Identification and optimization of AB2 phases using principal component analysis, evolutionary neural nets, and multiobjective genetic algorithms

Akash Agarwal, Frank Pettersson, Arunima Singh, Chang Sun Kong, Henrik Saxén, Krishna Rajan, Shuichi Iwata, Nirupam Chakraborti

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Available data for a large number of AB2 compounds were subjected to a rigorous study using a combination of Principal Component Analysis (PCA) technique, multiobjective genetic algorithms, and neural networks that evolved through genetic algorithms. The identification of various phases and phase-groups were very successfully done using a decision tree approach. Since the variable hyperspaces for the different phases were highly intersecting in nature, a cumulative probability index was defined for the formation of individual compounds, which was maximized along with Pauling's electronegativity difference. The resulting Pareto-frontiers provided further insight into the nature of bonding prevailing in these compounds.

Original languageEnglish (US)
Pages (from-to)274-281
Number of pages8
JournalMaterials and Manufacturing Processes
Volume24
Issue number3
DOIs
StatePublished - Mar 1 2009
Externally publishedYes

Keywords

  • AB2 compounds
  • Data mining
  • Decision tree
  • Evolutionary algorithm
  • Genetic algorithms
  • Laves phase
  • Multiobjective optimization
  • Neural network
  • Principal component analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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