Y.J.H. and Q.L. acknowledge support by the gift funding from Continental Technology LLC, Indianapolis, Indiana, USA. The high-throughput MD simulations were supported through computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor. This work also used the Extreme Science and Engineering Discovery Environment (XSEDE) Stampede2 at the TACC through allocation TG-DMR190035.
Strength of materials, Crystal Structure
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 (~105) 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.
Locate the Document
Hu, Yong-Jie; Zhao, Ge; Zhang, MingFei; Bin, Bin; Del Rose, Tyler; Zhao, Qian; Zu, Qan; Chen, Yang; Sun, Xuekun; de Jong, Maarten; and multiple additional authors, "Predicting Densities and Elastic Moduli of SiO2-based Glasses by Machine Learning" (2020). Mathematics and Statistics Faculty Publications and Presentations. 284.