Predicting atom types of anatase tio2 nanoparticles with machine learning
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Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Trans Tech Publications Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
9th International Conference on Engineering and Innovative Materials, ICEIM 2020 -- 4 September 2020 through 6 September 2020 -- Singapore -- 257789
Anahtar Kelimeler
Machine learning, Material science, Nanoparticles, Random forest
Kaynak
Key Engineering Materials
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
880 KEM
