Exploring Machine Learning Models for Predicting Efficiency in Carbazole-Based DSSC Sensitizers

This study explores the application of machine learning models in predicting the efficiency of carbazole-based dye-sensitized solar cell (DSSC) sensitizers. The research evaluates multiple machine learning algorithms to model structure–property relationships and optimize photovoltaic performance parameters. The results demonstrate the potential of data-driven approaches in accelerating material discovery and improving predictive accuracy in DSSC efficiency modeling. This work contributes to the growing integration of artificial intelligence in materials science and renewable energy research.