Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review

Abstract

Polymer composites are employed in a variety of applications due to their distinctive characteristics. Nevertheless, designing and optimizing these materials can be a lengthy and resourceintensive process for low cost and sustainable materials. Machine learning has the potential to simplify this process by offering predictions of the characteristics of novel composite materials based on their microstructures. This review outlines machine learning techniques and highlights the potential of machine learning to improve the design and optimization of polymer composites. This review also examines the difficulties and restrictions of utilizing machine learning in this context and offers insights into potential future research paths in this field.