Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks

Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks

We propose a novel descriptor of materials, named ‘cation fingerprints’, based on the chemical formula or concentrations of raw materials and their respective properties. To test its performance, this method was used to predict the viscosity of glass materials using the experimental database INTERGL...

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Bibliographic Details
Main Authors: Jaekyun Hwang, Yuta Tanaka, Seiichiro Ishino, Satoshi Watanabe
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Science and Technology of Advanced Materials
Subjects:
chemical composition
machine learning
materials informatics
neural network
oxide
glass
viscosity
Online Access:http://dx.doi.org/10.1080/14686996.2020.1786856

Internet

http://dx.doi.org/10.1080/14686996.2020.1786856

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