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The development of neural network potentials for cubic sodium chloride crystals with vacancies

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Abstract
We implement a method for computing the interatomic potentials by training and ftting neural networks with the data obtain from molecular dynamics simulations. We construct a longrange neural network potential and apply it to NaCl system including ideal and defected systems. We keep the short-range part with Behler type and the long-range part is calculated using the Ewald summation. The reason for choosing the Ewald sum is because it provides high accuracy and feasible computational speed when estimating the long-range potential, due to the rapid convergence between long-range contribution in reciprocal space and the shortrange contribution in real space. In this work, we not only compare the result to molecular dynamics simulations but also adopt them into the training set of neural network.
Author(s)
도안 티 수언 로언
Issued Date
2019
Awarded Date
2020-02
Type
Dissertation
Keyword
artificial neural networklong-range neural network potentialsEwald sum
URI
https://oak.ulsan.ac.kr/handle/2021.oak/6340
http://ulsan.dcollection.net/common/orgView/200000292387
Alternative Author(s)
Doan Thi Xuan Loan
Affiliation
울산대학교
Department
일반대학원 물리학과
Advisor
Young-Han Shin
Degree
Master
Publisher
울산대학교 일반대학원 물리학과
Language
eng
Rights
울산대학교 논문은 저작권에 의해 보호받습니다.
Appears in Collections:
Physics > 1. Theses (Master)
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