scholarly journals Machine learning for potential energy surfaces: An extensive database and assessment of methods

2019 ◽  
Vol 150 (24) ◽  
pp. 244113 ◽  
Author(s):  
Gunnar Schmitz ◽  
Ian Heide Godtliebsen ◽  
Ove Christiansen
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
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 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

Progress towards machine learning reaction rate constants

2021 ◽  
Author(s):  
Evan Komp ◽  
Nida Janulaitis ◽  
Stephanie Valleau
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Rate Constant ◽  
Rate Constants ◽  
Potential Energy Surfaces ◽  
Reaction Rate ◽  
Reaction Rate Constant ◽  
Reaction Rate Constants ◽  
The Cost ◽  
Energy Surfaces

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly becomes unfeasible as the system...

scholarly journals Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models

2018 ◽  
Vol 15 (1) ◽  
pp. 116-126 ◽  
Author(s):  
Zak E. Hughes ◽  
Joseph C. R. Thacker ◽  
Alex L. Wilson ◽  
Paul L. A. Popelier
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Learning Models ◽  
Energy Surfaces ◽  
Machine Learning Models
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