Many-Body Permutationally Invariant Polynomial Neural Network Potential Energy Surface for N4

2020 ◽  
Vol 16 (8) ◽  
pp. 4822-4832 ◽  
Author(s):  
Jun Li ◽  
Zoltan Varga ◽  
Donald G. Truhlar ◽  
Hua Guo
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Many Body ◽  
Polynomial Neural Network

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.

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 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).

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.

scholarly journals Using principal component analysis for neural network high-dimensional potential energy surface

2020 ◽  
Vol 152 (23) ◽  
pp. 234103
Author(s):  
Bastien Casier ◽  
Stéphane Carniato ◽  
Tsveta Miteva ◽  
Nathalie Capron ◽  
Nicolas Sisourat
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional
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