Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

2020 ◽  
Vol 124 (15) ◽  
pp. 3027-3035 ◽  
Author(s):  
Zhilong Wang ◽  
Yanqiang Han ◽  
Jinjin Li ◽  
Xiao He
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Accurate Calculation ◽  
Protein Energy ◽  
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.

An efficient and robust procedure to calculate absorption spectra of aqueous charged species applied to NO2-

2021 ◽  
Author(s):  
Lina Uribe ◽  
Sara Gómez ◽  
Tommaso Giovannini ◽  
Franco Egidi ◽  
Albeiro Restrepo
Keyword(s):  
Potential Energy ◽  
Absorption Spectra ◽  
Potential Energy Surfaces ◽  
Quantum Potential ◽  
Water Molecules ◽  
Accurate Calculation ◽  
Charged Species ◽  
Energy Surfaces ◽  
Explicit Water

Accurate calculation of absorption spectra of aqueous NO2- requires rigorously sampling the quantum potential energy surfaces for microsolvation of NO2- with at least five explicit water molecules and embedding the...

Calculations of the active mode and energetic barrier to electron attachment to CF3 and comparison with kinetic modeling of experimental results

2016 ◽  
Vol 18 (45) ◽  
pp. 31064-31071 ◽  
Author(s):  
Huixian Han ◽  
Benjamin Alday ◽  
Nicholas S. Shuman ◽  
Justin P. Wiens ◽  
Jürgen Troe ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Kinetic Modeling ◽  
Potential Energy Surfaces ◽  
Electron Attachment ◽  
Invariant Polynomial ◽  
Active Mode ◽  
Polynomial Neural Network ◽  
Energy Surfaces

Six-dimensional potential energy surfaces of both CF3 and CF3− were developed by fitting ∼3000 ab initio points using the permutation invariant polynomial-neural network (PIP-NN) approach.

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