Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Exploration of polymer materials using machine learning (ML) has been actively demonstrated for wide applications, while the multiple‐purpose ML and property prediction using small data is challenging. Herein, we propose a low‐cost refractive index prediction model of polymers using a multiple regression model with domain knowledge and variance inflation factor, enabling higher prediction accuracy (MAE = 0.03) and extrapolability even with a small dataset ( n ~ 40). Through combining this prediction model with the multi‐objective Bayesian optimization (MOBO) and the generative model, several polymer candidates with both high refractive index ( n D > 1.75) and visible light transparency are predicted and experimentally demonstrated. Overall, this work provides a less time‐/cost‐consuming approach for the accurate property prediction and the simultaneous maximization of multiple properties in polymers, even if only a small‐scale database is available.