%0 Journal Article %A Matthew Kristofer Horton %A Joseph Harold Montoya %A Miao Liu %A Kristin A Persson %B npj Computational Materials %D 2019 %G eng %N 1 %R 10.1038/s41524-019-0199-7 %T High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory %V 5 %8 06/2019 %! npj Comput Mater %X

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.