Master/diploma student
| Phone: | +49 351 46331461 |
| Room: | HAL 112 |
| Group: | computational materials science and theoretical nanophysics |
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Download contact:
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Zekiye Erarslan earned her B.Sc. in Bioengineering from Adana Science and Technology University in 2021. Her bachelor's thesis, supervised by Assoc. Prof. Yeliz Gürdal Durğun, focused on "Anesthetic Xe Recovery from exhaled gas mixtures using Bio-MOFs (Biological Metal Organic Frameworks)." She employed Grand Canonical Monte Carlo and Molecular Dynamic methods for Xe uptakes and permeabilities, and DFT simulations.
Currently pursuing an M.Sc. in Computational Modeling and Simulation at TU Dresden, she will be conducting her thesis research on improving DFTB (Density Functional Tight Binding) calculations using machine learning algorithms. Her work will be supervised by Dr. Leonardo Medrano Sandonas at the Chair of Materials Science and Nanotechnology, focusing on the development and integration of ML techniques to enhance the accuracy and efficiency of semi-empirical quantum mechanical methods.
Master/diploma student
| Phone: | +49 351 46331461 |
| Room: | HAL 112 |
| Group: | computational materials science and theoretical nanophysics |
|
Download contact:
|
Zekiye Erarslan earned her B.Sc. in Bioengineering from Adana Science and Technology University in 2021. Her bachelor's thesis, supervised by Assoc. Prof. Yeliz Gürdal Durğun, focused on "Anesthetic Xe Recovery from exhaled gas mixtures using Bio-MOFs (Biological Metal Organic Frameworks)." She employed Grand Canonical Monte Carlo and Molecular Dynamic methods for Xe uptakes and permeabilities, and DFT simulations.
Currently pursuing an M.Sc. in Computational Modeling and Simulation at TU Dresden, she will be conducting her thesis research on improving DFTB (Density Functional Tight Binding) calculations using machine learning algorithms. Her work will be supervised by Dr. Leonardo Medrano Sandonas at the Chair of Materials Science and Nanotechnology, focusing on the development and integration of ML techniques to enhance the accuracy and efficiency of semi-empirical quantum mechanical methods.
