Generative AI-Driven Design of Next-Generation Polymer Nanocomposite Structures

EasyChair Preprint 14575

Abstract

The advent of generative AI has revolutionized the field of materials science, enabling the design of next-generation polymer nanocomposite structures with unprecedented precision and efficiency. This paper presents a pioneering approach that leverages generative AI algorithms to create optimal nanocomposite architectures, surpassing traditional trial-and-error methods. By integrating machine learning, molecular dynamics simulations, and materials informatics, our framework predicts and optimizes the mechanical, thermal, and electrical properties of polymer nanocomposites. The results demonstrate a significant enhancement in material performance, paving the way for innovative applications in energy storage, aerospace, and biomedicine. This research showcases the transformative potential of generative AI-driven design in unlocking new frontiers in materials science and nanotechnology.

Keyphrases: Generative AI, machine learning, materials design, polymer nanocomposites

@booklet{EasyChair:14575,
  author    = {Abey Litty},
  title     = {Generative AI-Driven Design of Next-Generation Polymer Nanocomposite Structures},
  howpublished = {EasyChair Preprint 14575},
  year      = {EasyChair, 2024}}