scholarly journals Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

2018 ◽  
Vol 148 (24) ◽  
pp. 241702 ◽  
Author(s):  
Aditya Kamath ◽  
Rodrigo A. Vargas-Hernández ◽  
Roman V. Krems ◽  
Tucker Carrington ◽  
Sergei Manzhos
Keyword(s):  
Neural Networks ◽  
Potential Energy ◽  
Vibrational Spectrum ◽  
Comparative Study ◽  
Gaussian Process ◽  
Potential Energy Surfaces ◽  
Gaussian Process Regression ◽  
Energy Surfaces

scholarly journals Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

2022 ◽  
Author(s):  
Sergei Manzhos ◽  
Eita Sasaki ◽  
Manabu Ihara
Keyword(s):  
Machine Learning ◽  
Kinetic Energy ◽  
Potential Energy ◽  
Gaussian Process ◽  
Potential Energy Surfaces ◽  
Gaussian Process Regression ◽  
Multivariate Functions ◽  
Low Dimensional ◽  
Energy Surfaces ◽  
Kernel Design

Abstract We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.

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.

Vibrational spectrum of FHF− from high-level correlated calculations of potential energy surfaces

1989 ◽  
Vol 161 (6) ◽  
pp. 519-527 ◽  
Author(s):  
Vladimir Špirko ◽  
Geerd H.F. Diercksen ◽  
Andrzej J. Sadlej ◽  
Miroslav Urban
Keyword(s):  
Potential Energy ◽  
Vibrational Spectrum ◽  
Potential Energy Surfaces ◽  
High Level ◽  
Energy Surfaces
Sign in / Sign up

Export Citation Format

Share Document