Yazar "Dalkilic, Mehmet M." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    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.
  • [ X ]
    Öğe
    Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data
    (Elsevier, 2022) Temiz, Selcuk; Kurban, Hasan; Erol, Salim; Dalkilic, Mehmet M.
    The use of Electrochemical Impedance Spectroscopy on rechargeable Lithium-ion battery characterization is an extensively recognized non-destructive procedure for both in-situ and ex-situ analyses. In an impedance measurement for a rechargeable battery, the oscillating current with an accompanying phase angle is the response for a potential perturbation. The proper evaluation of phase angle as a crucial impedance parameter, provides critical understanding of the status of the battery. Although fast and simple, impedance data is difficult to interpret. Using a novel data-centric Machine Learning framework (co-modeling) we demonstrate how to impute experimental data quickly, precisely, and inexpensively that agrees with wholly experimentally generated data. In particular, we predict the phase angle with 99.9% accuracy by training the minimal empirical impedance data. This approach demonstrates a potentially burgeoning field of Machine Learning experimental data imputation and the consequence of faster diagnostic and study of batteries.