Computational intelligence method of estimating solid-liquid interfacial energy of materials at their melting temperatures

The reversible work required in forming an interface between a crystal and its coexisting liquid plays significant roles in many phase transformation and controls many processes such as nucleation, crystal growth, surface roughening and surface melting among others. Despite these significances, its experimental determination is difficult and only few experimental results are available in the literatures. This present work aims at circumventing these experimental challenges by developing a computational intelligence (CI) based model that relates solid-liquid interfacial energies of materials with their melting temperatures using support vector regression (SVR) with test-set cross validation optimization technique. The results of the developed CI-based model show persistent closeness to the few available experimental data than other compared existing theoretical models such as Miedema and den Broeder model, Granasy and Tegze model, Jiang combined model and Ewing model. The outstanding performance of the developed CI-based model as well as its implementation which only needs the value of melting temperature of the concerned material, is of immense importance in circumventing the experimental challenges in the practical attainment of equilibrium between a crystal and its melt for solid-liquid interfacial energy determination.