A unified active learning framework for photosensitizer design.

The design of high-performance photosensitizers for next-generation photovoltaic and clean energy applications remains a formidable challenge due to the vast chemical space, competing photophysical trade-offs, and computational limitations of traditional quantum chemistry methods. While machine learning offers potential solutions, existing approaches suffer from data scarcity and inefficient exploration of molecular configurations. This work introduces a unified active learning framework that systematically integrates semi-empirical quantum calculations with adaptive molecular screening strategies to accelerate photosensitizer discovery. Our methodology combines three principal components: (1) A hybrid quantum mechanics/machine learning pipeline generating a chemically diverse molecular dataset while maintaining quantum chemical accuracy at significantly reduced computational costs; (2) a graph neural network architecture and uncertainty quantification; (3) Novel acquisition strategies that dynamically balance broad chemical space exploration with targeted optimization of photophysical objectives. The framework demonstrates superior performance in predicting critical energy levels (