Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

2016 ◽  
Vol 144 (22) ◽  
pp. 224103 ◽  
Author(s):  
Brian Kolb ◽  
Bin Zhao ◽  
Jun Li ◽  
Bin Jiang ◽  
Hua Guo
2019 ◽  
Vol 21 (26) ◽  
pp. 14205-14213 ◽  
Author(s):  
Yafu Guan ◽  
Dong H. Zhang ◽  
Hua Guo ◽  
David R. Yarkony

A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.


2009 ◽  
Vol 110 (2) ◽  
pp. 432-445 ◽  
Author(s):  
Diogo A. R. S. Latino ◽  
Rui P. S. Fartaria ◽  
Filomena F. M. Freitas ◽  
João Aires-De-Sousa ◽  
Fernando M. S. Silva Fernandes

2007 ◽  
Vol 107 (11) ◽  
pp. 2120-2132 ◽  
Author(s):  
Diogo A. R. S. Latino ◽  
Filomena F. M. Freitas ◽  
João Aires-De-Sousa ◽  
Fernando M. S. Silva Fernandes

Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam

Expansion methods have been employed for some time to represent the potentialenergy surface for molecular systems. The basic concept involved with any expansion method is to write the PES expression for an N-atom system that requires the specification of 3N-6 internal coordinates as a sum of terms each of which involves fewer than N atoms and/or fewer than (3N-6) coordinate variables. Two approaches to the implementation of this concept have been suggested. In the first approach, the focus of attention is the number of internal coordinates upon which each term in the expansion depends.


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