Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers

Accurate prediction of polymer material properties through data-driven approaches greatly accelerates novel material development by reducing redundant experiments and trial-and-error processes. To address this limitation, we propose a novel approach to enhance dataset quality efficiently by integrating multi-algorithm outlier detection with selective re-experimentation of unreliable outlier cases. To demonstrate its general applicability, we report the performance improvements across multiple machine learning models, including Elastic Net, SVR, Random Forest, and TPOT, to predict the three key properties. Our method reliably reduces prediction error (RMSE) and significantly improves accuracy with minimal additional experimental work, requiring only about 5% of the dataset to be re-measured. These findings highlight the importance of data quality enhancement in achieving reliable machine learning applications in polymer science and present a scalable strategy for improving predictive reliability in materials science. Introduction Epoxy adhesives are extensively utilized in a wide range of industries, including automotive, aerospace, and civil engineering, due to their robust adhesion to various substrates, exceptional mechanical properties, and high resistance to heat, corrosion, and chemicals 1-4 . Primarily composed of epoxy resin and hardener (curing agent), epoxy adhesives may incorporate additional additives, such as accelerators and fillers, for modification 5 . Epoxy adhesives are formulated by subjecting their compositions to a curing process, which can occur at room temperature, elevated temperature, or through alternative methods such as exposure to UV light 6 .