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Prediction of Intramolecular Reorganization Energy Using Machine Learning

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dc.contributor.author Atahan Evrenk, Sule
dc.contributor.author Atalay, Fatma Betül
dc.date.accessioned 2019-12-30T07:28:21Z
dc.date.available 2019-12-30T07:28:21Z
dc.date.issued 2019
dc.identifier.citation Atahan-Evrenk, S., & Atalay, F. B. (2019). Prediction of Intramolecular Reorganization Energy Using Machine Learning. The Journal of Physical Chemistry A en_US
dc.identifier.issn 1089-5639
dc.identifier.other 9
dc.identifier.uri https://pubs.acs.org/doi/10.1021/acs.jpca.9b02733
dc.identifier.uri http://hdl.handle.net/20.500.11851/3260
dc.description.abstract Facile charge transport is desired for many applications of organic semiconductors (OSCs). To take advantage of high-throughput screening methodologies for the discovery of novel OSCs, parameters relevant to charge transport are of high interest. The intramolecular reorganization energy (RE) is one of the important charge transport parameters suitable for molecular-level screening. Because the calculation of the RE with quantum-chemical methods is expensive for large-scale screening, we investigated the possibility of predicting the RE from the molecular structure by means of machine learning methods. We combinatorially generated a molecular library of 5631 molecules with extended conjugated backbones using benzene, thiophene, furan, pyrrole, pyridine, pyridazine, and cyclopentadiene as building blocks and obtained the target electronic data at the B3LYP level of theory with the 6-31G* basis set. We compared ridge, kernel ridge, and deep neural net (DNN) regression models based on graph- and geometry-based descriptors. We found that DNNs outperform the other methods and can predict the RE with a coefficient of determination of 0.92 and root-mean-square error of similar to 12 meV. This study shows that the REs of organic semiconductor molecules can be predicted from the molecular structures with high accuracy. en_US
dc.description.sponsorship We thank Secil Usta and Simla B. Harma for help with DNN scripting and Isiksu Eksioglu for useful discussions regarding the use of the Keras python deep learning library. S.A.-E. acknowledges financial support from The Scientific and Technological Research Council of Turkey (Ardeb 3001 Programme, Grant 216Z096), software support from Chem Axon Ltd., and support from NVIDLA Corporation through the donation of the Titan Xp GPU used for this research.
dc.language.iso eng en_US
dc.publisher AMER CHEMICAL SOC en_US
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Molecular structure en_US
dc.subject Molecular modeling en_US
dc.subject Mathematical methods en_US
dc.title Prediction of Intramolecular Reorganization Energy Using Machine Learning en_US
dc.type article en_US
dc.relation.journal JOURNAL OF PHYSICAL CHEMISTRY A en_US
dc.contributor.department TOBB ETÜ, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü tr_TR
dc.contributor.department TOBB ETU, Faculty of Medicine, Department of Basic Medical Sciences en_US
dc.identifier.volume 123
dc.identifier.issue 36
dc.identifier.startpage 7855
dc.identifier.endpage 7863
dc.contributor.orcid https://orcid.org/0000-0002-4905-3491
dc.identifier.wos WOS:000486361100021
dc.contributor.tobbetuauthor Atahan Evrenk, Şule
dc.contributor.tobbetuauthor Atalay, Fatma Betül
dc.identifier.doi 10.1021/acs.jpca.9b02733
dc.contributor.wosresearcherID D-4736-2012
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı tr_TR


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