Optimizing thermal conductivity in functionalized macromolecules using Langevin dynamics and the globalized and bounded Nelder-Mead algorithm

• Published in: Physical review. E, Statistical, nonlinear, and soft matter physics Vol. 89; no. 5; p. 053313
• Main Authors: Ait Moussa, Abdellah, Jassemnejad, Bahaeddin
• Format: Journal Article
• Language: English
• Published: United States 01-05-2014
• ISSN: 1539-3755; 1550-2376
• DOI: 10.1103/PhysRevE.89.053313

Nanocomposites with high-aspect ratio fillers attract enormous attention because of the superior physical properties of the composite over the parent matrix. Nanocomposites with functionalized graphene as fillers did not produce the high thermal conductivity expected due to the high interfacial thermal resistance between the functional groups and graphene flakes. We report here a robust and efficient technique that identifies the configuration of the functionalities for improved thermal conductivity. The method combines linearization of the interatomic interactions, calculation, and optimization of the thermal conductivity using the globalized and bounded Nelder-Mead algorithm.