Neural network modelling on temperature coefficient of surface tension and its usage in melting point prediction of nanosized metal particles. (March 2019)

• Record Type: Journal Article
• Authors: Yeon, Jaebong; Ni, Peiyuan; Nakamoto, Masashi; Tanaka, Toshihiro
• Is Part Of: Calphad. Volume 64(2019)
• Page Start: 267
• Page End: 271
• Publication Date: 2019-03
• Subjects: Temperature coefficient -- Surface tension -- Neural network -- Liquid metal; Phase diagrams -- Data processing -- Periodicals; Thermochemistry -- Data processing -- Periodicals; Diagrammes de phases -- Informatique -- Périodiques; Thermochimie -- Informatique -- Périodiques; Thermodynamica; Electronic journals; 541.363
• DOI: 10.1016/j.calphad.2018.12.008
• Languages: English
• ISSNs: 0364-5916

Abstract

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.