Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms

Baidurya Bhattacharya, G. R. Dinesh Kumar, Akash Agarwal, Şakir Erkoç, Arunima Singh, Nirupam Chakraborti

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

Failure behavior of Zn coated Fe is simulated through molecular dynamics (MD) and the energy absorbed at the onset of failure along with the corresponding strain of the Zn lattice are computed for different levels of applied shear rate, temperature and thickness. Data-driven models are constructed by feeding the MD results to an evolutionary neural network. The outputs of these neural networks are utilized to carry out a multi-objective optimization through genetic algorithms, where the best possible tradeoffs between two conflicting requirements, minimum deformation and maximum energy absorption at the onset of failure, are determined by constructing a Pareto frontier.

Original languageEnglish (US)
Pages (from-to)821-827
Number of pages7
JournalComputational Materials Science
Volume46
Issue number4
DOIs
StatePublished - Oct 1 2009
Externally publishedYes

Keywords

  • Artificial neural networks
  • Fe-Zn system
  • Genetic algorithms
  • Hot-dip galvanizing
  • Molecular dynamics
  • Multi-objective optimization

ASJC Scopus subject areas

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

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