scholarly journals Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

2016 ◽  
Vol 93 (5) ◽  
Author(s):  
Kazuaki Toyoura ◽  
Daisuke Hirano ◽  
Atsuto Seko ◽  
Motoki Shiga ◽  
Akihide Kuwabara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Region ◽  
Energy Surface ◽  
Proton Conduction ◽  
Sampling Procedure ◽  
Low Energy ◽  
Selective Sampling

scholarly journals Probing the location of the unpaired electron in spin–orbit changing collisions of NO with Ar

2020 ◽  
Vol 22 (39) ◽  
pp. 22289-22301
Author(s):  
Cornelia G. Heid ◽  
Imogen P. Bentham ◽  
Victoria Walpole ◽  
Razvan Gheorghe ◽  
Pablo G. Jambrina ◽  
...  
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Unpaired Electron ◽  
Energy Surface ◽  
Spin Orbit ◽  
Selective Sampling

The ability to orient NO molecules prior to collision with Ar atoms allows selective sampling of different potential energy surface regions and elucidation of the associated collision pathways.

scholarly journals Exact quantum dynamics background of dispersion interactions: case study for CH4·Ar in full (12) dimensions

2020 ◽  
Vol 22 (5) ◽  
pp. 2792-2802
Author(s):  
Gustavo Avila ◽  
Dóra Papp ◽  
Gábor Czakó ◽  
Edit Mátyus
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Quantum Dynamics ◽  
Energy Surface ◽  
Van Der Waals ◽  
Dispersion Interactions ◽  
Van Der Waals Complex ◽  
Exact Quantum

A full-dimensional ab initio potential energy surface is developed and utilized in full-dimensional variational vibrational computations for the CH4·Ar van-der-Waals complex.

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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