Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning

Woon, Kai Lin and Chong, Zhao Xian and Ariffin, Azhar and Chan, Chee Seng (2021) Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning. Journal of Molecular Graphics and Modelling, 105. p. 107891. ISSN 1093-3263

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Official URL: https://doi.org/10.1016/j.jmgm.2021.107891

Abstract

Fused tricyclic organic compounds are an important class of organic electronic materials. In designing molecules for organic electronics, knowing what chemical structure that be used to tune the molecular property is one of the keys that can help to improve the material performance. In this research, we applied machine learning and data analytic approaches in addressing this problem. The energy states (Lowest Unoccupied Molecular Orbital (HOMO), Highest Occupied Molecular Orbitals (LUMO), singlet (Es) and triplet (ET) energy) of more than 10 thousand fused tricyclics are calculated. Corresponding descriptors are also generated. We find that the Coulomb matrix is a poorer descriptor than high-level descriptors in a multilayer perceptron neural network. Correlations as high as 0.95 is obtained using a multilayer perceptron neural network with Mean Absolute Error as low as 0.08 eV. The descriptors that are important in tuning the energy levels are revealed using the Random Forest algorithm. Correlations of such descriptors are also plotted. We found that the higher the number of tertiary amines, the deeper are the HOMO and LUMO levels. The presence of N[dbnd]N in the aromatic rings can be used to tune the ES. However, there is no single dominant descriptor that can be correlated with the ET. A collection of descriptors is found to give a far better correlation with ET. This research demonstrated that machine learning and data analytics in guiding how certain chemical substructures correlate with the molecule energy states. © 2021 Elsevier Inc.

Item Type: Article
Uncontrolled Keywords: Machine learning; Data analytics; Tricyclics; Triplet
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QC Physics
Q Science > QD Chemistry
Divisions: Faculty of Computer Science & Information Technology
Faculty of Science > Dept of Chemistry
Faculty of Science > Dept of Physics
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 03 May 2021 05:37
Last Modified: 03 May 2021 05:37
URI: http://eprints.um.edu.my/id/eprint/25921

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