Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes

TL;DR

This review discusses how machine learning accelerates polymer material research by improving property prediction and design, while addressing challenges like data quality and model interpretability.

Contribution

It provides a comprehensive overview of machine learning applications in polymer materials, including methodologies, data strategies, and future research directions.

Findings

Machine learning significantly speeds up polymer property prediction and design.

Key technologies include molecular descriptors, data standardization, and high-quality databases.

Future trends involve multi-scale modeling and interpretable machine learning.

Abstract

This paper systematically reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic technologies such as molecular descriptors and feature representation, data standardization and cleaning, and records a number of high-quality polymer databases. Subsequently, it elaborates on the key role of machine learning in polymer property prediction and material design, covering the specific applications of algorithms such as traditional machine learning, deep learning, and transfer learning; further, it deeply expounds on data-driven design strategies, such as reverse design, high-throughput virtual screening, and multi-objective optimization. The paper also systematically introduces the complete process of constructing high-reliability machine learning models and summarizes effective experimental verification, model evaluation, and optimization methods. Finally, it summarizes the current technical challenges in research, such as data quality and model generalization ability, and looks forward to future development trends including multi-scale modeling, physics-informed machine learning, standardized data sharing, and interpretable machine learning.