An improved sampling strategy for global energy minimization of multi-component systems

Richard Otis, Maria Emelianenko, Zi Kui Liu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Effective initial sampling plays an important role in capturing key details about the energy surfaces of multi-component, multi-sublattice phases for the purposes of accurate convergence toward the global minimum energy configuration of a given system. It is shown that, when using the appropriate statistical distribution, both quasi-random and pseudo-random sampling methods compare well with the standard uniform grid-based technique. Moreover, the combination of random sampling with uniform grid points, while maintaining sampling performance for equilibrium calculations in the Al-Co-Cr system, significantly increases performance for a fictive 10-component system.

Original languageEnglish (US)
Pages (from-to)282-291
Number of pages10
JournalComputational Materials Science
Volume130
DOIs
StatePublished - Apr 1 2017

Fingerprint

random sampling
Global Minimization
Multicomponent Systems
Sampling Strategy
Energy Minimization
Random Sampling
sampling
grids
Sampling
optimization
statistical distributions
sublattices
surface energy
Grid
Statistical Distribution
Surface Energy
Global Minimum
Sampling Methods
energy
configurations

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

Cite this

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An improved sampling strategy for global energy minimization of multi-component systems. / Otis, Richard; Emelianenko, Maria; Liu, Zi Kui.

In: Computational Materials Science, Vol. 130, 01.04.2017, p. 282-291.

Research output: Contribution to journalArticle

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