Exact state-to-state quantum dynamics of the F+HD→HF(v′=2)+D reaction on model potential energy surfaces

2008 ◽  
Vol 129 (6) ◽  
pp. 064303 ◽  
Author(s):  
Dario De Fazio ◽  
Vincenzo Aquilanti ◽  
Simonetta Cavalli ◽  
Antonio Aguilar ◽  
Josep M. Lucas
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
Model Potential ◽  
Energy Surfaces

CHEMICAL REACTIONS IN THE O(1D) + HCl SYSTEM III.

2002 ◽  
Vol 01 (02) ◽  
pp. 285-293 ◽  
Author(s):  
HIDEYUKI KAMISAKA ◽  
HIROKI NAKAMURA ◽  
SHINKOH NANBU ◽  
MUTSUMI AOYAGI ◽  
WENSHENG BIAN ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Potential Energy ◽  
Chemical Reactions ◽  
Reaction Mechanisms ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
Total Angular Momentum ◽  
Nonadiabatic Transitions ◽  
Angular Momentum Quantum Number ◽  
Energy Surfaces

Using the accurate global potential energy surfaces for the 11A′′ and 21A′ states reported in the previous sister Paper I, detailed quantum dynamics calculations are performed for these adiabatic surfaces separately for J = 0 (J: total angular momentum quantum number). In addition to the significant overall contributions of these states to the title reactions reported in the second Paper II of this series, quantum dynamics on these excited potential energy surfaces (PES) are clarified in terms of the PES topographies, which are quite different from that of the ground PES. The reaction mechanisms are found to be strongly selective and nicely explained as vibrationally nonadiabatic transitions in the vicinity of potential ridge.

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

2017 ◽  
Vol 16 (05) ◽  
pp. 1730001 ◽  
Author(s):  
Alex Brown ◽  
E. Pradhan
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
The Neural Network ◽  
Sum Of Products ◽  
High Level ◽  
Energy Surfaces ◽  
Dynamics Problems

In this paper, the use of the neural network (NN) method with exponential neurons for directly fitting ab initio data to generate potential energy surfaces (PESs) in sum-of-product form will be discussed. The utility of the approach will be highlighted using fits of CS2, HFCO, and HONO ground state PESs based upon high-level ab initio data. Using a generic interface between the neural network PES fitting, which is performed in MATLAB, and the Heidelberg multi-configuration time-dependent Hartree (MCTDH) software package, the PESs have been tested via comparison of vibrational energies to experimental measurements. The review demonstrates the potential of the PES fitting method, combined with MCTDH, to tackle high-dimensional quantum dynamics problems.

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