Sammanfattning
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.
Originalspråk | Odefinierat/okänt |
---|---|
Sidor (från-till) | 274–281 |
Antal sidor | 8 |
Tidskrift | Materials and Manufacturing Processes |
Volym | 24 |
Nummer | 3 |
DOI | |
Status | Publicerad - 2009 |
MoE-publikationstyp | A1 Tidskriftsartikel-refererad |
Nyckelord
- AB2 compounds
- Data mining
- Decision tree
- Evolutionary algorithm
- Genetic algorithms
- Laves phase
- Multiobjective optimization
- Neural network
- Principal component analysis