Advanced High-Throughput Rational Design of Porphyrin-Sensitized Solar Cells Using Interpretable Machine Learning.

Accurately predicting the power conversion efficiency (PCE) in dye-sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This study presents precise, predictive, and interpretable machine learning (ML) models specifically designed for Zn-porphyrin-sensitized solar cells. The model leverages theoretically computable, effective, and reusable molecular descriptors (MDs) to address this challenge.