TY - JOUR
T1 - Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning
AU - Yang, Kai
AU - Xu, Xinyi
AU - Yang, Benjamin
AU - Cook, Brian
AU - Ramos, Herbert
AU - Krishnan, N. M.Anoop
AU - Smedskjaer, Morten M.
AU - Hoover, Christian
AU - Bauchy, Mathieu
N1 - Funding Information:
This work was supported by the National Science Foundation under Grants No. 1562066, 1762292, and 1826420. M.M.S. acknowledges support from the Independent Research Fund Denmark under Grant No. 8105-00002.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41598-019-45344-3
DO - 10.1038/s41598-019-45344-3
M3 - Article
C2 - 31217500
AN - SCOPUS:85067832489
SN - 2045-2322
VL - 9
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 8739
ER -