High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model
By Cormac Toher, J. Plata, Ohad Levy, M. de Jong, Mark Asta, Marco Buongiorno Nardelli, Stefano Curtarolo. 2014
222 citations recorded by the source corpus.
The quasiharmonic Debye approximation has been implemented within the aflow and Materials Project frameworks for high-throughput computational materials science (Automatic Gibbs Library, agl), in order to calculate thermal properties such as the Debye temperature and the thermal conductivity of materials. We demonstrate that the agl method, which is significantly cheaper computationally compared to the fully ab initio approach, can reliably predict the ordinal ranking of the thermal conductivity for several different classes of semiconductor materials. In particular, a high Pearson (i.e., linear) correlation is obtained between the experimental and agl computed values of the lattice thermal conductivity for a set of 75 compounds including materials with cubic, hexagonal, rhombohedral, and tetragonal symmetry.
Subfields: Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/048575e79afa75fbe21fd645c90fc014554bbcdb
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In Materials Chemistry
- Stefano Curtarolo
- Shyue Ping Ong
- Cormac Toher
- Anubhav Jain
- Kristin A. Persson
- Marco Buongiorno Nardelli
- Muratahan Aykol
- Gus L. W. Hart
Citations in the corpus
- Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows (2017, methodology): “Atomate includes a standard procedure modeled after the Automatic Gibbs Library (AGL) [48] workflow for fitting energy-volume data to equations of state and the subsequent computation of Fig.”
- The AFLOW standard for high-throughput materials science calculations (2015, methodology): “d from a single calculation. For example, sets of RELAXand STATICcalculations for different cell volumes and/or atomic configurations are used to calculate thermal and me chanical properties by the AGL [12] and APL [1] methods inplemented within the AFLOW framework. In the following, we describe the p”