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

Abstract

Thorough characterization of the thermo-mechanical 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 thermo-elastic 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 thermo-elastic 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.

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

“…AAPL: AutomaticAnharmonic-Phonon-Library; AGL: AFLOW-Gibbs-Library; 15 APL: Automatic-Phonon-Library; 28,35,16 QHA: quasi-harmonic approximation; 7 ACBN0: Agapito Curtarolo Buongiorno Nardelli ab-initio DFT functional; 40 AFLOWπ: A minimalist AFLOW-Python approach to high-throughput ab-initio calculations for the generation of tight-binding hamiltonians and the calculation with the ACBN0 functional. 111 …”

“…Similarly, classical molecular dynamics combined with Green-Kubo relations [11][12][13] is reasonably quick but requires the knowledge of specific force fields. On the contrary, frameworks based on the quasiharmonic Debye model, such as GIBBS 14 or the Automatic-Gibbs-Library (AGL), 15,16 are extremely efficient as pre-screening techniques but they lack quantitative accuracy.…”

Predicting superhard materials via a machine learning informed evolutionary structure search

“…This set of materials spanned a wide range of hardness values (H Exp v = 0.2 − 96.0 GPa), and included ionic and covalently bonded crystals, as well as intermetallics. It has been demonstrated that AFLOW-AEL calculates reasonable bulk and shear moduli [29], so it should not be a surprise that the Vickers hardnesses obtained using them are in good agreement with experiment, and the predicted values reflect the variation in the results of the models themselves.…”

“…Detailed computational settings for the AFLOW-AEL [29] calculations used to determine the elastic properties are described in Ref. 66.…”

“…AFLOW (Automatic FLOW) is an automatic framework for high-throughput materials discovery [17] that includes a materials database with over 2 million entries [27]. Many properties have been calculated for the materials in the AFLOW database including vibrational properties with the Automatic Phonon Library (APL) [17], thermal properties with the Automatic GIBBS Library (AGL) [28], and thermomechanical properties such as the bulk and shear moduli with the Automatic Elasticity Library (AEL) [22,29]. It is now possible to interact with the AFLOW database via a RESTful API [30] and the AFLUX materials search API [31].…”