Modeling Off-Stoichiometry Materials with a High-Throughput Ab-Initio Approach

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 (ZnS1−xSex), a wide-gap oxide semiconductor (MgxZn1−xO), and an iron alloy (Fe1−xCux) at various stoichiometries. arXiv:1511.04373v1 [cond-mat.mtrl-sci]

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

“…All structures are fully relaxed using the automated framework AFLOW [28][29][30][31][32][33][34][35][36][37][38][39] and the VASP package. 113 Optimizations are performed following the AFLOW standards.…”

“…For a rational software for accelerated materials development, all the geometric optimizations, symmetry analyses, supercell creation, pre-processing and post-processing, and automatic error corrections to get the IFCs, in addition to the appropriate integration for the BTE must be performed by a single code. Here, we present Automatic Anharmonic Phonon Library (AAPL), which computes the IFCs and solves the BTE to predict κ ' as part of the AFLOW high-throughput framework, [28][29][30][31][32][33][34][35][36][37][38][39] automatizing the entire process. The software is being finalized for an official open-source release during 2017, within the GNU GPL license.…”

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

“…Since their inception, high throughput materials science frameworks such as AFLOW [1][2][3][4][5][6][7][8] have been amassing large databases of materials properties. For instance, the AFLOW database [9][10][11][12] alone contains over 1.7 million material compounds with over 170 million calculated properties, generated from the Inorganic Crystal Structure Database (ICSD) [13][14][15], as well as by decorating crystal structure prototypes [16].…”

Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides

“…The technological relevance of cation-disordered oxides creates the desire to predict whether a given composition is likely to be disordered. While high-throughput firstprinciples computations are useful to screen specific composition spaces for stable disordered compounds [6][7][8][9], a better understanding of the origin of cation disorder might lead to simple design criteria so that time-consuming computations can be avoided.…”