Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions.

Area of Science:

• Materials Science
• Computational Chemistry
• Data Science

Background:

• Dual-mode fluorescent materials are crucial for advanced applications like bioimaging, sensing, displays, and lighting.
• Traditional methods for optimizing these materials are costly, labor-intensive, and struggle with complex structure-property relationships.

Purpose of the Study:

• To systematically review the application of machine learning (ML) in predicting the performance of fluorescent materials.
• To summarize fundamental principles, methodologies, and applications of ML for fluorescent material design.

Main Methods:

• Covers core ML techniques including supervised regression, neural networks, and physics-informed hybrid frameworks.
• Analyzes representative fluorescent materials such as AIE luminogens, TADF emitters, quantum dots, carbon dots, perovskites, and inorganic phosphors.
• Details modeling workflows: data preprocessing, descriptor selection, model validation, and algorithmic optimization strategies like data augmentation and transfer learning.

Main Results:

• Machine learning offers a powerful alternative to traditional methods for predicting fluorescent material performance.
• Various ML models and workflows have been successfully applied to diverse classes of fluorescent materials.
• Identifies key strategies for enhancing ML model performance and applicability.

Conclusions:

• Machine learning significantly enhances the efficiency and accuracy of fluorescent material design and discovery.
• Addresses challenges such as data scarcity, model interpretability, and transferability to advance ML in materials science.
• Provides a comprehensive overview and future outlook for ML-driven fluorescent material research.