Generative AI for Designing Self-Healing Properties in Polymer Nanocomposites

EasyChair Preprint 14663

Abstract

The integration of Generative AI in designing polymer nanocomposites with self-healing properties has revolutionized the field of materials science. This innovative approach leverages machine learning algorithms to optimize the composition, structure, and functionality of nanocomposites, enabling the creation of materials that can autonomously repair damage and restore their integrity. By predicting the behavior of various polymer-nanoparticle combinations, Generative AI streamlines the design process, reducing the need for trial-and-error experiments and accelerating the development of self-healing materials. This technology has far-reaching implications for various industries, including aerospace, automotive, and biomedical engineering, where durability and sustainability are paramount. This paper explores the potential of Generative AI in designing self-healing polymer nanocomposites, highlighting its benefits, challenges, and future prospects.

Keyphrases: Generative AI, Self-healing materials, polymer nanocomposites

@booklet{EasyChair:14663,
  author    = {Abill Robert},
  title     = {Generative AI for Designing Self-Healing Properties in Polymer Nanocomposites},
  howpublished = {EasyChair Preprint 14663},
  year      = {EasyChair, 2024}}