scholarly journals Thermodynamic rules for zeolite formation from machine learning based global optimization

2020 ◽  
Vol 11 (37) ◽  
pp. 10113-10118
Author(s):  
Sicong Ma ◽  
Cheng Shang ◽  
Chuan-Ming Wang ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Atomic Simulation ◽  
Zeolite Formation ◽  
Zeolite P

Machine learning based atomic simulation explores more than one million minima from global potential energy surface of SiAlPO system, and identifies thermodynamics rules on energetics, framework and composition for stable zeolite.

scholarly journals Atomic structure of boron resolved using machine learning and global sampling

2018 ◽  
Vol 9 (46) ◽  
pp. 8644-8655 ◽  
Author(s):  
Si-Da Huang ◽  
Cheng Shang ◽  
Pei-Lin Kang ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Atomic Structure ◽  
Energy Surface ◽  
First Time

Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of β-B.

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.

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