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Title: Curved-line search algorithm for ab initio atomic structure relaxation

Abstract

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.

Authors:
 [1];  [2];  [2];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division; Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Superlattices and Microstructures
  2. Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Superlattices and Microstructures
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division
Publication Date:
Research Org.:
Oak Ridge National Laboratory, Oak Ridge Leadership Computing Facility (OLCF); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1544368
DOE Contract Number:  
AC02-05-CH11231
Resource Type:
Journal Article
Journal Name:
Physical Review B
Additional Journal Information:
Journal Volume: 96; Journal Issue: 11; Journal ID: ISSN 2469-9950
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English

Citation Formats

Chen, Zhanghui, Li, Jingbo, Li, Shushen, and Wang, Lin-Wang. Curved-line search algorithm for ab initio atomic structure relaxation. United States: N. p., 2017. Web. doi:10.1103/PhysRevB.96.115141.
Chen, Zhanghui, Li, Jingbo, Li, Shushen, & Wang, Lin-Wang. Curved-line search algorithm for ab initio atomic structure relaxation. United States. doi:10.1103/PhysRevB.96.115141.
Chen, Zhanghui, Li, Jingbo, Li, Shushen, and Wang, Lin-Wang. Fri . "Curved-line search algorithm for ab initio atomic structure relaxation". United States. doi:10.1103/PhysRevB.96.115141.
@article{osti_1544368,
title = {Curved-line search algorithm for ab initio atomic structure relaxation},
author = {Chen, Zhanghui and Li, Jingbo and Li, Shushen and Wang, Lin-Wang},
abstractNote = {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.},
doi = {10.1103/PhysRevB.96.115141},
journal = {Physical Review B},
issn = {2469-9950},
number = 11,
volume = 96,
place = {United States},
year = {2017},
month = {9}
}

Works referenced in this record:

Systematic Study of Au 6 to Au 12 Gold Clusters on MgO(100) F Centers Using Density-Functional Theory
journal, March 2012


Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
journal, October 1996


QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
journal, September 2009

  • Giannozzi, Paolo; Baroni, Stefano; Bonini, Nicola
  • Journal of Physics: Condensed Matter, Vol. 21, Issue 39, Article No. 395502
  • DOI: 10.1088/0953-8984/21/39/395502