Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for Multivariate Function Representation: Application to Molecular Potential Energy Surfaces

2020 ◽  
Vol 124 (37) ◽  
pp. 7598-7607 ◽  
Author(s):  
Mohamed Ali Boussaidi ◽  
Owen Ren ◽  
Dmitry Voytsekhovsky ◽  
Sergei Manzhos
Keyword(s):  
Random Sampling ◽  
Potential Energy Surfaces ◽  
Gaussian Process Regression ◽  
High Dimensional ◽  
High Dimensional Model Representation ◽  
Dimensional Model ◽  
Function Representation ◽  
Molecular Potential ◽  
Molecular Potential Energy ◽  
Energy Surfaces

Two-level stochastic search of low-energy conformers for molecular spectroscopy: implementation and validation of MM and QM models

2019 ◽  
Vol 21 (36) ◽  
pp. 19921-19934 ◽  
Author(s):  
Balasubramanian Chandramouli ◽  
Sara Del Galdo ◽  
Marco Fusè ◽  
Vincenzo Barone ◽  
Giordano Mancini
Keyword(s):  
Potential Energy Surfaces ◽  
Molecular Spectroscopy ◽  
Stochastic Search ◽  
Low Energy ◽  
Stationary Points ◽  
Flexible Systems ◽  
Molecular Potential ◽  
Internal Motions ◽  
Molecular Potential Energy ◽  
Energy Surfaces

The search for stationary points in the molecular potential energy surfaces (PES) is a problem of increasing relevance in molecular sciences especially for large, flexible systems featuring several large-amplitude internal motions.

Towards efficient refinement of molecular potential energy surfaces: Ammonia as a case study

2011 ◽  
Vol 268 (1-2) ◽  
pp. 123-129 ◽  
Author(s):  
Sergei N. Yurchenko ◽  
Robert J. Barber ◽  
Jonathan Tennyson ◽  
Walter Thiel ◽  
Per Jensen
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Molecular Potential ◽  
Molecular Potential Energy ◽  
Energy Surfaces
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