Accelerating organic luminescent material discovery: from quantum chemical calculations to machine learning

Area of Science:

• Materials Science
• Physical Chemistry
• Computational Chemistry

Background:

• Organic luminescent materials are crucial for optoelectronics, sensing, and diagnostics.
• Rational design is hindered by complex photophysics, conflicting parameters, and vast chemical space.

Purpose of the Study:

• To review recent advances in rational design of organic luminescent materials.
• To explore the use of quantum mechanics and machine learning (ML) in this field.

Main Methods:

• Systematic review of literature on organic luminescent materials.
• Application of quantum mechanics calculations to establish molecular descriptors.
• Utilizing machine learning (ML) for high-throughput screening.

Main Results:

• Key molecular descriptors bridge electronic structure and photophysical properties.
• ML provides a framework for discovering high-performance luminescent materials.
• Decoupling of conflicting performance factors like quantum efficiency and lifetime.

Conclusions:

• Quantum mechanics and ML facilitate the precise design of organic luminescent materials.
• This approach enables on-demand customization of advanced functional materials.
• Future research can accelerate the discovery of novel luminescent systems.