New Perspectives on CO2–Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface

2020 ◽  
Vol 124 (9) ◽  
pp. 5174-5181 ◽  
Author(s):  
Marcos del Cueto ◽  
Xueyao Zhou ◽  
Linsen Zhou ◽  
Yaolong Zhang ◽  
Bin Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
High Dimensional

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

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials

2015 ◽  
Vol 17 (13) ◽  
pp. 8356-8371 ◽  
Author(s):  
Suresh Kondati Natarajan ◽  
Tobias Morawietz ◽  
Jörg Behler
Keyword(s):  
Neural Network ◽  
Density Functional Theory ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Density Functional ◽  
Energy Surface ◽  
Water Clusters ◽  
High Dimensional ◽  
Functional Theory ◽  
The Density Functional Theory

We report a reactive neural network potential for protonated water clusters that accurately represents the density-functional theory potential-energy surface.

An accurate full-dimensional potential energy surface for the reaction OH + SO → H + SO2

2021 ◽  
Vol 23 (1) ◽  
pp. 487-497
Author(s):  
Jie Qin ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network

An accurate full-dimensional PES for the OH + SO ↔ H + SO2 reaction is developed by the permutation invariant polynomial-neural network approach.

An accurate full-dimensional permutationally invariant potential energy surface for the interaction between H2O and CO

2019 ◽  
Vol 21 (43) ◽  
pp. 24101-24111 ◽  
Author(s):  
Yang Liu ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Polynomial Neural Network ◽  
Invariant Potential

The first full-dimensional accurate potential energy surface was developed for the CO + H2O system based on ca. 102 000 points calculated at the CCSD(T)-F12a/AVTZ level using a permutation invariant polynomial-neural network (PIP-NN) method.

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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