Uncovering new low-dimensional magnets: A computational toolbox and experimental results

A quantitative geometric predictor for the dimensionality of magnetic interactions has been released as a free searchable database. This predictor is based on networks of superexchange interactions and can be quickly calculated for crystalline compounds of arbitrary chemistry, occupancy, or symmetry. The resulting data are useful for classifying structural families of magnetic compounds. Starting with 42,520 compounds, we have classified and quantified compounds with 3d transition metal cations. The predictor reveals trends in magnetic interactions that are often not apparent from the space group of the compounds, such as triclinic or monoclinic compounds that are strongly 2D. We present specific cases where the predictor identifies compounds that should exhibit competition between 1D and 2D interactions, and how the predictor can be used to identify sparsely-populated regions of chemical space with as-yet-unexplored topologies of specific 3d magnetic cations. Use of the toolkit to identify new low-dimensional spin-1/2 materials will be discussed, along with the prospects for future applications to rare-earth systems.