Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning

The application of machine learning to predict materials’ properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throu...

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Detalles Bibliográficos
Autores principales: Yang, Kai, Xu, Xinyi, Yang, Benjamin, Cook, Brian, Ramos, Herbert, Krishnan, N. M. Anoop, Smedskjaer, Morten M., Hoover, Christian, Bauchy, Mathieu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584533/
https://www.ncbi.nlm.nih.gov/pubmed/31217500
http://dx.doi.org/10.1038/s41598-019-45344-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584533/
https://www.ncbi.nlm.nih.gov/pubmed/31217500
http://dx.doi.org/10.1038/s41598-019-45344-3

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