Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes.

Self-healing polyurethanes (PUs) exhibit an inherent trade-off between mechanical strength and self-healing efficiency. Although optimizing the feed ratio can address the above limitations, identifying an appropriate ratio through trial-and-error is not straightforward. Machine learning offers promising approaches for composition-property optimization. However, the multi-property optimization for PUs with specific ingredients remains challenging, especially with minimal samples. A chemically-informed active learning (CIAL) framework is developed that integrates domain knowledge with machine learning to optimize fluorescent self-healing PU with merely 20 experimental datasets. By combining gradient boosting regression with multi-objective optimization, the framework successfully achieves co-optimization of mechanical properties and self-healing efficiency, with the relative error of the comprehensive performance index below 12%. The framework's efficiency is further highlighted to achieve optimal results with only 15 samples when discrete performance data are available. The developed P20B sample serves as an intelligent protective coating for Q235, simultaneously achieving real-time fluorescence visualization of damage sites and long-term anti-corrosion. This work provides an innovative solution for intelligent design of polymer materials using tiny experimental data.