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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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Yang K,Xu X,Yang B,Cook B,Ramos H,Krishnan NMA,Smedskjaer MM,Hoover C,Bauchy M

doi

10.1038/s41598-019-45344-3

subject

Has Abstract

pub_date

2019-06-19 00:00:00

pages

8739

issue

1

issn

2045-2322

pii

10.1038/s41598-019-45344-3

journal_volume

9

pub_type

杂志文章

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