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 Mattrup Smedskjær, Christian Hoover, Mathieu Bauchy

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

9 Citationer (Scopus)
24 Downloads (Pure)

Abstrakt

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.

OriginalsprogEngelsk
Artikelnummer8739
TidsskriftScientific Reports
Vol/bind9
Udgave nummer1
Antal sider11
ISSN2045-2322
DOI
StatusUdgivet - 19 jun. 2019

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