Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

A Agarwal, Frank Pettersson, A Singh, CS Kong, Henrik Saxén, K Rajan, S Iwata, N Chakraborti

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)


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 languageUndefined/Unknown
Pages (from-to)274–281
Number of pages8
JournalMaterials and Manufacturing Processes
Issue number3
Publication statusPublished - 2009
MoE publication typeA1 Journal article-refereed


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

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