A datamining approach to classify, select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternar...

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Bibliographic Details
Published in:International journal of hydrogen energy 2018-10, Vol.43 (41), p.19111-19120
Main Authors: Djellouli, A., Benyelloul, K., Aourag, H., Bekhechi, S., Adjadj, A., Bouhadda, Y., ElKedim, O.
Format: Article
Language:English
Subjects:
Artificial neural network
Datamining approach
Formability
Intermetallic compounds
Principal component analysis
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Summary:In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (RA), Pauling's electronegativity (χA) and atomic electron density (ZA/RA3). From 76 combinations, 55 systems which have χA ≥ 1.5, ZA/RA3>1.28 and RA 
ISSN:0360-3199
1879-3487
DOI:10.1016/j.ijhydene.2018.08.122