Abstract
Accurate estimation technique that accommodates few data points is useful and desired in tackling the difficulties in experimental determination of surface energies of materials. We hereby propose a computational intelligence technique on the platform of support vector regression (SVR) using test-set-cross-validation method to develop surface energies estimator (SEE) that is capable of estimating the average surface energy of materials. The SEE was developed from SVR by training and testing the model using thirteen data points. The developed SEE was then used to estimate average surface energies of different classes of metals in periodic table. Comparison of our results with the experimental values and the surface energies obtained from other theoretical models show excellent agreement. The developed SEE can be a tool through which average surface energies of materials can be estimated as a result of its outstanding performance over the existing models. (C) 2015 Elsevier B.V. All rights reserved.