Details

Autor(en) / Beteiligte

• Yang, Kai
• Xu, Xinyi
• Yang, Benjamin
• Cook, Brian
• Ramos, Herbert
• Krishnan, N M Anoop
• Smedskjaer, Morten M
• Hoover, Christian
• Bauchy, Mathieu

Titel

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

Ist Teil von

• Scientific reports, 2019-06, Vol.9 (1), p.8739-11, Article 8739

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.