Machine Learning Approaches for Enhancing Thermal Conductivity in Polymer Nanocomposites

EasyChair Preprint 14570

Abstract

Polymer nanocomposites have garnered significant attention in recent years due to their potential to enhance thermal conductivity, making them suitable for various applications, including electronics, energy storage, and thermal management systems. However, optimizing thermal conductivity in these materials remains a complex challenge. This study explores the application of machine learning approaches to enhance thermal conductivity in polymer nanocomposites. We employ a combination of experimental data and computational modeling to develop predictive models that relate material properties and thermal conductivity. Our results demonstrate that machine learning algorithms can effectively identify optimal nanofiller concentrations, dispersion patterns, and polymer matrices to achieve enhanced thermal conductivity. Furthermore, we investigate the potential of machine learning-driven design of new polymer nanocomposites with tailored thermal properties. This research contributes to the development of advanced materials with improved thermal conductivity, enabling innovative solutions for thermal management and energy applications

Keyphrases: Nanocomposites, Thermal, conductivity, machine learning, polymer

@booklet{EasyChair:14570,
  author    = {Abey Litty},
  title     = {Machine Learning Approaches for Enhancing Thermal Conductivity in Polymer Nanocomposites},
  howpublished = {EasyChair Preprint 14570},
  year      = {EasyChair, 2024}}