Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles

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dc.authoridKurban, Mustafa/0000-0002-7263-0234
dc.authoridKURBAN, HASAN/0000-0003-3142-2866
dc.contributor.authorKurban, Hasan
dc.contributor.authorKurban, Mustafa
dc.date.accessioned2024-12-24T19:26:59Z
dc.date.available2024-12-24T19:26:59Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
dc.description.abstractIn 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.
dc.identifier.doi10.1016/j.commatsci.2021.110490
dc.identifier.issn0927-0256
dc.identifier.issn1879-0801
dc.identifier.scopus2-s2.0-85104594606
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.commatsci.2021.110490
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6442
dc.identifier.volume195
dc.identifier.wosWOS:000663149100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofComputational Materials Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectMaterial science
dc.subjectCH3NH3PbI3
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectXGBoost
dc.subjectExtreme Gradient Boosting
dc.titleBuilding Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles
dc.typeArticle

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