Yazar "Kurban, Mustafa" seçeneğine göre listele

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    A DFT study on stability and electronic structure of AlN nanotubes
    (Elsevier, 2021) Muz, Iskender; Kurban, Hasan; Kurban, Mustafa
    Structural, energetic, electronic, reactivity and stability properties of armchair (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9) and (10,10) aluminum nitride nanotubes (AlNNTs) with different diameter have been probed using density functional theory (DFT) in terms of Moreover, the chemical reactivity characteristics of AlNNTs have performed via some of the quantum molecular descriptors. Our results also indicate that the increasing diameter of AlNNTs gives rise to notable changes in the electronic structure of the AlNNTs. Moreover, results for UV/vis spectra of AlNNTs indicate that the maximum wavelength absorption lie in the range 188-194 nm. The number Al-N bonds and segregation phenomena of Al and N atoms in the AlNNTs have been investigated to better understand the stability of AlNNTs. Besides, the energy gap and chemical hardness enhance with increase diameter of AlNNTs, thus resulting in a rise in the stability, while the AlNNTs with smaller can be considered as a candidate for the adsorption of gas molecules and drugs for nano-electronic applications.
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    Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles
    (Elsevier, 2021) Kurban, Hasan; Kurban, Mustafa
    In this study, we built a variety of Machine Learning (ML) systems over 23 different sizes of CH3NH3PbI3 perovskite nanoparticles (NPs) to predict the atoms in the NPs from their geometric locations. Our findings show that a specific type of ML algorithms, tree-based models which are Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), can perfectly learn CH3NH3PbI3 perovskite NPs. Surprisingly, some popular ML algorithms such as Naive Bayes (NB), Support Vector Machines (SVM), Partial Least Squares (PLS), Regularized Logistic Regression (LR), Neural Networks (NN), Stacked Auto-Encoder Deep Neural Network (DNN), K-Nearest Neighbor (KNN) fail to learn CH3NH3PbI3 perovskite NPs.
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    Density-functional tight-binding approach for the structural analysis and electronic structure of copper hydride metallic nanoparticles
    (Elsevier, 2019) Kurban, Hasan; Kurban, Mustafa; Dalkilic, Mehmet
    We perform a theoretical investigation using the density functional tight-binding (DFTB) approach for the structural analysis and electronic structure of copper hydride (CuH) metallic nanoparticles (NPs) of different size (from 0.7 to 1.6 nm). By increasing the size of CuH NPs, the number of bonds, segregation phenomena and radial distribution function (RDF) of binary Cu-Cu, Cu-H and H-H interactions are analyzed using new implementations in R code. The results reveal that the number of Cu-Cu bonds is more than that of Cu-H while the number of H-H bonds are the less. Thus, a large amount of H atoms prefers to connect to Cu atoms. The increase in the size of the NPs contributes to their stabilization because of the increase in the interaction of H-H bonding. The segregation of Cu and H atoms shows that Cu atoms tend to co-locate at the center, while H atoms tend to reside on the surface. From the density of state (DOS) analysis, CuH NPs shows a metallic character which is compatible with experimental data. HOMO and Fermi levels decrease from -3.555 to -3.443 eV and from -3.510 to -3.441 eV. Herein, an increase in the size contributes to the stabilization of CuH NP due to decrease in the HOMO energies.
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    Effect of Mg content on electronic structure, optical and structural properties of amorphous ZnO nanoparticles: A DFTB study
    (Elsevier, 2021) Kurban, Hasan; Alaei, Sholeh; Kurban, Mustafa
    In this work, we perform a theoretical analysis of structural, electronic, and optical properties of pure and Mg-doped amorphous ZnO nanoparticles (a-ZnO NPs) using DFTB method. Our results show that Zn atoms are more preferential for Mg atoms than for O atoms because the number of Mg-Zn bonds is greater than that of Mg-O. The rise in the content of Mg in a-ZnO NPs leads to an increase of Mg-Zn and Mg-O interactions. Mg atoms prefer to locate near the center of a-ZnO NP, but Zn and O atoms nearly preserve their positions which is compatible with radial distribution function peaks. The orbital energies display a decrease in the energy gap from 3.592 to 3.546 eV while increasing Mg content. The LUMO level is also significantly shifted to higher energies. The results also reveal that the performance of pure a-ZnO NP can be enhanced with a subsequent increase in Mg content.
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    Predicting atom types of anatase tio2 nanoparticles with machine learning
    (Trans Tech Publications Ltd, 2021) Kurban, Hasan; Kurban, Mustafa; Sharma, Parichit; Dalkilic, Mehmet M.
    Machine learning (ML) has recently made a major contribution to the fields of Material Science (MS). In this study, ML algorithms are used to learn atoms types over structural geometrical data of anatase TiO2 nanoparticles produced at different temperature levels with the densityfunctional tight-binding method (DFTB). Especially for this work, Random Forest (RF), Decision Trees (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NB), which are among the most popular ML algorithms, were run to learn titanium (Ti) and oxygen (O) atoms. RF outperforms other algorithms, almost succeeding in learning this skewed data set close to perfect. The use of ML algorithms with datasets compatible with its mathematical design increases their learning performance. Therefore, we find it remarkable that a certain type of ML algorithm performs almost perfectly. Because it can help material scientists predict the behavior and structural and electronic properties of atoms at different temperatures. © 2021 Trans Tech Publications Ltd, Switzerland.
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    Rare-class learning over Mg-doped ZnO nanoparticles
    (Elsevier, 2021) Kurban, Hasan; Kurban, Mustafa
    This interdisciplinary study is conducted to find answers to two important questions which researchers often face in Machine Learning (ML) and Material Science (MS) fields. In this work, we measure the performance of the most popular ML algorithms (more than a dozen) on rare-class learning problem and determine the best learning algorithm for atom type prediction over the Mg-doped ZnO nanoparticles data obtained from the densityfunctional tight-binding method. As a result, we observe that tree-based ML algorithms such as Extreme Gradient Boosting (XGB), Decision Trees (DT), Random Forest (RF), outperform other types of ML algorithms, e. g., cost-sensitive learning, prototype models, support vector machines, kernel methods, on both rare-class learning and atom type prediction.
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    Tailoring the structural properties and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles: DFTB calculations
    (Elsevier, 2020) Kurban, Hasan; Dalkilic, Mehmet; Temiz, Selcuk; Kurban, Mustafa
    In this study, we perform a theoretical investigation using the density functional tight-binding (DFTB) approach for the structural analysis and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles (NPs). Our results show that the number of Ti-O bonds is greater than that of O-O, while the number of Ti-Ti bonds is fewer. Thus, large amounts of O atoms prefer to connect to Ti atoms. The increase in the temperature of the NPs contributes to an increase in the interaction of Ti-O bonding, but a decrease in the O-O bonding. The segregation of Ti and O atoms shows that Ti atoms tend to co-locate at the center, while O atoms tend to reside on the surface. Increasing temperature causes a decrease of the bandgap from 3.59 to 2.62 eV for the brookite phase, which is much more energetically favorable compared to the bulk, while it could increase the bandgap from 3.15 to 3.61 eV for anatase phase. For three-phase TiO2 NPs, LUMO and Fermi levels decrease. The HOMO level of anatase phase NP decreases, but it increases for brookite and rutile phase TiO2 nanoparticles. An increase in the temperature contributes to the stabilization of anatase phase TiO2 NP due to a decrease in the HOMO energies.