Machine‐Learning‐Driven G‐Quartet‐Based Circularly Polarized Luminescence Materials (Adv. Mater. 4/2024)

Machine Learning In article number 2310455, Lingyan Feng and co-workers pioneer the application of machine learning (ML) models to guide the synthesis of circularly polarized luminescence (CPL) gels characterized by a high dissymmetry factor (glum) and diverse chiral regulation methods. The relationship between synthesis parameters and the glum value is clarified using ML models, successfully forecasting and producing G-quartet-based CPL materials with a maximum glum value of 0.15.