Chemical design of SiO_(2)-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 composi...Chemical design of SiO_(2)-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 SiO_(2)-based glasses can be efficiently predicted by machine learning(ML)techniques across a complex compositional space with multiple(>10)types of additive oxides besides SiO_(2).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).展开更多
基金This work also used the Extreme Science and Engineering Discovery Environment(XSEDE)Stampede2 at the TACC through allocation TG-DMR190035.
文摘Chemical design of SiO_(2)-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 SiO_(2)-based glasses can be efficiently predicted by machine learning(ML)techniques across a complex compositional space with multiple(>10)types of additive oxides besides SiO_(2).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).
