Dataset: Predicting densities and elastic moduli of SiO2-based glasses by machine learning
Published: 3 years ago Views: 1208 Downloads: 674 DOI: doi:10.13011/m3-4kwv-g523 License: Open Database License (ODC-ODbL) Size: 0 B
  • Yong-Jie Hu
  • Ge Zhao
  • Mingfei Zhang
  • Bin Bin
  • Tyler Del Rose
  • Qian Zhao
  • Qun Zu
  • Yang Chen
  • Xuekun Sun
  • Maarten de Jong
  • Liang Qi

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. University of Michigan