Data-driven designing of dyes: Chemical space generation and dipole moment prediction

• Published in: Materials science & engineering. B, Solid-state materials for advanced technology Solid-state materials for advanced technology, 2025-01, Vol.311, p.117792, Article 117792
• Main Authors: Hussain Tahir, Mudassir, Kadyrov, Tagir, Mohamed Moussa, Ihab
• Format: Article
• Language: English
• Subjects: Chemical space; Dipole moment; Dyes; Machine learning; Synthetic accessibility
• ISSN: 0921-5107
• DOI: 10.1016/j.mseb.2024.117792

[Display omitted] •Machine learning models are used to predict the dipole moment.•Database of 10,000 dyes is generated.•Clustering analysis is performed to visualize the generated database.•Synthetic accessibility score is predicted. The current study presents machine learning-assisted designing of dyes for photovoltaics applications. Multiple machine learning models are trained to predict the dipole moment. Random forest model has appeared as best model with lower root mean square error value (1.01 Debye) and higher r-squared value (0.87). New dyes are designed using automatic method and their dipole moment is predicted using best machine learning model. The generated chemical space of dyes is visualized and analyzed using cluster plot, silhouette plot and t-distributed Stochastic Neighbor Embedding (t-SNE plot). 30 dyes with highest dipole moment values (6.31–7.12 Debye) are chosen. Chemical similarity analyses are performed on the selected dyes using cluster analysis and heatmap. Furthermore, an investigation into the synthetic accessibility score of the newly designed dyes is conducted. This method facilitates the swift selection of dyes for potential use in photovoltaic devices.