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Li, Jun, Jiang, Bin, Guo, Hua (2013) Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems. The Journal of Chemical Physics, 139 (20). 204103pp. doi:10.1063/1.4832697

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Reference TypeJournal (article/letter/editorial)
TitlePermutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
JournalThe Journal of Chemical Physics
AuthorsLi, JunAuthor
Jiang, BinAuthor
Guo, HuaAuthor
Year2013 (November 28)Volume139
Issue20
PublisherAIP Publishing
DOIdoi:10.1063/1.4832697Search in ResearchGate
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Mindat Ref. ID2364336Long-form Identifiermindat:1:5:2364336:7
GUID0
Full ReferenceLi, Jun, Jiang, Bin, Guo, Hua (2013) Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems. The Journal of Chemical Physics, 139 (20). 204103pp. doi:10.1063/1.4832697
Plain TextLi, Jun, Jiang, Bin, Guo, Hua (2013) Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems. The Journal of Chemical Physics, 139 (20). 204103pp. doi:10.1063/1.4832697
In(2013, November) The Journal of Chemical Physics Vol. 139 (20) AIP Publishing

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Jiang, Bin, Guo, Hua (2013) Permutation invariant polynomial neural network approach to fitting potential energy surfaces. The Journal of Chemical Physics, 139 (5). 54112pp. doi:10.1063/1.4817187
Jiang, Bin, Guo, Hua (2014) Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions. The Journal of Chemical Physics, 141 (3). 34109pp. doi:10.1063/1.4887363
Xie, Changjian, Zhu, Xiaolei, Yarkony, David R., Guo, Hua (2018) Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices. The Journal of Chemical Physics, 149 (14). 144107pp. doi:10.1063/1.5054310
Kolb, Brian, Zhao, Bin, Li, Jun, Jiang, Bin, Guo, Hua (2016) Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks. The Journal of Chemical Physics, 144 (22). 224103pp. doi:10.1063/1.4953560
Chen, Rongjun, Shao, Kejie, Fu, Bina, Zhang, Dong H. (2020) Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms. The Journal of Chemical Physics, 152 (20) 204307pp. doi:10.1063/5.0010104
Li, Jun, Guo, Hua (2015) Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system. The Journal of Chemical Physics, 143 (21). 214304pp. doi:10.1063/1.4936660
Shao, Kejie, Chen, Jun, Zhao, Zhiqiang, Zhang, Dong H. (2016) Communication: Fitting potential energy surfaces with fundamental invariant neural network. The Journal of Chemical Physics, 145 (7). 71101pp. doi:10.1063/1.4961454
Dawes, Richard, Lolur, Phalgun, Li, Anyang, Jiang, Bin, Guo, Hua (2013) Communication: An accurate global potential energy surface for the ground electronic state of ozone. The Journal of Chemical Physics, 139 (20). 201103pp. doi:10.1063/1.4837175
Li, Jun, Chen, Jun, Zhang, Dong H., Guo, Hua (2014) Quantum and quasi-classical dynamics of the OH + CO → H + CO2reaction on a new permutationally invariant neural network potential energy surface. The Journal of Chemical Physics, 140 (4). 44327pp. doi:10.1063/1.4863138
Lu, Dandan, Behler, Jörg, Li, Jun (2020) Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance. The Journal of Physical Chemistry A, 124 (28) 5737-5745 doi:10.1021/acs.jpca.0c04182
Li, Jun, Jiang, Bin, Guo, Hua (2013) Spin-orbit corrected full-dimensional potential energy surfaces for the two lowest-lying electronic states of FH2O and dynamics for the F + H2O → HF + OH reaction. The Journal of Chemical Physics, 138 (7). 74309pp. doi:10.1063/1.4791640
 
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