Combining the AFLOW GIBBS and Elastic Libraries for efficiently and robustly screening thermo-mechanical properties of solids

TL;DR

This paper introduces a combined computational approach using the AFLOW GIBBS and Elastic Libraries to efficiently and robustly screen the thermo-mechanical properties of over 3,000 crystalline solids, aiding accelerated materials design.

Contribution

It develops and benchmarks a new thermo-elastic library that integrates mechanical and thermodynamic property calculations within the AFLOW framework, improving accuracy over previous methods.

Findings

The combined approach accurately predicts bulk and shear moduli.

It provides reliable estimates of Debye temperatures and thermal conductivities.

The method outperforms the GIBBS-only approach in matching experimental data.

Abstract

Thorough characterization of the thermo-mechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and it is one of the main obstacles for the development of effective accelerated materials design strategies. The rapid screening of new potential systems requires highly integrated, sophisticated and robust computational approaches. We tackled the challenge by surveying more than 3,000 crystalline solids within the AFLOW framework with the newly developed "Automatic Elasticity Library" combined 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. The new thermo-elastic library 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, Gr\"uneisen parameters, and thermal conductivities of a wide variety of materials. The effect of different choices of equations of state is examined and the optimum combination of properties for the Leibfried-Schl\"omann prediction of thermal conductivity is identified, leading to improved agreement with experimental results than the GIBBS-only approach.