Journal article
Predicting densities and elastic moduli of SiO2-based glasses by machine learning
npj computational materials, v 6(1), 25
20 Mar 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales. Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (>10) types of additive oxides besides SiO2. Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO2-based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (similar to 10(5)) compositions of a multicomponent glass system to construct a compositional-property database that allows for a fruitful overview on the glass density and elastic properties.
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Details
- Title
- Predicting densities and elastic moduli of SiO2-based glasses by machine learning
- Creators
- Yong-Jie Hu - Univ Michigan, Dept Mat Sci & Engn, Ann Arbor, MI 48109 USAGe Zhao - Pennsylvania State UniversityMingfei Zhang - University of Michigan–Ann ArborBin Bin - University of Michigan–Ann ArborTyler Del Rose - University of Michigan–Ann ArborQian Zhao - China National Building Materials GroupQun Zu - China National Building Materials GroupYang Chen - China National Building Materials GroupXuekun Sun - ContinentalMaarten de Jong - University of California, BerkeleyLiang Qi - University of Michigan–Ann Arbor
- Publication Details
- npj computational materials, v 6(1), 25
- Publisher
- Springer Nature
- Number of pages
- 13
- Grant note
- Continental Technology LLC, Indianapolis, Indiana, USA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Materials Science and Engineering
- Web of Science ID
- WOS:000521993100004
- Scopus ID
- 2-s2.0-85083222912
- Other Identifier
- 991021931770104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Physical
- Materials Science, Multidisciplinary