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.

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.

An accurate full-dimensional potential energy surface for the reaction OH + SO → H + SO2

2021 ◽  
Vol 23 (1) ◽  
pp. 487-497
Author(s):  
Jie Qin ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network

An accurate full-dimensional PES for the OH + SO ↔ H + SO2 reaction is developed by the permutation invariant polynomial-neural network approach.

An accurate full-dimensional permutationally invariant potential energy surface for the interaction between H2O and CO

2019 ◽  
Vol 21 (43) ◽  
pp. 24101-24111 ◽  
Author(s):  
Yang Liu ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Polynomial Neural Network ◽  
Invariant Potential

The first full-dimensional accurate potential energy surface was developed for the CO + H2O system based on ca. 102 000 points calculated at the CCSD(T)-F12a/AVTZ level using a permutation invariant polynomial-neural network (PIP-NN) method.

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.

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.

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.

A global coupled cluster potential energy surface for HCl + OH ↔ Cl + H2O

2017 ◽  
Vol 19 (15) ◽  
pp. 9770-9777 ◽  
Author(s):  
Junxiang Zuo ◽  
Bin Zhao ◽  
Hua Guo ◽  
Daiqian Xie
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Cluster Method ◽  
Coupled Cluster ◽  
Invariant Polynomial ◽  
Coupled Cluster Method ◽  
Polynomial Neural Network ◽  
Explicitly Correlated

A new and more accurate full-dimensional global potential energy surface (PES) for the ground electronic state of the ClH2O system is developed by using the permutation invariant polynomial-neural network (PIP-NN) method to fit 15 777 points obtained using an explicitly correlated unrestricted coupled-cluster method with single, double, and perturbative triple excitations (UCCSD(T)-F12b).

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.

Methane dissociation on Ni(111): A fifteen-dimensional potential energy surface using neural network method

2015 ◽  
Vol 143 (14) ◽  
pp. 144701 ◽  
Author(s):  
Xiangjian Shen ◽  
Jun Chen ◽  
Zhaojun Zhang ◽  
Kejie Shao ◽  
Dong H. Zhang
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Neural Network Method ◽  
Network Method ◽  
Methane Dissociation
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