Curved-line search algorithm for ab initio atomic structure relaxation

被引：3
作者：
Chen, Zhanghui [1 ,2 ]
Li, Jingbo [2 ]
Li, Shushen [2 ]
Wang, Lin-Wang [1 ]
机构：
[1] Lawrence Berkeley Natl Lab, Div Mat Sci, One Cyclotron Rd,Mail Stop 50F, Berkeley, CA 94720 USA
[2] Chinese Acad Sci, Inst Semicond, State Key Lab Superlattices & Microstruct, POB 912, Beijing 100083, Peoples R China
来源：
PHYSICAL REVIEW B | 2017年 / 96卷 / 11期
关键词：
DENSITY-FUNCTIONAL THEORY; OPTIMIZATION; CLUSTERS; NANOCLUSTERS; METALS;
D O I：
10.1103/PhysRevB.96.115141
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Ab initio atomic relaxations often take large numbers of steps and long times to converge, especially when the initial atomic configurations are far from the local minimum or there are curved and narrow valleys in the multidimensional potentials. An atomic relaxation method based on on-the-flight force learning and a corresponding curved-line search algorithm is presented to accelerate this process. Results demonstrate the superior performance of this method for metal and magnetic clusters when compared with the conventional conjugate-gradient method.
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页数：5
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