Journal
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
Volume 21, Issue 1, Pages 492-504Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/14686996.2020.1786856
Keywords
Chemical composition; machine learning; materials informatics; neural network; oxide; glass; viscosity
Categories
Funding
- Japan Science and Technology Agency
- Program for Leading Graduate Schools J01 (MERIT), Japan Society for the Promotion of Science
Ask authors/readers for more resources
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 INTERGLAD. Using artificial neural network models, we succeeded in predicting the temperature required for glass to have a specific viscosity within a root-mean-square error of 33.0 degrees C. We were also able to evaluate the effect of particular target raw materials using a model trained without including the specific target raw material. The results show that cation fingerprints with a neural network model can predict some unseen combinations of raw materials. In addition, we propose a method for estimating the prediction accuracy by calculating cosine similarity of the input features of the material which we want to predict.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available