Title: Estimation of surface energies of face-centred cubic metals using computational intelligence technique

Authors: Taoreed O. Owolabi; Kabiru O. Akande; Sunday O. Olatunji

Addresses: Physics Department, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia; Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, Ondo State Nigeria ' Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia ' Computer Science Department, University of Dammam, Dammam, Kingdom of Saudi Arabia

Abstract: There are numerous challenges associated with the experimental determination of average surface energies of materials despite the significances of material surface energy in understanding oxidation, catalysis, corrosion, crystal growth and adsorption. This research work aims at circumventing these challenges by developing a computational intelligent model using support vector regression (SVR) with test-set-cross validation optimisation technique. SVR-based model was developed by training and testing SVR using experimental data of selected thirty-four periodic metals. The developed SVR-based model was used to estimate average surface energies of face-centred cubic (fcc) metals and the obtained values were compared with the available experimental results. Average surface energies obtained from the developed SVR-based model show consistent closeness with the experimental values than the results of other existing theoretical models. The accuracy attained by the developed model shows its excellent potential in circumventing the difficulties associated with experimental determination of average surface energies of materials.

Keywords: support vector regression; SVR; surface energy; descriptors; face-centred cubic metals; computational intelligence; modelling.

DOI: 10.1504/IJMATEI.2015.072855

International Journal of Materials Engineering Innovation, 2015 Vol.6 No.4, pp.272 - 287

Available online: 04 Nov 2015 *

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