Modeling Disordered Materials with a High Throughput ab-initio Approach

TL;DR

This paper presents an automated high-throughput ab-initio framework for modeling disordered materials, enabling the calculation of their properties by considering all relevant supercell configurations and ensemble averaging.

Contribution

The authors introduce a novel software framework that automates the modeling of partial occupation in disordered materials using high-throughput first principles calculations.

Findings

Successfully modeled properties of ZnS$_{1-x}$Se$_x$, Mg$_{x}$Zn$_{1-x}$O, and Fe$_{1-x}$Cu$_{x}$ at various stoichiometries.

Demonstrated the framework's ability to evaluate ensemble average properties as a function of temperature.

Validated the approach with case studies on different classes of disordered materials.

Abstract

Predicting material properties of disordered systems remains a long-standing and formidable challenge in rational materials design. To address this issue, we introduce an automated software framework capable of modeling partial occupation within disordered materials using a high-throughput (HT) first principles approach. At the heart of the approach is the construction of supercells containing a virtually equivalent stoichiometry to the disordered material. All unique supercell permutations are enumerated and material properties of each are determined via HT electronic structure calculations. In accordance with a canonical ensemble of supercell states, the framework evaluates ensemble average properties of the system as a function of temperature. As proof of concept, we examine the framework's final calculated properties of a zinc chalcogenide (ZnS$_{1-x}$Se$_x$), a wide-gap oxide semiconductor (Mg$_{x}$Zn$_{1-x}$O), and an iron alloy (Fe$_{1-x}$Cu$_{x}$) at various stoichiometries.