Neural network modelling on temperature coefficient of surface tension and its usage in melting point prediction of nanosized metal particles |
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| Affiliation: | 1. Siberian State Industrial University, Kirov str. 42, 654007 Novokuznetsk, Russia;2. National University of Science and Technology “MISIS”, Leninskiy av. 4, 119049 Moscow, Russia;3. Institute of Strength Physics and Materials Science SB RAS, Akademicheskiy str. 2/4, 634021 Tomsk, Russia;1. National Research Council, CNR IENI, Corso Promessi Sposi 29, 23900 Lecco, Italy;2. Politecnico di Milano, Department of Mechanical Engineering, Via La Masa 1, 20156 Milano, Italy;1. Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan;2. Department of Materials Processing, Graduate School of Engineering, Tohoku University, 6-6-02 Aramaki-Aza-Aoba, Aoba-ku, Sendai 980-8579, Japan;3. Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 6-3 Aramaki-Aza-Aoba, Aoba-ku, Sendai 980-8578, Japan |
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| Abstract: | Temperature coefficient of surface tension is a very important parameter to calculate phase diagrams of nanoparticle metal systems. In this paper, neural network calculation was for the first time used to evaluate the temperature coefficient. It shows that the constructed neural network can predict the temperature coefficient values for 37 metals, with the deviation from the averaged experimental measurements smaller than 25%. Furthermore, the neural network predictions were compared with the calculated values by using an empirical equation and it shows a better performance. |
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| Keywords: | Temperature coefficient Surface tension Neural network Liquid metal |
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