Boosting-Based Machine Learning Applications in Polymer Science: A Review

The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest in machine learning (ML) methods to aid in data analysis, material design, and predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost and LightGBM, have emerged as powerful tools for tackling high-dimensional and complex problems in polymer science. This paper provides an overview of the applications of boosting methods in polymer science, highlighting their contributions to areas such as structure-property relationships, polymer synthesis, performance prediction, and material characterization. By examining recent case studies on the applications of boosting techniques in polymer science, this review aims to highlight their potential for advancing the design, characterization, and optimization of polymer materials.