通过快速维数分析揭示各向异性磁相位
Uncovering anisotropic magnetic phases via fast dimensionality analysis
摘要：
提出了一种磁相互作用维数的定量几何预测器。该预测器基于超交换相互作用的网络，并且可以快速计算任意化学、占据或对称性的晶体化合物。所得的数据是有用的分类结构家族的磁性化合物。从42，520种化合物开始，我们对含有$3d$过渡金属阳离子的化合物进行了分类和定量。预测揭示了磁相互作用的趋势，这些趋势通常不明显，从化合物的空间群，如三斜或单斜化合物，是强烈的二维。我们提出了具体的情况下，预测识别化合物，应该表现出1D和2D之间的相互作用的竞争，以及如何预测可用于识别稀疏填充的区域的化学空间与尚未探索的拓扑结构的特定$3d$磁性阳离子。可以使用可搜索的网络表单访问预测器以获得化合物的完整列表，并且可以自由导出关于阳离子-阴离子和阳离子-阳离子配位的连接性、对称性和价态的进一步信息。
英文摘要：
A quantitative geometric predictor for the dimensionality of magnetic interactions is presented. 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 is 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. The predictor can be accessed for the full list of compounds using a searchable web form, and further information on the connectivity, symmetry, and valence of cation-anion and cation-cation coordination can be freely exported.
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