High-level ab initio methods for calculation of potential energy surfaces of van der Waals complexes

2004 ◽  
Vol 98 (4) ◽  
pp. 388-408 ◽  
Author(s):  
Timothy J. Giese ◽  
Darrin M. York
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Van Der Waals ◽  
Van Der Waals Complexes ◽  
Ab Initio Methods ◽  
High Level ◽  
Energy Surfaces

Non-covalent interactions and their impact on the complexation thermodynamics of noble gases with methanol

2020 ◽  
Vol 22 (30) ◽  
pp. 17171-17180 ◽  
Author(s):  
Lúcio Renan Vieira ◽  
Sandro Francisco de Brito ◽  
Mateus Rodrigues Barbosa ◽  
Thiago Oliveira Lopes ◽  
Daniel Francisco Scalabrini Machado ◽  
...  
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Noble Gases ◽  
Van Der Waals ◽  
Reliable Information ◽  
Van Der Waals Complexes ◽  
Non Covalent Interactions ◽  
Energy Surfaces ◽  
Covalent Interactions

Accurate ab initio calculations provide the reliable information needed to study the potential energy surfaces that control the non-covalent interactions (NCIs) responsible for the formation of weak van der Waals complexes.

Potential energy surfaces and dynamics of He n Br2 van der Waals complexes

2007 ◽  
pp. 193-202
Author(s):  
Gerardo Delgado-Barrio ◽  
David López-Durán ◽  
álvaro Valdés ◽  
Rita Prosmiti ◽  
Maria Pilar De Lara-Castells ◽  
...  
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Van Der Waals ◽  
Van Der Waals Complexes ◽  
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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