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The magnets are typically classified into Stoner and Heisenberg type, depending on the itinerant or localized nature of the constituent magnetic moments. In this work, we investigate theoretically the behaviour of the magnetic moments of iron and cobalt in their B2-ordered alloy. The results based on local spin density approximation for the density functional theory (DFT) suggest that the Co magnetic moment strongly depends on the directions of the surrounding magnetic moments, which usually indicates the Stoner-type mechanism of magnetism. This is consistent with the disordered local moment picture of the paramagnetic state, where the magnetic moment of cobalt gets substantially suppressed. We argue that this is due to the lack of strong on-site electron correlations, which we take into account by employing a combination of DFT and dynamical mean-field theory (DMFT). Within LDA + DMFT, we find a substantial quasiparticle mass renormalization and a non Fermi-liquid behaviour of Fe-3d orbitals. The resulting spectral functions are in very good agreement with measured spin-resolved photoemission spectra. Our results suggest that local correlations play an essential role in stabilizing a robust local moment on Co in the absence of magnetic order at high temperatures.

The pathway toward the tailored synthesis of materials starts with precise characterization of the conformational properties and dynamics of individual molecules. Electron spin resonance (ESR)-based scanning tunneling microscopy can potentially address molecular structure with unprecedented resolution. Here, we determine the fine structure and geometry of an individual titanium-hydride molecule, utilizing a combination of a newly developed millikelvin ESR scanning tunneling microscope in a vector magnetic field and ab initio approaches. We demonstrate a strikingly large anisotropy of the g tensor, unusual for a spin doublet ground state, resulting from a nontrivial orbital angular momentum stemming from the molecular ground state. We quantify the relationship between the resultant fine structure, hindered rotational modes, and orbital excitations. Our model system provides avenues to determine the structure and dynamics of individual molecules.

Complexity of patterns is key information for human brain to differ objects of about the same size and shape. Like other innate human senses, the complexity perception cannot be easily quantified. We propose a transparent and universal machine method for estimating structural (effective) complexity of two-dimensional and three-dimensional patterns that can be straightforwardly generalized onto other classes of objects. It is based on multistep renormalization of the pattern of interest and computing the overlap between neighboring renormalized layers. This way, we can define a single number characterizing the structural complexity of an object. We apply this definition to quantify complexity of various magnetic patterns and demonstrate that not only does it reflect the intuitive feeling of what is "complex" and what is "simple" but also, can be used to accurately detect different phase transitions and gain information about dynamics of nonequilibrium systems. When employed for that, the proposed scheme is much simpler and numerically cheaper than the standard methods based on computing correlation functions or using machine learning techniques.