scholarly journals Choosing the right molecular machine learning potential

2021 ◽  
Author(s):  
Max Pinheiro Jr ◽  
Fuchun Ge ◽  
Nicolas Ferré ◽  
Pavlo O. Dral ◽  
Mario Barbatti
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Quantum Chemistry ◽  
Potential Energy Surfaces ◽  
Reaction Rates ◽  
Learning Potential ◽  
Physicochemical Processes ◽  
The Right ◽  
Energy Surfaces ◽  
Insight Into

Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra....

scholarly journals DIFFUSION MONTE CARLO USING MACHINE LEARNING POTENTIAL ENERGY SURFACES

2021 ◽  
Author(s):  
Ryan DiRisio ◽  
Anne McCoy ◽  
Fenris Lu ◽  
Jacob Finney ◽  
Mark Boyer
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Learning Potential ◽  
Diffusion Monte Carlo ◽  
Energy Surfaces

scholarly journals Energy, orbital and structural stacking landscape of a purine homodimer system

2020 ◽  
Vol 139 (10) ◽  
Author(s):  
Tomasz Sierański
Keyword(s):  
Potential Energy ◽  
Intermolecular Interactions ◽  
Potential Energy Surfaces ◽  
Natural Bond Orbital ◽  
Natural Bond Orbital Analysis ◽  
Energy Sources ◽  
Energy Decomposition ◽  
Energy Surfaces ◽  
Orbital Analysis ◽  
Insight Into

Abstract The multidimensional study, combining the extensive calculations of potential energy surfaces for the parallel-displaced configurations and methods such as energy decomposition and natural bond orbital analysis, has been carried out. The resulted data give an energy, orbital and structural landscapes of this biologically essential system. The balance of the two energy sources, electrostatic and dispersion, is clearly visible. The obtained results, taken as a whole, provide an insight into the hierarchy of intermolecular interactions in the purine system, together with their sources.

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 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 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
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