activity
20172022
most citedPredicting densities and elastic moduli of SiO2-based glasses by machine learning

10 citations · 17 across the 4 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci2022

A bond counting model for accurate prediction of lattice parameter of bcc solid solution alloys

Chris Tandoc, Liang Qi, Yong-Jie Hu

Lattice Parameter is an important material feature in High Entropy Alloy (HEA) Design. Vegards Law is typically used to estimate lattice parameters but is often inaccurate for meta…

cond-mat.mtrl-sci20206 cited

Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Yong-Jie Hu, Aditya Sundar, Shigenobu Ogata +1

Body-centered cubic (bcc) refractory multicomponent alloys are of great interest due to their remarkable strength at high temperatures. Meanwhile, further optimizing the chemical c…

cond-mat.mtrl-sci201910 cited

Predicting densities and elastic moduli of SiO2-based glasses by machine learning

Yong-Jie Hu, Ge Zhao, Mingfei Zhang +8

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 m…

cond-mat.mtrl-sci2018

Universal correlation between electronic factors and solute-defect interactions in bcc refractory metals

Yong-Jie Hu, Ge Zhao, Baiyu Zhang +4

The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries play an essential role in determining physical, chemical and mec…

cond-mat.mtrl-sci20171 cited

A First-principles approach to predict Seebeck coefficients: Application to La3-xTe4

Yi Wang, Yong-Jie Hu, Shun-Li Shang +6

Theoretical descriptions of the Seebeck coefficient in terms of the differential electrical conductivity given by Cutler and Mott is the foundation of later works in terms of trans…