Cargando…
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...
Autores principales: | Hwang, Jaekyun, Tanaka, Yuta, Ishino, Seiichiro, Watanabe, Satoshi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476533/ https://www.ncbi.nlm.nih.gov/pubmed/32939174 http://dx.doi.org/10.1080/14686996.2020.1786856 |
Ejemplares similares
-
Materials inspired by mathematics
por: Kotani, Motoko, et al.
Publicado: (2016) -
Machine learning reveals orbital interaction in materials
por: Lam Pham, Tien, et al.
Publicado: (2017) -
MDTS: automatic complex materials design using Monte Carlo tree search
por: M. Dieb, Thaer, et al.
Publicado: (2017) -
Recent progress in the conversion of biomass wastes into functional materials for value-added applications
por: Zhou, Chufan, et al.
Publicado: (2020) -
Role of supercritical carbon dioxide (scCO(2)) in fabrication of inorganic-based materials: a green and unique route
por: Liu, Hao, et al.
Publicado: (2021)