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

ABSTRACT: 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.

SUBMITTER: Yang K

PROVIDER: S-EPMC6584533 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Yang Kai K Xu Xinyi X Yang Benjamin B Cook Brian B Ramos Herbert H Krishnan N M Anoop NMA Smedskjaer Morten M MM Hoover Christian C Bauchy Mathieu M

Scientific reports 20190619 1

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 d ...[more]

PMID: 31217500

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