312 citations · 324 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2019★ 10 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-sci2016★ 2 cited
Predicting the lattice thermal conductivity of solids by solving the Boltzmann transport equation: AFLOW - AAPL an automated, accurate and effcient framework
Jose J. Plata, Demet Usanmaz, Pinku Nath +7
One of the most accurate approaches for calculating lattice thermal conductivity, , is solving the Boltzmann transport equation starting from third-order anharmonic force cons…
cond-mat.mtrl-sci2014★ 312 cited
High-Throughput Computational Screening of thermal conductivity, Debye temperature and Grüneisen parameter using a quasi-harmonic Debye Model
Cormac Toher, Jose J. Plata, Ohad Levy +4
The quasi-harmonic Debye approximation has been implemented within the AFLOW and Materials Project frameworks for high-throughput computational science (Automatic Gibbs Library, AG…