Predicting atom types of anatase tio2 nanoparticles with machine learning

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dc.contributor.authorKurban, Hasan
dc.contributor.authorKurban, Mustafa
dc.contributor.authorSharma, Parichit
dc.contributor.authorDalkilic, Mehmet M.
dc.date.accessioned2024-12-24T19:09:45Z
dc.date.available2024-12-24T19:09:45Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
dc.description9th International Conference on Engineering and Innovative Materials, ICEIM 2020 -- 4 September 2020 through 6 September 2020 -- Singapore -- 257789
dc.description.abstractMachine 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.
dc.identifier.doi10.4028/www.scientific.net/KEM.880.89
dc.identifier.endpage94
dc.identifier.isbn978-303573826-1
dc.identifier.issn1013-9826
dc.identifier.scopus2-s2.0-85105927331
dc.identifier.scopusqualityQ4
dc.identifier.startpage89
dc.identifier.urihttps://doi.org10.4028/www.scientific.net/KEM.880.89
dc.identifier.urihttps://hdl.handle.net/20.500.12604/3749
dc.identifier.volume880 KEM
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTrans Tech Publications Ltd
dc.relation.ispartofKey Engineering Materials
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectMachine learning
dc.subjectMaterial science
dc.subjectNanoparticles
dc.subjectRandom forest
dc.titlePredicting atom types of anatase tio2 nanoparticles with machine learning
dc.typeConference Object

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