scholarly journals Sampling strategy in efficient potential energy surface mapping for predicting atomic diffusivity in crystals by machine learning

2020 ◽  
Vol 101 (18) ◽  
Author(s):  
Kazuaki Toyoura ◽  
Takeo Fujii ◽  
Kenta Kanamori ◽  
Ichiro Takeuchi
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Sampling Strategy ◽  
Surface Mapping

scholarly journals A machine learning based intramolecular potential for a flexible organic molecule

2020 ◽  
Vol 224 ◽  
pp. 247-264 ◽  
Author(s):  
Daniel J. Cole ◽  
Letif Mones ◽  
Gábor Csányi
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Organic Molecule ◽  
Energy Surface ◽  
Kernel Regression ◽  
Quantum Mechanical ◽  
Machine Learning Technique ◽  
Analytical Potential ◽  
Learning Technique

Here, we employ the kernel regression machine learning technique to construct an analytical potential that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine.

scholarly journals Exploring a potential energy surface by machine learning for characterizing atomic transport

2018 ◽  
Vol 97 (12) ◽  
Author(s):  
Kenta Kanamori ◽  
Kazuaki Toyoura ◽  
Junya Honda ◽  
Kazuki Hattori ◽  
Atsuto Seko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Atomic Transport

scholarly journals An On-the-fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems

2020 ◽  
Author(s):  
zheng cheng ◽  
Zhao Dongbo ◽  
Jing Ma ◽  
Wei Li ◽  
Shuhua Li
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Active Learning ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Force Field ◽  
Ab Initio Calculation ◽  
Energy Surface ◽  
Speed Up ◽  
Ab Inito

The paper describes a modification to the generalized energy-based fragmentation (GEBF) method that uses a machine fitted potential energy surface for the subsytems instead of ab initio calculation, in order to speed up the calculations. An on-the-fly active learning is used to construct vaious kind of subsystems force field automatically. Our method can bpyss over 99% of the QM calculations during the ab inito molecular dynamics.

K–L resonant X-ray Raman scattering as a tool for potential energy surface mapping

2007 ◽  
Vol 439 (4-6) ◽  
pp. 402-406 ◽  
Author(s):  
S. Carniato ◽  
R. Taïeb ◽  
R. Guillemin ◽  
L. Journel ◽  
M. Simon ◽  
...  
Keyword(s):  
Potential Energy ◽  
Raman Scattering ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Surface Mapping ◽  
X Ray

scholarly journals An On-the-fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems

2020 ◽  
Author(s):  
zheng cheng ◽  
Zhao Dongbo ◽  
Jing Ma ◽  
Wei Li ◽  
Shuhua Li
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Active Learning ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Force Field ◽  
Ab Initio Calculation ◽  
Energy Surface ◽  
Speed Up ◽  
Ab Inito

The paper describes a modification to the generalized energy-based fragmentation (GEBF) method that uses a machine fitted potential energy surface for the subsytems instead of ab initio calculation, in order to speed up the calculations. An on-the-fly active learning is used to construct vaious kind of subsystems force field automatically. Our method can bpyss over 99% of the QM calculations during the ab inito molecular dynamics.

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