Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks

2019 ◽  
Vol 10 (6) ◽  
pp. 1185-1191 ◽  
Author(s):  
Yaolong Zhang ◽  
Xueyao Zhou ◽  
Bin Jiang
Keyword(s):  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Direct Dynamics ◽  
Reactive Potential ◽  
Energy Surfaces

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.

Valence–Bond Order (VBO): A New Approach to Modeling Reactive Potential Energy Surfaces for Complex Systems, Materials, and Nanoparticles

2009 ◽  
Vol 5 (3) ◽  
pp. 594-604 ◽  
Author(s):  
Meiyu Zhao ◽  
Mark A. Iron ◽  
Przemysław Staszewski ◽  
Nathan E. Schultz ◽  
Rosendo Valero ◽  
...  
Keyword(s):  
Potential Energy ◽  
Complex Systems ◽  
Potential Energy Surfaces ◽  
Bond Order ◽  
Valence Bond ◽  
New Approach ◽  
Reactive Potential ◽  
Energy Surfaces

scholarly journals Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes

2017 ◽  
Vol 121 (13) ◽  
pp. 2552-2557 ◽  
Author(s):  
Brian Kolb ◽  
Paul Marshall ◽  
Bin Zhao ◽  
Bin Jiang ◽  
Hua Guo
Keyword(s):  
Potential Energy ◽  
Gaussian Processes ◽  
Potential Energy Surfaces ◽  
Reactive Potential ◽  
Energy Surfaces
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