Validity of the site-averaging approximation for modeling the dissociative chemisorption of H2 on Cu(111) surface: A quantum dynamics study on two potential energy surfaces

2014 ◽  
Vol 141 (19) ◽  
pp. 194302 ◽  
Author(s):  
Tianhui Liu ◽  
Bina Fu ◽  
Dong H. Zhang
2016 ◽  
Vol 45 (13) ◽  
pp. 3621-3640 ◽  
Author(s):  
Bin Jiang ◽  
Minghui Yang ◽  
Daiqian Xie ◽  
Hua Guo

Recent advances in quantum dynamical characterization of polyatomic dissociative chemisorption on accurate global potential energy surfaces are critically reviewed.


2008 ◽  
Vol 129 (6) ◽  
pp. 064303 ◽  
Author(s):  
Dario De Fazio ◽  
Vincenzo Aquilanti ◽  
Simonetta Cavalli ◽  
Antonio Aguilar ◽  
Josep M. Lucas

2002 ◽  
Vol 01 (02) ◽  
pp. 285-293 ◽  
Author(s):  
HIDEYUKI KAMISAKA ◽  
HIROKI NAKAMURA ◽  
SHINKOH NANBU ◽  
MUTSUMI AOYAGI ◽  
WENSHENG BIAN ◽  
...  

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.


2017 ◽  
Vol 16 (05) ◽  
pp. 1730001 ◽  
Author(s):  
Alex Brown ◽  
E. Pradhan

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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