Generative AI-Driven Innovation in Nanofiller Dispersion Optimization for Polymer Composites

EasyChair Preprint 14593

Abstract

The optimization of nanofiller dispersion in polymer composites is a crucial step in enhancing their mechanical, thermal, and electrical properties. However, traditional trial-and-error approaches are time-consuming and often yield suboptimal results. This study explores the potential of generative Artificial Intelligence (AI) in revolutionizing nanofiller dispersion optimization. By leveraging machine learning algorithms and generative models, we demonstrate the ability to predict and design optimal nanofiller dispersion patterns, leading to improved polymer composite performance. Our approach enables the rapid exploration of vast design spaces, uncovering novel dispersion strategies and accelerating the development of high-performance polymer composites. The integration of generative AI in nanofiller dispersion optimization paves the way for innovative applications in various industries, including aerospace, automotive, and energy.

Keyphrases: Artificial Intelligence, Nanofiller Dispersion, Polymer composites

@booklet{EasyChair:14593,
  author    = {Abi Cit},
  title     = {Generative AI-Driven Innovation in Nanofiller Dispersion Optimization for Polymer Composites},
  howpublished = {EasyChair Preprint 14593},
  year      = {EasyChair, 2024}}