High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory

High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory

TitleHigh-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory
Publication TypeJournal Article
Year of Publication2019
AuthorsMatthew Kristofer Horton, Joseph Harold Montoya, Miao Liu, Kristin A Persson
Journalnpj Computational Materials
Volume5
Issue1
Date Published06/2019
Abstract

We present a robust, automatic high-throughput workflow for the calculation of magnetic ground state of solid-state inorganic crystals, whether ferromagnetic, antiferromagnetic or ferrimagnetic, and their associated magnetic moments within the framework of collinear spin-polarized Density Functional Theory. This is done through a computationally efficient scheme whereby plausible magnetic orderings are first enumerated and prioritized based on symmetry, and then relaxed and their energies determined through conventional DFT + U calculations. This automated workflow is formalized using the atomatecode for reliable, systematic use at a scale appropriate for thousands of materials and is fully customizable. The performance of the workflow is evaluated against a benchmark of 64 experimentally known mostly ionic magnetic materials of non-trivial magnetic order and by the calculation of over 500 distinct magnetic orderings. A non-ferromagnetic ground state is correctly predicted in 95% of the benchmark materials, with the experimentally determined ground state ordering found exactly in over 60% of cases. Knowledge of the ground state magnetic order at scale opens up the possibility of high-throughput screening studies based on magnetic properties, thereby accelerating discovery and understanding of new functional materials.

DOI10.1038/s41524-019-0199-7
Short Titlenpj Comput Mater
Refereed DesignationRefereed