Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

Abstract

There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Machine-learning (ML) algorithms are gaining attention as cost-effective scalable solutions to exploit the physical and chemical structures of polymers. The basic steps for applying ML in polymer science such as fingerprinting, algorithms, open-source databases, representations, and polymer design are detailed in this review. Further, a state-of-the-art review of the prediction of various polymer material properties using ML is reviewed. Finally, we discuss open-ended research questions on ML application to PCR polymers as well as potential challenges in the prediction of their properties using artificial intelligence for more efficient and targeted PCR polymer discovery and development.

PolyNC: a natural and chemical language model for the prediction of unified polymer properties

“…In the past, attempts to solely rely on language models for polymer property prediction tasks were hindered by the scarcity and unattainability of high-quality labeled polymer datasets, 37 while the availability of high-quality open-source polymer datasets is steadily increasing. [38][39][40][41] More encouragingly, extensive work has shown that data augmentationbased approaches are effective in addressing the scarcity of polymer data, 15,42,43 and harnessing the intelligence of general language models proves benecial for comprehending scientic language via language models. [44][45][46][47] To the best of our knowledge, a completely end-to-end language-based approach for directly predicting the properties of polymers from natural and chemical languages, rather than being used as intermediates to connect molecular structures to downstream models, is currently lacking.…”

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

“…Another critical research direction is the application of ML in biopolymer recycling and waste reduction. ,, As the world grapples with plastic pollution, biopolymers offer a sustainable alternative. ML can be leveraged to improve recycling processes, optimize waste management systems, and develop new biodegradable materials.…”

Application of Digital Methods in Polymer Science and Engineering

“…[ 146 ] One of these describes the use of ML to predict the properties of recycled polymers, providing emphasis on fingerprinting, algorithms, open‐source databases, representations, and polymer design. [ 147 ] However, there are only a very few literature reports focusing on the optimization of polymer processing parameters related to, for example, melt processing, extrusion, or injection molding.…”