Physica A: Statistical Mechanics and its Applications, volume 617, pages 128678
Machine learning-based prediction of elastic properties of amorphous metal alloys
Publication type: Journal Article
Publication date: 2023-05-01
Quartile SCImago
Q2
Quartile WOS
Q2
Impact factor: 3.3
ISSN: 03784371
Statistical and Nonlinear Physics
Statistics and Probability
Abstract
The Young’s modulus E is the key mechanical property that determines the resistance of solids to tension/compression. In the present work, the correlation of the quantity E with such characteristics as the total molar mass M of alloy components, the number of components n forming an alloy, the yield stress σy and the glass transition temperature Tg has been studied in detail based on a large set of empirical data for the Young’s modulus of different amorphous metal alloys. It has been established that the values of the Young’s modulus of metal alloys under normal conditions correlate with such a mechanical characteristic as the yield stress as well as with the glass transition temperature. As found, the specificity of the “chemical formula” of alloy, which is determined by molar mass M and number of components n, does not affect on elasticity of the material. The machine learning algorithm identified both the quantities M and n as insignificant factors in determining E. A simple non-linear regression model is obtained that relates the Young’s modulus with Tg and σy, and this model correctly reproduces the experimental data for metal alloys of different types. This obtained regression model generalizes the previously presented empirical relation E≃49.8σy for amorphous metal alloys.
Citations by journals
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1
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Metals
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Metals
1 publication, 33.33%
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Crystals
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Crystals
1 publication, 33.33%
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Radiation Physics and Chemistry
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Radiation Physics and Chemistry
1 publication, 33.33%
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1
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Citations by publishers
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1
2
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
2 publications, 66.67%
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Elsevier
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Elsevier
1 publication, 33.33%
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1
2
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Galimzyanov B. N. et al. Machine learning-based prediction of elastic properties of amorphous metal alloys // Physica A: Statistical Mechanics and its Applications. 2023. Vol. 617. p. 128678.
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Galimzyanov B. N., Doronina M. A., Mokshin A. V. Machine learning-based prediction of elastic properties of amorphous metal alloys // Physica A: Statistical Mechanics and its Applications. 2023. Vol. 617. p. 128678.
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TY - JOUR
DO - 10.1016/j.physa.2023.128678
UR - https://doi.org/10.1016%2Fj.physa.2023.128678
TI - Machine learning-based prediction of elastic properties of amorphous metal alloys
T2 - Physica A: Statistical Mechanics and its Applications
AU - Galimzyanov, Bulat N.
AU - Doronina, Maria A.
AU - Mokshin, Anatolii V.
PY - 2023
DA - 2023/05/01 00:00:00
PB - Elsevier
SP - 128678
VL - 617
SN - 0378-4371
ER -
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@article{2023_Galimzyanov,
author = {Bulat N. Galimzyanov and Maria A. Doronina and Anatolii V. Mokshin},
title = {Machine learning-based prediction of elastic properties of amorphous metal alloys},
journal = {Physica A: Statistical Mechanics and its Applications},
year = {2023},
volume = {617},
publisher = {Elsevier},
month = {may},
url = {https://doi.org/10.1016%2Fj.physa.2023.128678},
pages = {128678},
doi = {10.1016/j.physa.2023.128678}
}