A unified active learning framework for photosensitizer design.

Area of Science:

• Materials Science
• Computational Chemistry
• Renewable Energy

Background:

• Designing efficient photosensitizers for solar energy is challenging due to vast chemical spaces and computational costs.
• Existing machine learning methods face data scarcity and inefficient molecular exploration.

Purpose of the Study:

• To develop a unified active learning framework for accelerated photosensitizer discovery.
• To integrate semi-empirical quantum calculations with adaptive screening strategies.
• To overcome limitations in traditional quantum chemistry and machine learning approaches.

Main Methods:

• A hybrid quantum mechanics/machine learning pipeline for accurate, cost-effective molecular dataset generation.
• A graph neural network architecture with uncertainty quantification.
• Novel acquisition strategies for exploring chemical space and optimizing photophysical properties.

Main Results:

• The framework demonstrates superior prediction of critical energy levels (T1/S1) compared to conventional methods.
• Effectively prioritizes synthetically feasible photosensitizer candidates.
• Generates a chemically diverse dataset with quantum chemical accuracy at reduced cost.

Conclusions:

• The developed framework accelerates the discovery of optoelectronic materials for solar energy applications.
• Open-sourcing the dataset and tools establishes a platform for data-driven materials discovery.
• Provides a scalable solution for designing next-generation photovoltaic materials.