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Şule Atahan-Evrenk Assistant Professor Faculty of Medicine TOBB University of Economics and Technology

News

• June 2019: Our paper Prediction of Intramolecular Reorganization Energy Using Machine Learning is on the cover of the Journal of Physical Chemistry A.
  

  

About me

Research Interests

Teaching

Currently teaching at TOBB ETU:

• TIP 101 Introductory Chemistry
• TIP 103 Introductory Biophysics
• TIP 201 Biophysics
• TIP 202 Biophysics

Previously taught as a teaching assistant:

• Science of the Physical Universe: Invisible Worlds (Harvard U.)
• Invisible Worlds: Micro- and Nanothings. Science, Technology and Public Policy (Harvard U.)
• General Chemistry (U. of Maryland, CP)

Selected Publications (Google Scholar)

• Prediction of Intramolecular Reorganization Energy Using Machine Learning
  Sule Atahan-Evrenk, F. Betül Atalay
  The Journal of Physical Chemistry A (2019), 123 (36), p. 7855-7863
  [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 ∼12 meV. This study shows that the REs of organic semiconductor molecules can be predicted from the molecular structures with high accuracy.
• Coherent Dynamics of Mixed Frenkel and Charge-Transfer Excitons in Dinaphtho[2,3-b:2′3′-f]thieno[3,2-b]-thiophene Thin Films: The Importance of Hole Delocalization
  Takatoshi Fujita, Sule Atahan-Evrenk, Nicolas P. D. Sawaya, and Alan Aspuru-Guzik
  The Journal of Physical Chemistry Letters, Volume 7 (2016) p.1374
  [abstract]
  Charge-transfer states in organic semiconductors play crucial roles in processes such as singlet fission and exciton dissociation at donor/acceptor interfaces. Recently, a time-resolved spectroscopy study of dinaphtho[2,3-b:2′3′-f]thieno[3,2-b]-thiophene (DNTT) thin films provided evidence for the formation of mixed Frenkel and charge-transfer excitons after the photoexcitation. Here, we investigate optical properties and excitation dynamics of the DNTT thin films by combining ab initio calculations and a stochastic Schrödinger equation. Our theory predicts that the low-energy Frenkel exciton band consists of 8–47% CT character. The quantum dynamics simulations show coherent dynamics of Frenkel and CT states in 50 fs after the optical excitation. We demonstrate the role of charge delocalization and localization in the mixing of CT states with Frenkel excitons as well as the role of their decoherence.
• Prediction and Calculation of Crystal Structures: Methods and Applications
  Volume Editors: Şule Atahan-Evrenk, Alan Aspuru-Guzik
  Topics in Current Chemistry, Springer-Verlag Berlin Heidelberg, Volume 345 (2014)
  [Front matter (pdf)]

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