Reproducing kernel Hilbert space interpolation methods as a paradigm of high dimensional model representations: Application to multidimensional potential energy surface construction

2003 ◽  
Vol 119 (13) ◽  
pp. 6433-6442 ◽  
Author(s):  
Tak-San Ho ◽  
Herschel Rabitz
Keyword(s):  
Hilbert Space ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Reproducing Kernel ◽  
Energy Surface ◽  
Reproducing Kernel Hilbert Space ◽  
High Dimensional ◽  
Dimensional Model ◽  
Interpolation Methods ◽  
Surface Construction

scholarly journals Using principal component analysis for neural network high-dimensional potential energy surface

2020 ◽  
Vol 152 (23) ◽  
pp. 234103
Author(s):  
Bastien Casier ◽  
Stéphane Carniato ◽  
Tsveta Miteva ◽  
Nathalie Capron ◽  
Nicolas Sisourat
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional

EXPLORING THE ENERGY LANDSCAPE

2005 ◽  
Vol 19 (15n17) ◽  
pp. 2877-2885 ◽  
Author(s):  
DAVID J. WALES
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Rate Constants ◽  
Energy Surface ◽  
Energy Landscape ◽  
High Dimensional ◽  
Global Minima ◽  
Structure Dynamics ◽  
Recent Advances ◽  
Dimensional Object

Calculations of structure, dynamics and thermodynamics in molecular science all rely on the underlying potential energy surface (PES). Recent advances allow us to visualise this high-dimensional object in a compact fashion, locate global minima efficiently, and sample multistep pathways to obtain rate constants. These methods have been applied to a wide variety of systems, including clusters, glasses and biomolecules, and enable us to treat dynamics on the experimental timescale and beyond.

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