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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/45816
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dc.contributor.authorKhinevich, A.-
dc.contributor.authorStsiapanau, A. A.-
dc.contributor.authorSmirnov, A. G.-
dc.date.accessioned2021-11-05T07:13:55Z-
dc.date.available2021-11-05T07:13:55Z-
dc.date.issued2021-
dc.identifier.citationKhinevich, A. Мachine learning methods for predict electrophysical properties of semiconductor materials for optoelectronic and energy storage devices / A. Khinevich, A. Stsiapanau, A. Smirnov // Nano-Desing, Tehnology, Computer Simulations=Нанопроектирование, технология, компьютерное моделирование (NDTCS-2021) : тезисы докладов XIX Международного симпозиума, Минск, 28-29 октября 2021 года / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. А. Богуш [и др.]. – Минск, 2021. – P. 65–66.ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/45816-
dc.description.abstractThere were several notable attempts at utilizing Machine Learning to predict physical properties of various materials. Huang et al. reported prediction of band gap properties for ternary metal nitride compounds using ML approach based on the calculated data using Heyd–Scuseria–Ernzerhof (HSE) hybrid functionals and Perdew–Burke-Ernzerhof (PBE) DFT methods. In that study electronegativity, valence and covalent radius were used as feature for the training of the ML algorithm and prediction. In another study, high accuracy of the prediction was achieved for the ML algorithm trained on the dataset with 3 only features such as ionic radius, electronegativity and number of row associated with position of specific element in the periodic table.ru_RU
dc.language.isoenru_RU
dc.publisherБГУИРru_RU
dc.subjectматериалы конференцийru_RU
dc.subjectconference proceedingsru_RU
dc.subjectmachine learningru_RU
dc.subjectsemiconductor materialsru_RU
dc.titleМachine learning methods for predict electrophysical properties of semiconductor materials for optoelectronic and energy storage devicesru_RU
dc.typeСтатьяru_RU
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