Machine Learning for Organic Fluorescent Materials

Abstract

Organic fluorescent materials (OFMs), characterized by their unique molecular structures and exceptional optical properties, have demonstrated significant potential in diverse applications such as bioimaging, sensors, and display technologies. Nevertheless, the reliance on chemists' intuition and experience in the traditional design of OFMs, coupled with the high cost and lack of scalability of conventional methods such as fluorescence detection and Density Functional Theory (DFT) calculations, makes it difficult to keep up with the rapid development of the field. The advent of machine learning (ML) has introduced transformative possibilities, enabling data-driven exploration of the intricate relationships between molecular structures and fluorescence properties. Herein, we review the applications of ML in the innovative design of OFMs with an emphasis on the workflow of modeling, optical property prediction, and OFM design. We also discuss the critical role of data curation and feature engineering in enhancing model performance. Our review provides an overview of commonly used models and assesses their efficacy. We critically examine key challenges such as database construction, model interpretability, and generalization ability, trying to provide a comprehensive framework that advances the integration of ML in the research of organic fluorescent materials, thereby facilitating the development of next-generation materials.

Keywords

aggregation-induced emission / database construction / density functional theory / machine learning / organic fluorescent materials

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