4.7 Article

Two improved electronegativity equalization methods for charge distribution in large scale non-uniform system

期刊

COMPUTERS & MATHEMATICS WITH APPLICATIONS
卷 81, 期 -, 页码 693-701

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2019.12.017

关键词

Molecular dynamics; Electronegativity equalization method; Non-uniform system

资金

  1. National Key Research and Development Program of China [2016YFB0600101]
  2. Natural Science Fund of China [91741103]

向作者/读者索取更多资源

This study proposes two versions of improved EEM, namely the locally equilibrated EEM and the subzone EEM, to enhance the accuracy of charge computing in MD simulations. Comparisons between the improved EEMs and the original EEM in uniform and non-uniform systems demonstrate that the improved EEMs successfully eliminate the effect of unreasonable long-range charge interaction encountered in the original EEM.
Nowadays, molecular dynamics (MD) using force fields has become an effective tool for physical and chemical problems. In the MD method, the classical Electronegativity Equalization Method (EEM) is widely accepted for atom charge computing, which may result in unreasonable predictions for large scale non-uniform systems. In order to improve the accuracy of charge computing in MD simulations, two versions of improved EEM for charge distribution calculation are proposed in the present work, without introducing new empirical parameters, namely the locally equilibrated EEM and the subzone EEM. The aim of the proposed methods is to eliminate the unreasonable interaction of the long-range charge in large scale non-uniform systems. Comparison between the two improved EEMs and the original EEM in both uniform and non-uniform systems shows that both the improved EEMs successfully eliminate the effect of the unreasonable long-range charge interaction which is inevitably encountered in the original EEM. (C) 2019 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据