Predictive Modeling of Bio-Based Polymer Nanocomposites Using Artificial Intelligence and Machine Learning

EasyChair Preprint 14503

Abstract

Bio-based polymer nanocomposites represent a promising class of materials with enhanced mechanical, thermal, and barrier properties, offering sustainable alternatives to conventional synthetic polymers. However, optimizing the properties of these nanocomposites poses significant challenges due to the complex interactions between the polymer matrix and the nanoscale fillers. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to develop predictive models that accurately forecast the properties of bio-based polymer nanocomposites based on their composition, processing parameters, and nanofiller characteristics. By leveraging large datasets from experimental studies and high-throughput simulations, AI and ML algorithms are trained to identify critical patterns and correlations within the material's structure-property relationships. The resulting predictive models enable the efficient design and optimization of bio-based nanocomposites, reducing the need for time-consuming and costly experimental trials. This research not only accelerates the development of high-performance bio-based materials but also contributes to the broader adoption of sustainable materials in various industrial applications. The study underscores the transformative potential of AI and ML in advancing material science, particularly in the realm of bio-based polymer nanocomposites.

Keyphrases: Artificial Intelligence, Bio-based polymer nanocomposites, machine learning

@booklet{EasyChair:14503,
  author    = {Abey Litty},
  title     = {Predictive Modeling of Bio-Based Polymer Nanocomposites Using Artificial Intelligence and Machine Learning},
  howpublished = {EasyChair Preprint 14503},
  year      = {EasyChair, 2024}}