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

ABSTRACT: 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

SUBMITTER: Liang K 

PROVIDER: S-EPMC12758484 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature