Ultra-fast prediction of D-π-A organic dye absorption maximum with advanced ensemble deep learning models.

The quick and precise estimation of D-π-A Organic Dye absorption maxima in different solvents is an important challenge for the efficient design of novel chemical structures that could improve the performance of dye-sensitized solar cells (DSSCs) and related technologies. Time-Dependent Density Functional Theory (TD-DFT) has often been employed for these predictions, but it has limitations, including high computing costs and functional dependence, particularly for solvent interactions. In this study, we introduce a high-accuracy and rapid deep-learning ensemble method using daylight fingerprints as chemical descriptors to predict the absorption maxima (λ_max) of D-π-A organic dyes in 18 different solvent environments. This study introduces a novel approach leveraging advanced ensemble deep learning of 10 models of multiple neural architectures including convolutional networks to demonstrate exceptional predictive power in capturing complex relationships between molecular structures with solvent interaction and absorption maximum. Leveraging a comprehensive range of molecular descriptors from organic dye fingerprints, we developed a highly accurate ensemble model with an R² of 0.94 and a mean absolute error (MAE) of 8.6 nm, which enhances predictive accuracy and significantly reduces computational time. Additionally, we developed a user-friendly web-based platform that allows for quick prediction of absorption maxima including solvent effect. This tool, which directly uses SMILES representations and advanced deep learning techniques, offers significant potential for accelerating the discovery of efficient dye candidates for various applications, including solar energy, environmental solutions, and medical research. This research opens the door to more effective next-generation dye design, which will facilitate rapid testing in a variety of fields and design an efficient new material.