@inproceedings{565b2874bd28466082fe7793c9348b8c,
title = "Predicting atom types of anatase tio2 nanoparticles with machine learning",
abstract = "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), Na{\"i}ve 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.",
keywords = "Machine learning, Material science, Nanoparticles, Random forest",
author = "Hasan Kurban and Mustafa Kurban and Parichit Sharma and Dalkilic, {Mehmet M.}",
note = "Publisher Copyright: {\textcopyright} 2021 Trans Tech Publications Ltd, Switzerland.; 9th International Conference on Engineering and Innovative Materials, ICEIM 2020 ; Conference date: 04-09-2020 Through 06-09-2020",
year = "2021",
doi = "10.4028/www.scientific.net/KEM.880.89",
language = "English",
isbn = "9783035738261",
series = "Key Engineering Materials",
publisher = "Trans Tech Publications Ltd",
pages = "89--94",
editor = "Muhammad Yahaya and Takahiro Ohashi",
booktitle = "Engineering and Innovative Materials IX - Selected peer-reviewed full text papers from the 9th International Conference on Engineering and Innovative Materials, ICEIM 2020",
address = "Switzerland",
}