Ex) Article Title, Author, Keywords
New Phys.: Sae Mulli 2020; 70: 398-404
Published online May 29, 2020 https://doi.org/10.3938/NPSM.70.398
Copyright © New Physics: Sae Mulli.
Doukyun KIM1, Chul Hong PARK2*
1Department of Physics, Pusan National University, Pusan 46241, Korea
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
We examined a machine learning method to extract the atom-pair interaction potential energy in materials. The data for the atomic structures and the corresponding total energies were generated by using the ab-initio molecular dynamic simulation, by which the artificial neural network (ANN) was trained to predict the total energies of the materials. Two ANNs were assigned: one to simulate (i) the dependence of the atom-pair interaction energy on the distance between the nearest atoms and the other to simulate (ii) the angular distortion energy. We found that compared to the true energies the total energies of Si could be successfully predicted with an error of about 1 meV/atom for atomic structures generated at 300 K, and that the dependence of the atomic interaction energy on the distance and the angular distortion energy could be obtained by training an ANN for atomic structures of various volumes.
Keywords: Neural network, Atom-pair potential, Machine-learning, Total energy, Si