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 comp...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
Springer Verlag
2015
|
| Subjects: | |
| Online Access: | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84906536296&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38763 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Summary: | 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. © Springer International Publishing Switzerland 2015. |
|---|