scholarly journals Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

2014 ◽  
Vol 115 (16) ◽  
pp. 1012-1020 ◽  
Author(s):  
Sergei Manzhos ◽  
Richard Dawes ◽  
Tucker Carrington
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
High Dimensional ◽  
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.

scholarly journals Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions

2018 ◽  
Vol 122 (3) ◽  
pp. 1761-1769 ◽  
Author(s):  
Qinghua Liu ◽  
Xueyao Zhou ◽  
Linsen Zhou ◽  
Yaolong Zhang ◽  
Xuan Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Surface Scattering ◽  
High Dimensional ◽  
Energy Surfaces

Exact state-to-state quantum dynamics of the F+HD→HF(v′=2)+D reaction on model potential energy surfaces

2008 ◽  
Vol 129 (6) ◽  
pp. 064303 ◽  
Author(s):  
Dario De Fazio ◽  
Vincenzo Aquilanti ◽  
Simonetta Cavalli ◽  
Antonio Aguilar ◽  
Josep M. Lucas
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
Model Potential ◽  
Energy Surfaces

scholarly journals Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH

2019 ◽  
Vol 21 (26) ◽  
pp. 14205-14213 ◽  
Author(s):  
Yafu Guan ◽  
Dong H. Zhang ◽  
Hua Guo ◽  
David R. Yarkony
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Adiabatic Potential ◽  
General Algorithm ◽  
Energy Surfaces

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

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