DC Field | Value | Language |
dc.contributor.author | Khinevich, A. | - |
dc.contributor.author | Stsiapanau, A. A. | - |
dc.contributor.author | Smirnov, A. G. | - |
dc.date.accessioned | 2021-11-05T07:13:55Z | - |
dc.date.available | 2021-11-05T07:13:55Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Khinevich, 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.uri | https://libeldoc.bsuir.by/handle/123456789/45816 | - |
dc.description.abstract | There 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.iso | en | ru_RU |
dc.publisher | БГУИР | ru_RU |
dc.subject | материалы конференций | ru_RU |
dc.subject | conference proceedings | ru_RU |
dc.subject | machine learning | ru_RU |
dc.subject | semiconductor materials | ru_RU |
dc.title | Мachine learning methods for predict electrophysical properties of semiconductor materials for optoelectronic and energy storage devices | ru_RU |
dc.type | Статья | ru_RU |
Appears in Collections: | NDTCS 2021
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