DFT and machine learning integration to predict efficiency of modified metal-free dyes in DSSCs

Abstract

Power conversion efficiency (PCE) prediction in dye-sensitized solar cells (DSSCs) increasingly relies on computation and machine learning, lowering experimental demands and accelerating materials discovery. In this work we incorporated quantum-chemical descriptors, computed via density-functional theory (DFT), with cheminformatic descriptors generated using the Mordred library to train two machine learning models. The Random Forest and XGBoost models were trained on a dataset of 40 dyes, together with their literature experimental PCEs. The model stabilities were investigated using multiple random state configurations (30, 38, 42 and 50). The trained models were used to evaluate newly engineered dyes, and then validated through electronic structure analysis. The novel dyes are derivatives of: (E)-10-methyl-9-(3-(10-methylacridin-9(10H)-ylidene)prop-1-en-1-yl)acridin-10-ium (C-PE3), 10-methyl-9-((1E,3E)-5-(10-methylacridin-9(10H)-ylidene)penta-1,3-dien-1-yl)acridin-10-ium (C-PE5) and 10-methyl-9-((1E,3E,5E)-7-(10-methylacridin-9(10H)-ylidene)hepta-1,3,5-trien-1-yl)acridin-10-ium (C-PE7). A R² = 0.8904 and RMSE = 0.0038 for XGBoost as performer under the random state of 38 were achieved. Both models, XGBoost and RF identified C3-PE5 and C3-PE7 as top promising candidates, with predicted PCEs of 5.49 % and 5.43 %, respectively. By integrating DFT/cheminformatics and machine learning techniques, this study enabled PCE prediction with no need for experimental input.