A new potential energy surface for the ground electronic state of the LiH2 system, and dynamics studies on the H(2S) + LiH(X1Σ+) → Li(2S) + H2(X1Σg+) reaction

2015 ◽  
Vol 17 (17) ◽  
pp. 11732-11739 ◽  
Author(s):  
Jiuchuang Yuan ◽  
Di He ◽  
Maodu Chen
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Electronic State ◽  
Energy Surface ◽  
Ground Electronic State ◽  
Neural Network Method ◽  
Network Method ◽  
High Level

A new potential energy surface of the LiH2 system is obtained using a neural network method based on high-level energies.

A neural network potential energy surface for the NaH2 system and dynamics studies on the H(2S) + NaH(X1Σ+) → Na(2S) + H2(X1Σg+) reaction

2017 ◽  
Vol 19 (30) ◽  
pp. 19873-19880 ◽  
Author(s):  
Shufen Wang ◽  
Jiuchuang Yuan ◽  
Huixing Li ◽  
Maodu Chen
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
High Level

A new potential energy surface of the NaH2 system is obtained using the neural network method based on high-level energies.

Quasi-classical trajectory studies on the full-dimensional accurate potential energy surface for the OH + H2O = H2O + OH reaction

2017 ◽  
Vol 19 (27) ◽  
pp. 17718-17725 ◽  
Author(s):  
Mengna Bai ◽  
Dandan Lu ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Classical Trajectory ◽  
Invariant Polynomial ◽  
Neural Network Method ◽  
Polynomial Neural Network ◽  
Network Method

The first accurate PES for the OH + H2O reaction is developed by using the permutation invariant polynomial-neural network method to fit ∼48 000 CCSD(T)-F12a/AVTZ calculated points.

scholarly journals A global potential energy surface and dynamics study of the Au+ + H2 → H + Au+H reaction

RSC Advances ◽  
2017 ◽  
Vol 7 (57) ◽  
pp. 35648-35654
Author(s):  
Shufen Wang ◽  
Di He ◽  
Wentao Li ◽  
Maodu Chen
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ground State ◽  
Energy Surface ◽  
Invariant Polynomials ◽  
Neural Network Method ◽  
Network Method

A global potential energy surface (PES) of the ground state of the Au+H2 system was constructed using a neural network method with permutation invariant polynomials.

scholarly journals A new potential energy surface for the ground state of the LiH2+system and dynamic studies on LiH+(X2Σ+) + H(2S) → Li+(1S) + H2(X1Σ+g)

RSC Advances ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. 7008-7014 ◽  
Author(s):  
Man Dong ◽  
Wentao Li ◽  
Di He ◽  
Maodu Chen
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ground State ◽  
Energy Surface ◽  
Neural Network Method ◽  
Dynamic Studies ◽  
The Neural Network ◽  
Network Method

An accurate potential energy surface for the ground state of the LiH2+system is constructed with the neural network method.

A new global potential energy surface of the SH2+(X4A′′) system and quantum calculations for the S+ + H2(v = 0–3, j = 0) reaction

2021 ◽  
Author(s):  
Ziliang Zhu ◽  
Aijie Zhang ◽  
Di He ◽  
Wentao Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Ground State ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Quantum Calculations ◽  
Neural Network Method ◽  
Polynomial Neural Network ◽  
Network Method

A new global potential energy surface (PES) for the ground state of the SH2+(X4A′′) system is constructed using a permutation invariant polynomial neural network method.

Ab initio properties and potential energy surface of the ground electronic state of BeHe2+

2006 ◽  
Vol 429 (1-3) ◽  
pp. 335-340 ◽  
Author(s):  
Alister J. Page ◽  
David J.D. Wilson ◽  
Ellak I. von Nagy-Felsobuki
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Electronic State ◽  
Energy Surface ◽  
Ground Electronic State

Potential energy surface and ro-vibrational energies of Ne+3in the ground electronic state

2002 ◽  
Vol 90 (3) ◽  
pp. 1232-1239 ◽  
Author(s):  
Milan Šindelka ◽  
Vladimír Špirko ◽  
Jan Urban ◽  
Pavel Mach ◽  
Jerzy Leszczynski
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Electronic State ◽  
Energy Surface ◽  
Ground Electronic State ◽  
Vibrational Energies
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