamics Simulation ◽  
Kernel Ridge Regression ◽  
Nonadiabatic Molecular Dynamics ◽  
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.

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

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%.

Trajectory-based nonadiabatic molecular dynamics without calculating nonadiabatic coupling in the avoided crossing case: trans ↔ cis photoisomerization in azobenzene

2014 ◽  
Vol 16 (47) ◽  
pp. 25883-25895 ◽  
Author(s):  
Le Yu ◽  
Chao Xu ◽  
Yibo Lei ◽  
Chaoyuan Zhu ◽  
Zhenyi Wen
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Adiabatic Potential ◽  
Nonadiabatic Coupling ◽  
Avoided Crossing ◽  
Nonadiabatic Molecular Dynamics ◽  
Energy Surfaces

Analytical nonadiabatic switching probability along a trajectory can be simulated based only on electronic adiabatic potential energy surfaces and its gradients.

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