Transforming high-dimensional potential energy surfaces into sum-of-products form using Monte Carlo methods

2017 ◽  
Vol 147 (6) ◽  
pp. 064105 ◽  
Author(s):  
Markus Schröder ◽  
Hans-Dieter Meyer
Keyword(s):  
Monte Carlo ◽  
Potential Energy ◽  
Monte Carlo Methods ◽  
Potential Energy Surfaces ◽  
High Dimensional ◽  
Sum Of Products ◽  
Energy Surfaces

Monte Carlo studies of aqueous solution of nitrogen using different potential energy surfaces

1986 ◽  
Vol 58 (1) ◽  
pp. 65-83 ◽  
Author(s):  
E.S. Fois ◽  
A. Gamba ◽  
G. Morosi ◽  
P. Demontis ◽  
G.B. Suffritti
Keyword(s):  
Aqueous Solution ◽  
Monte Carlo ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Monte Carlo Studies ◽  
Energy Surfaces

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

2017 ◽  
Vol 16 (05) ◽  
pp. 1730001 ◽  
Author(s):  
Alex Brown ◽  
E. Pradhan
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
The Neural Network ◽  
Sum Of Products ◽  
High Level ◽  
Energy Surfaces ◽  
Dynamics Problems

In this paper, the use of the neural network (NN) method with exponential neurons for directly fitting ab initio data to generate potential energy surfaces (PESs) in sum-of-product form will be discussed. The utility of the approach will be highlighted using fits of CS2, HFCO, and HONO ground state PESs based upon high-level ab initio data. Using a generic interface between the neural network PES fitting, which is performed in MATLAB, and the Heidelberg multi-configuration time-dependent Hartree (MCTDH) software package, the PESs have been tested via comparison of vibrational energies to experimental measurements. The review demonstrates the potential of the PES fitting method, combined with MCTDH, to tackle high-dimensional quantum dynamics problems.

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