Enhancing the Mechanical Properties of Bio-Based Polymer Nanocomposites through Advanced Machine Learning Algorithms.

EasyChair Preprint 14515

Abstract

The growing demand for sustainable materials has driven significant interest in bio-based polymer nanocomposites, which offer a promising alternative to traditional petrochemical-based polymers. However, optimizing the mechanical properties of these materials remains a complex challenge due to the intricate interplay between polymer matrices and nanofillers. This study explores the potential of advanced machine learning (ML) algorithms to enhance the mechanical properties of bio-based polymer nanocomposites. By leveraging large datasets of experimental and simulated material properties, we develop predictive models that can accurately forecast the mechanical behavior of these nanocomposites under various conditions. The ML models are trained to identify critical factors influencing strength, elasticity, and toughness, enabling the design of composites with superior mechanical performance. Additionally, the study examines the potential of generative algorithms to suggest novel material compositions that maximize desired properties. The results demonstrate that ML-driven approaches can significantly accelerate the development and optimization of bio-based polymer nanocomposites, paving the way for more resilient and sustainable materials in a wide range of applications.

Keyphrases: Bio-based polymer nanocomposites, machine learning, mechanical properties

@booklet{EasyChair:14515,
  author    = {Abey Litty},
  title     = {Enhancing the Mechanical Properties of Bio-Based Polymer Nanocomposites through Advanced Machine Learning Algorithms.},
  howpublished = {EasyChair Preprint 14515},
  year      = {EasyChair, 2024}}