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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...
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...
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