Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids

Thorough characterization of the thermomechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and is one of the main obstacles for the development of effective accelerated materials design strategies. The rapid screening of new potential materials requires highly integrated, sophisticated, and robust computational approaches. We tackled the challenge by developing an automated, integrated workflow with robust error-correction within the AFLOW framework which combines the newly developed “Automatic Elasticity Library” with the previously implemented GIBBS method. The first extracts the mechanical properties from automatic self-consistent stress-strain calculations, while the latter employs those mechanical properties to evaluate the thermodynamics within the Debye model. This new thermoelastic workflow is benchmarked against a set of 74 experimentally characterized systems to pinpoint a robust computational methodology for the evaluation of bulk and shear moduli, Poisson ratios, Debye temperatures, Grüneisen parameters, and thermal conductivities of a wide variety of materials. The effect of different choices of equations of state and exchange-correlation functionals is examined and the optimum combination of properties for the Leibfried-Schlömann prediction of thermal conductivity is identified, leading to improved agreement with experimental results than the GIBBS-only approach. The framework has been applied to the AFLOW.org data repositories to compute the thermoelastic properties of over 3500 unique materials. The results are now available online by using an expanded version of the REST-API described in the Appendix.

Figure 1

(a) Bulk modulus, (b) shear modulus, (c) Poisson ratio, (d) lattice thermal conductivity at 300 K, (e) acoustic Debye temperature, and (f) Grüneisen parameter of zinc-blende (AFLOW prototype: $\mathsf{AB}\_\mathsf{cF}\mathsf{8}\_\mathsf{216}\_\mathsf{c}\_\mathsf{a}$ [62]) and diamond ($\mathsf{A}\_\mathsf{cF}\mathsf{8}\_\mathsf{227}\_\mathsf{a}$ [62]) structure semiconductors.

Figure 2

(a) Bulk modulus, (b) shear modulus, (c) Poisson ratio, (d) lattice thermal conductivity at 300 K, (e) Debye temperature, and (f) Grüneisen parameter of rock-salt structure (AFLOW Prototype: $\mathsf{AB}\_\mathsf{cF}\mathsf{8}\_\mathsf{225}\_\mathsf{a}\_\mathsf{b}$ [62]) semiconductors. The Debye temperatures plotted in (b) are ${\theta }_{\mathrm{a}}$, except for SnTe where ${\theta }_{\mathrm{D}}$ is quoted in Ref. [88].

Figure 3

(a) Bulk modulus, (b) shear modulus, (c) Poisson ratio, (d) lattice thermal conductivity, (e) Debye temperature, and (f) Grüneisen parameter of hexagonal structure semiconductors. The Debye temperatures plotted in (e) are ${\theta }_{\mathrm{a}}$, except for InSe and InN where ${\theta }_{\mathrm{D}}$ values are quoted in Refs. [88, 89, 114].

Figure 4

(a) Lattice thermal conductivity of rhombohedral semiconductors at 300 K. (b) Lattice thermal conductivity of body-centred tetragonal semiconductors at 300 K.