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

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number8739
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

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Silicates
Elastic Modulus
Molecular Dynamics Simulation
Glass
Machine Learning

ASJC Scopus subject areas

  • General

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Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning. / Yang, Kai; Xu, Xinyi; Yang, Benjamin; Cook, Brian; Ramos, Herbert; Krishnan, N. M.Anoop; Smedskjaer, Morten M.; Hoover, Christian; Bauchy, Mathieu.

In: Scientific reports, Vol. 9, No. 1, 8739, 01.12.2019.

Research output: Contribution to journalArticle

Yang, Kai ; Xu, Xinyi ; Yang, Benjamin ; Cook, Brian ; Ramos, Herbert ; Krishnan, N. M.Anoop ; Smedskjaer, Morten M. ; Hoover, Christian ; Bauchy, Mathieu. / Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning. In: Scientific reports. 2019 ; Vol. 9, No. 1.
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