Machine-Learning-Driven G-Quartet-Based Circularly Polarized Luminescence Materials

Circularly polarized luminescence (CPL) materials have garnered significant interest due to their potential applications in chiral functional devices. Synthesizing CPL materials with a high dissymmetry factor (glum) remains a significant challenge. Inspired by efficient machine learning (ML) applications in scientific research, this work demonstrates ML-based techniques for the first time to guide the synthesis of G-quartet-based CPL gels with high glum values and multiple chiral regulation strategies. Employing an "experiment-prediction-verification" approach, this work devises a ML classification and regression model for the solvothermal synthesis of G-quartet gels in deep eutectic solvents. This process illustrates the relationship between various synthesis parameters and the glum value. The decision tree algorithm demonstrates superior performance across six ML models, with model accuracy and determination coefficients amounting to 0.97 and 0.96, respectively. The screened CPL gels exhibiting a glum value up to 0.15 are obtained through combined ML guidance and experimental verification, among the highest ones reported till now for biomolecule-based CPL systems. These findings indicate that ML can streamline the rational design of chiral nanomaterials, thereby expediting their further development. This study pioneers 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 gels with a maximum glum value of 0.15.image