The tetranuclear iron(III) compounds [Fe4(μ3-O)2(μ-LZ)4] (1–3) were obtained by reaction of FeCl3 with the shortened salen-type N2O2 tetradentate Schiff bases N,N’-bis(salicylidene)-o-Z-phenylmethanediamine H2LZ (Z = NO2, Cl and OMe, respectively), where the one-carbon bridge between the two iminic nitrogen donor atoms guide preferentially to the formation of oligonuclear species, and the ortho position of the substituent Z on the central phenyl ring selectively drives towards Fe4 bis-oxido clusters. All compounds show a flat almost-symmetric butterfly-like conformation of the {Fe4(μ3-O)2} core, surrounded by the four Schiff base ligands, as depicted by both the X-ray molecular structures of 1 and 2 and the optimized geometries of all derivatives as obtained by UM06/6-311G(d) DFT calculations. The strength of the antiferromagnetic exchange coupling constants between the iron(III) ions varies among the three derivatives, despite their magnetic cores remain structurally almost unvaried, as well as the coordination of the metal ions, with a distorted octahedral environment for the two-body iron ions, Feb, and a pentacoordination with trigonal bipyramidal geometry for the two-wing iron ions, Few. The different magnetic behavior within the series of examined compounds may be ascribed to the influence of the electronic features of Z on the electron density distribution (EDD) of the central {Fe4(μ3-O)2} core, substantiated by a Quantum Theory of Atoms In Molecules (QTAIM) topological analysis of the EDD, as obtained by UM06 calculations 1–3.
When building classification models of complex systems with many classes, the traditional chemometric approaches such as discriminant analysis or soft independent modeling of class analogy often fail. Some people resort to advanced deep neural network, but this is only an option if there is access to very many samples. Another alternative often used is to build hierarchical models where subclasses are sort of peeled off one or a few at a time. Such approaches often outperform classical classification as well as deep neural network on small multi-class problems. The downside though is that it is very cumbersome to build such hierarchies of models. It requires substantial work of a skilled person. In this paper, we develop a fully automated approach for building hierarchical models and test the performance on a number of classification problems.
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