中国物理学会期刊网
物理  2017, Vol.46 Issue (9): 582-589  DOI:10.7693/wl20170902
基于智能全局优化算法的理论结构预测
(吉林大学物理学院 超硬材料国家重点实验室 长春 130012)
Structure prediction via intelligent global optimization algorithms
(State Key Lab of Superhard Materials,College of Physics,Jilin University,Changchun 130012,China)

摘要

凝聚态物质内部的原子堆垛方式,即微观原子结构,是深入理解其各种宏观物理和化学性质的基础。近年来,随着基于群智理论的全局优化算法和第一性原理计算方法的发展,只根据物质的化学组分和外界条件,通过理论计算来确定或预测物质的微观原子结构成为可能。文章将对目前国内外主要理论结构预测方法进行简要的概述,重点介绍基于群智算法的卡里普索(CALYPSO)结构预测方法的基本原理及其在凝聚态物质结构研究中的一些典型应用。

Abstract

Atomic structure is the basis for deep understanding of various physical and chemical properties of condensed matter. In recent years, with the rapid development of global optimization algorithms and first-principles methods, it has become possible to determine or predict the atomic structure through theoretical calculations with only information about the chemical composition and external conditions. This article will briefly introduce the basic principles of current theoretical structure prediction methods, with particular emphasis on the swarm-intelligence based CALYPSO method and its applications.
收稿日期:2017-08-09

引用本文

[中文]
高朋越,吕健,王彦超,马琰铭. 基于智能全局优化算法的理论结构预测[J]. 物理, 2017, 46(9): 582-589.
[英文]
GAO Peng-Yue,LV Jian,WANG Yan-Chao,MA Yan-Ming. Structure prediction via intelligent global optimization algorithms[J]. Physics, 2017, 46(9): 582-589.
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