Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems

Abstract

Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and Gradient Boosting, kernel methods such as SVR, neural networks, graph-based approaches, while it applies principled hyperparameter tuning and uncertainty quantification, along with model-interpretability tools such as SHAP and systematic ablation to identify the most important material and processing factors. The proposed methodology is demonstrated with curated, publicly available datasets, and we discuss means for synthetic data augmentation, cross-validation protocols, assessment of model robustness, and best practices in reporting results in a reproducible way. The results show that data-driven models reduce prediction error and speed up the processes of materials selection in view of Net Zero targets, given limited available experimental iterations; this informs intelligent lightweighting of renewable energy components. Furthermore, the study highlights the capability of machine learning models to capture complex nonlinear relationships between composition, processing parameters, and mechanical response that are difficult to address using conventional trial-and-error approaches. By reducing reliance on extensive experimental campaigns, the proposed framework supports faster design cycles and more efficient utilization of material and energy resources in renewable energy applications.