312 citations · 377 across the 4 of their papers we have counts for
4 papers
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…
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…
Combining the AFLOW GIBBS and Elastic Libraries for efficiently and robustly screening thermo-mechanical properties of solids
Cormac Toher, Corey Oses, Jose J. Plata +8
Thorough characterization of the thermo-mechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and it i…
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…