|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Computational Design and Simulation of Materials (CDSM 2018): Computational Design of Materials
||Optimizing Elastic Moduli of the Silicate Glasses through High-throughput Atomistic Modeling and Machine Learning Techniques
||Yong-Jie Hu, Ge Zhao, Tyler Del Rose, Maarten De Jong, Liang Qi
|On-Site Speaker (Planned)
Chemical design of the silicate glass with high elastic moduli 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 several material properties at different length scales. This work presents a computational framework to efficiently predict the elastic moduli of the silicate glass across a multicomponent compositional design space, including 11 types of additive oxides, by an integration of high-throughput molecular dynamic (MD) calculations and machine learning (ML) techniques. Our newly developed ML model can predict the elastic moduli for k-nary silicate glass systems, using the learning datasets generated from MD calculations for only binary and ternary systems. The usefulness of our model is illustrated by identifying the most relevant materials descriptors that determine the elastic moduli and screening for the silicate glasses with high stiffness.
||Planned: Supplemental Proceedings volume