Anjana K.S.,
Rencemon T. Thoppil,
Sneha Anna Sunny,
Renjith Thomas*,
Anila Skariah,
- Student, Department of Chemistry, St Berchmans College (Autonomous), Changanassery, Kerela, India
- Student, Department of Chemistry, St Berchmans College (Autonomous), Changanassery, Kerela, India
- Student, Department of Chemistry, St Berchmans College (Autonomous), Changanassery, Kerela, India
- Professor, Department of Chemistry, St Berchmans College (Autonomous), Changanassery, Kerela, India
- Faculty, Department of Economics, St Berchmans College (Autonomous), Changanassery, Kerela, India
Abstract
This study investigates the optical properties of rubrene derivatives to develop an accurate predictive model for absorption maxima using computational chemistry and chemoinformatic techniques. We benchmarked various quantum chemical methods, identifying that the M06-2X/aug-cc-pVDZ method in dichloromethane (DCM) provided the strongest correlation with experimental data. Key molecular descriptors such as band gap, ionization potential, and electrophilicity index were calculated and analyzed using principal component analysis (PCA) to identify significant factors influencing absorption maxima. A multiple linear regression model was then developed and validated using test molecules, achieving an R² value of 0.7512. The predictive model demonstrated average accuracy in forecasting absorption maxima, aligning well with experimentally observed values. This study offers some insights into the structure–property relationships in rubrene derivatives and provides a reliable computational approach for guiding the design of new OLED materials.
Furthermore, the influence of substituent effects on the electronic distribution within the rubrene framework was systematically examined, revealing that electron-donating groups tend to induce bathochromic shifts, while electron-withdrawing groups lead to hypsochromic behavior. Solvent effects were also evaluated using implicit solvation models, confirming the critical role of polarity in modulating excited-state properties. The robustness of the developed model was assessed through cross-validation and external validation datasets, ensuring its generalizability across structurally diverse derivatives. In addition, correlation analysis highlighted the combined contribution of frontier molecular orbital energies and global reactivity descriptors in determining optical responses. These findings not only enhance the understanding of photophysical behavior in rubrene-based systems but also establish a cost-effective screening strategy for the rational design and optimization of high-performance organic optoelectronic materials.
Keywords: Rubrene, DFT, machine learning, absorption maxima, predictive modelling
[This article belongs to International Journal of Cheminformatics ]
Anjana K.S., Rencemon T. Thoppil, Sneha Anna Sunny, Renjith Thomas*, Anila Skariah. DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives. International Journal of Cheminformatics. 2026; 04(01):41-56.
Anjana K.S., Rencemon T. Thoppil, Sneha Anna Sunny, Renjith Thomas*, Anila Skariah. DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives. International Journal of Cheminformatics. 2026; 04(01):41-56. Available from: https://journals.stmjournals.com/ijci/article=2026/view=242845
References
| Volume | 04 |
| Issue | 01 |
| Received | 31/03/2026 |
| Accepted | 22/04/2026 |
| Published | 30/04/2026 |
| Publication Time | 30 Days |
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