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

Tutkimustuotos: LehtiartikkeliArtikkeliTieteellinenvertaisarvioitu

18 Sitaatiot (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.
AlkuperäiskieliEi tiedossa
JulkaisuMaterials and Manufacturing Processes
DOI - pysyväislinkit
TilaJulkaistu - 2009
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu


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