scholarly journals Gaussian process models of potential energy surfaces with boundary optimization

2021 ◽  
Vol 155 (14) ◽  
pp. 144106
Author(s):  
Jack Broad ◽  
Simon Preston ◽  
Richard J. Wheatley ◽  
Richard S. Graham
Keyword(s):  
Potential Energy ◽  
Gaussian Process ◽  
Potential Energy Surfaces ◽  
Process Models ◽  
Gaussian Process Models ◽  
Boundary Optimization ◽  
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 Active learning in Gaussian process interpolation of potential energy surfaces

2018 ◽  
Vol 149 (17) ◽  
pp. 174114 ◽  
Author(s):  
Elena Uteva ◽  
Richard S. Graham ◽  
Richard D. Wilkinson ◽  
Richard J. Wheatley
Keyword(s):  
Active Learning ◽  
Potential Energy ◽  
Gaussian Process ◽  
Potential Energy Surfaces ◽  
Energy Surfaces

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

Resolving the Quadruple Bonding Conundrum in C2 Using Insights Derived from Excited State Potential Energy Surfaces: A Molecular Orbital Perspective

2019 ◽  
Author(s):  
Ishita Bhattacharjee ◽  
Debashree Ghosh ◽  
Ankan Paul
Keyword(s):  
Excited State ◽  
Potential Energy ◽  
Molecular Orbital ◽  
High Spin ◽  
Potential Energy Surfaces ◽  
Valence Bond ◽  
Deep Minimum ◽  
State Potential ◽  
Energy Surfaces ◽  
Mo Theory

The question of quadruple bonding in C<sub>2</sub> has emerged as a hot button issue, with opinions sharply divided between the practitioners of Valence Bond (VB) and Molecular Orbital (MO) theory. Here, we have systematically studied the Potential Energy Curves (PECs) of low lying high spin sigma states of C<sub>2</sub>, N<sub>2</sub> and Be<sub>2</sub> and HC≡CH using several MO based techniques such as CASSCF, RASSCF and MRCI. The analyses of the PECs for the<sup> 2S+1</sup>Σ<sub>g/u</sub> (with 2S+1=1,3,5,7,9) states of C<sub>2</sub> and comparisons with those of relevant dimers and the respective wavefunctions were conducted. We contend that unlike in the case of N<sub>2</sub> and HC≡CH, the presence of a deep minimum in the <sup>7</sup>Σ state of C<sub>2</sub> and CN<sup>+</sup> suggest a latent quadruple bonding nature in these two dimers. Hence, we have struck a reconciliatory note between the MO and VB approaches. The evidence provided by us can be experimentally verified, thus providing the window so that the narrative can move beyond theoretical conjectures.

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