КФУ. Карточка публикации. Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network

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ARRHENIUS CROSSOVER TEMPERATURE OF GLASS-FORMING LIQUIDS PREDICTED BY AN ARTIFICIAL NEURAL NETWORK

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2023

Язык

английский

Авторы, сотрудники КФУ

Галимзянов Булат Наилевич, автор

Доронина Мария Алексеевна, автор

Мокшин Анатолий Васильевич, автор

Библиографическое описание на языке оригинала

Galimzyanov B.N., Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network / B.N. Galimzyanov,M.A. Doronina, A.V. Mokshin // Materials. - 2023. - V. 16. № 3. - P. 1127.

Аннотация

The Arrhenius crossover temperature, TA, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below TA. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature T is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature T. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature TA and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature TA was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature Tg, the melting temperature Tm, the ratio of these temperatures Tg/Tm and the fragility index m were applied as input parameters. It has been established that the temperatures Tg and Tm are significant parameters, whereas their ratio Tg/Tm and the fragility index m do not correlate much with the temperature TA. An important result of the present work is the analytical equation relating the temperatures Tg, Tm and TA, and that, from the algebraic point of view, is the equation for a second-order curved surface. It was shown that this equation allows one to correctly estimate the temperature TA for a large class of materials, regardless of their compositions and glass-forming abilities.

Ключевые слова

machine learning; physical properties; organic compounds;metallic alloys; silicates; borates

Название журнала

Materials

URL

https://www.mdpi.com/1996-1944/16/3/1127

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https://repository.kpfu.ru/?p_id=277795

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Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Галимзянов Булат Наилевич	ru_RU
dc.contributor.author	Доронина Мария Алексеевна	ru_RU
dc.contributor.author	Мокшин Анатолий Васильевич	ru_RU
dc.date.accessioned	2023-01-01T00:00:00Z	ru_RU
dc.date.available	2023-01-01T00:00:00Z	ru_RU
dc.date.issued	2023	ru_RU
dc.identifier.citation	Galimzyanov B.N., Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network / B.N. Galimzyanov,M.A. Doronina, A.V. Mokshin // Materials. - 2023. - V. 16. № 3. - P. 1127.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=277795	ru_RU
dc.description.abstract	Materials	ru_RU
dc.description.abstract	The Arrhenius crossover temperature, TA, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below TA. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature T is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature T. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature TA and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature TA was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature Tg, the melting temperature Tm, the ratio of these temperatures Tg/Tm and the fragility index m were applied as input parameters. It has been established that the temperatures Tg and Tm are significant parameters, whereas their ratio Tg/Tm and the fragility index m do not correlate much with the temperature TA. An important result of the present work is the analytical equation relating the temperatures Tg, Tm and TA, and that, from the algebraic point of view, is the equation for a second-order curved surface. It was shown that this equation allows one to correctly estimate the temperature TA for a large class of materials, regardless of their compositions and glass-forming abilities.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU