Optimizing Interface Area of Percolated Domains in Two Dimensional Binary Compound: Artificial Neural Network Modeling on Monte Carlo Experiments

This work investigated the competition effect between the cohesive and adhesive interaction on the domain (of the same atom) profiles with an emphasis on the percolation and interface in two-dimensional binary compound materials. The Ising model was used to represent the two types of the binary compound arranging on two-dimensional square lattice. Monte Carlo simulation and Kawasaki algorithm was used to thermally update the configuration of the system. With varying the system sizes, the environmental temperature, and adhesive to cohesive interaction ratio, the system microstructure were investigated to extract the average number of percolated domains and their interface via pairs of neighboring different-atoms. The results, displayed via the main effect plot, show the average number of percolated domain reduces with increasing the temperature and adhesive interaction strength. However, the normalized interface area decreases in larger system or in stronger adhesive interaction system. Nevertheless, with increasing the temperature, the normalized interface area results in peak where its maximum is due to the thermal fluctuation effect. Since the relationship among these considered dependent and independent parameter is very complex, to establish formalism that can represent this relationship is not trivial. Therefore, the artificial neural network (ANN) was used to create database of relationship among the considered parameters such that the optimized condition in retrieving desired results can be comprehensively set. Good agreement between the real targeted outputs and the predicted outputs from the ANN was found, which confirms the functionality of the artificial neural network on modeling the complex phenomena in this study. This work therefore presents another step in the understanding of how mixed interaction plays its role in binary compound and how a data mining technique assists development of enhanced understanding in materials science and engineering topics.

[1]  Gerard T. Barkema,et al.  Monte Carlo Methods in Statistical Physics , 1999 .

[2]  Kurt Binder,et al.  A Guide to Monte Carlo Simulations in Statistical Physics , 2000 .

[3]  Yongyut Laosiritaworn,et al.  Artificial neural network modeling of ceramics powder preparation: Application to NiNb2O6 , 2008 .

[4]  J. Springer,et al.  Improved three-dimensional optical model for thin-film silicon solar cells , 2004 .

[5]  M. Ramos,et al.  Understand the importance of molecular organization at polymer–polymer interfaces in excitonic solar cells , 2014 .

[6]  L. Lyon,et al.  Interfacial nonradiative energy transfer in responsive core-shell hydrogel nanoparticles. , 2001, Journal of the American Chemical Society.

[7]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[8]  K. Binder,et al.  A Guide to Monte Carlo Simulations in Statistical Physics: Preface , 2005 .

[9]  K. Kawasaki.  Diffusion Constants near the Critical Point for Time-Dependent Ising Models. I , 1966 .

[10]  M. Odén,et al.  Mechanical properties and machining performance of Ti1−xAlxN-coated cutting tools , 2005 .

[11]  Order–disorder in binary alloys: a theoretical description by use of the Ising model , 2001 .

[12]  Michael J. Berry,et al.  Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.

[13]  A. Planes,et al.  Unified mean-field study of ferro- and antiferromagnetic behavior of the Ising model with external field , 1997 .

[14]  Judith E. Dayhoff,et al.  Neural Network Architectures: An Introduction , 1989 .

[15]  Peter Jaeckel,et al.  Monte Carlo methods in finance , 2002 .

[16]  Henk W. J. Blöte,et al.  The simple-cubic lattice gas with nearest-neighbour exclusion: Ising universality , 1996 .