High throughput combinatorial method for fast and robust prediction of lattice thermal conductivity

Abstract

The lack of computationally inexpensive and accurate ab-initio based methodologies to predict lattice thermal conductivity, without computing the anharmonic force constants or time-consuming ab-initio molecular dynamics, is one of the obstacles preventing the accelerated discovery of new high or low thermal conductivity materials. The Slack equation is the best alternative to other more expensive methodologies but is highly dependent on two variables: the acoustic Debye temperature, θ a , and the Grüneisen parameter, γ. Furthermore, different definitions can be used for these two quantities depending on the model or approximation. In this article, we present a combinatorial approach to elucidate which definitions of both variables produce the best predictions of the lattice thermal conductivity, κ l . A set of 42 compounds was used to test accuracy and robustness of all possible combinations. This approach is ideal for obtaining more accurate values than fast screening models based on the Debye model, while being significantly less expensive than methodologies that solve the Boltzmann transport equation.

An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

“…7,86 Qualitatively, all the three frameworks have high linear correlation with experiments (Pearson, r); AAPL and QHA-APL are also very effective in rank ordering the compounds (Spearman, ρ).…”

“…6 Fast and robust predictions of this quantity remain a challenge: 7 semi-empirical models [8][9][10] are computationally inexpensive but require some experimental data. Similarly, classical molecular dynamics combined with Green-Kubo relations [11][12][13] is reasonably quick but requires the knowledge of specific force fields.…”

“…7,17 Although QHA-based models overall improve accuracy of κ ' , they are far from the results obtained from calculating the anharmonic force constants and solving the associated Boltzmann transport equation (BTE). 8,18 To the best of our knowledge, solving the BTE is the optimal method for systematically and accurately calculating thermal conductivity.…”

Automated Computation of Materials Properties

“…The quasi-harmonic approximation is the less computationally demanding of these two methods, and compares harmonic calculations of phonon properties at different volumes to predict anharmonic properties. The different volume calculations can be in the form of harmonic phonon calculations as described above [148,149], or simple static primitive cell calculations [49,136]. The Quasi-Harmonic Approximation is implemented within APL and referred to as QHA-APL [49].…”

“…The specific heat capacity, Debye temperature and Grüneisen parameter can then be combined to calculate other properties such as the specific heat capacity at constant pressure C p , the thermal coefficient of expansion α, and the lattice thermal conductivity κ L [149], using similar expressions to those described in Section 3 C.…”