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Title: An adaptive genetic algorithm for crystal structure prediction

We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.
 [1] ;  [2] ;  [2] ;  [2] ;  [2] ;  [3] ;  [4] ;  [2]
  1. Xiamen Univ., (People's Republic of China); Ames Lab., Ames, IA (United States)
  2. Ames Lab., Ames, IA (United States)
  3. Ames Lab., Ames, IA (United States); Univ. of Minnesota, Minneapolis, MN (United States)
  4. Univ. of Minnesota, Minneapolis, MN (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
IS--J 8227
Journal ID: ISSN 0953-8984
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Physics. Condensed Matter; Journal Volume: 26; Journal Issue: 03
IOP Publishing
Research Org:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
36 MATERIALS SCIENCE Condensed matter: structural, mechanical & thermal