Efficient generation of sum-of-products representations of high-dimensional potential energy surfaces based on multimode expansions

2016 ◽  
Vol 144 (11) ◽  
pp. 114114 ◽  
Author(s):  
Benjamin Ziegler ◽  
Guntram Rauhut
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
High Dimensional ◽  
Efficient Generation ◽  
Sum Of Products ◽  
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 High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning

2020 ◽  
Vol 1 (1) ◽  
pp. 013001 ◽  
Author(s):  
Oliver T Unke ◽  
Debasish Koner ◽  
Sarbani Patra ◽  
Silvan Käser ◽  
Markus Meuwly
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Molecular Simulations ◽  
High Dimensional ◽  
Energy Surfaces
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