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Materials, volume 16, issue 3, pages 1127

Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network

Galimzyanov Bulat N. ^{1, 2}

Doronina Maria A. ¹

Mokshin Anatolii V. ^{1, 2}

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Institute of Physics, Kazan Federal University, 420008 Kazan, Russia |

Udmurt Federal Research Center of the Ural Branch of RAS, 426067 Izhevsk, Russia |

Publication type: Journal Article

Publication date: 2023-01-28

Multidisciplinary Digital Publishing Institute (MDPI)

Journal: Materials

Quartile SCImago

Quartile WOS

Impact factor: 3.4

ISSN: 19961944

DOI: 10.3390/ma16031127

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PubMed ID: 36770134

General Materials Science

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.

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	1 2
Materials	Materials, 2, 33.33% Materials 2 publications, 33.33%
Metals	Metals, 1, 16.67% Metals 1 publication, 16.67%
Applied Sciences (Switzerland)	Applied Sciences (Switzerland), 1, 16.67% Applied Sciences (Switzerland) 1 publication, 16.67%
Polymers	Polymers, 1, 16.67% Polymers 1 publication, 16.67%
Crystals	Crystals, 1, 16.67% Crystals 1 publication, 16.67%
	1 2

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Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 6, 100% Multidisciplinary Digital Publishing Institute (MDPI) 6 publications, 100%
	1 2 3 4 5 6

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Galimzyanov B. N. et al. Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network // Materials. 2023. Vol. 16. No. 3. p. 1127.

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

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TY - JOUR

DO - 10.3390/ma16031127

UR - https://doi.org/10.3390%2Fma16031127

TI - Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network

T2 - Materials

AU - Galimzyanov, Bulat N.

AU - Doronina, Maria A.

AU - Mokshin, Anatolii V.

PY - 2023

DA - 2023/01/28 00:00:00

PB - Multidisciplinary Digital Publishing Institute (MDPI)

SP - 1127

IS - 3

VL - 16

PMID - 36770134

SN - 1996-1944

ER -

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@article{2023_Galimzyanov,

author = {Bulat N. Galimzyanov and Maria A. Doronina and Anatolii V. Mokshin},

title = {Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network},

journal = {Materials},

year = {2023},

volume = {16},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

month = {jan},

url = {https://doi.org/10.3390%2Fma16031127},

number = {3},

pages = {1127},

doi = {10.3390/ma16031127}

}

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Galimzyanov, Bulat N., et al. “Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network.” Materials, vol. 16, no. 3, Jan. 2023, p. 1127. https://doi.org/10.3390%2Fma16031127.

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