A DFT and Molecular Correlational Analysis on Newly Designed Silicon-Carbide Quantum Dots with Extended Acceptors for their Photovoltaic Performance

For their improved photovoltaic (PV) performance, this study addresses the electronic and structural basis of silicon carbide quantum dots (SQs) structures. For this, we employ Density Functional Theory (DFT) to develop and optimize donor-π-acceptor based Si₈C₈H₈ derived SQs. Using RDKit of Python programming language, their molecular descriptors are calculated. Their Topological Polar Surface Area (TPSA) emerges to be the most influential parameter by their correlational analysis, having a substantial correlation with their maximum absorption (λ_max). The Random Forest Regression model predicts their open circuit voltage (V_oc) with its R-Squared (R²) of 0.82. Additionally, their λ_max as descriptor (Max_Abs) displays its strong correlations with both Short-Circuit Current Density (J_sc, R = 0.71) and Light Harvesting Efficiency (LHE, R = 0.83) which suggests that a longer λ_max can promote higher PV efficiency. There are also moderate correlations which existed for open circuit voltage (V_oc, R = 0.56) and Max_Abs with its LogP and V_oc (R = 0.63). These results guide the design of high-performance SQ-based PV materials by offering insightful information on structure–property relationships.