Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning-Reference-Cited by-同舟云学术

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Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

Published:2019-08-29 Issue:1 Volume:3 Page:
ISSN:2397-2106
Container-title:npj Materials Degradation
language:en
Short-container-title:npj Mater Degrad

Author:

Liu Han,Zhang Tony,Anoop Krishnan N. M.^ORCID,Smedskjaer Morten M.^ORCID,Ryan Joseph V.,Gin Stéṕhane^ORCID,Bauchy Mathieu

Funder

National Science Foundation

This research was performed using funding received from the DOE Office of Nuclear Energy's Nuclear Energy University Programs.

Publisher

Springer Science and Business Media LLC

Subject

Materials Chemistry,Materials Science (miscellaneous),Chemistry (miscellaneous),Ceramics and Composites

Link

http://www.nature.com/articles/s41529-019-0094-1.pdf

Reference56 articles.

1. Mauro, J. C., Tandia, A., Vargheese, K. D., Mauro, Y. Z. & Smedskjaer, M. M. Accelerating the design of functional glasses through modeling. Chem. Mater. 28, 4267–4277 (2016).

2. Brauer, D. S., Rüssel, C. & Kraft, J. Solubility of glasses in the system P2O5–CaO–MgO–Na2O–TiO2: experimental and modeling using artificial neural networks. J. Non-Cryst. Solids 353, 263–270 (2007).

3. Cassar, D. R., de Carvalho, A. C. P. L. F. & Zanotto, E. D. Predicting glass transition temperatures using neural networks. Acta Mater. 159, 249–256 (2018).

4. Yang, K. et al. Prediction of silicate glasses’ stiffness by high-throughput molecular dynamics simulations and machine learning. arXiv:1901.09323 [cond-mat, physics:physics] (2019).

5. Anoop Krishnan, N. M. et al. Predicting the dissolution kinetics of silicate glasses using machine learning. J. Non-Cryst. Solids 487, 37–45 (2018).

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