Vladimir P. Antropov

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Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in(More)
The electronic structure and magnetic properties of pure and doped Fe 16 N 2 systems have been studied in the local-density (LDA) and quasiparticle self-consistent GW approximations. The GW magnetic moment of pure Fe 16 N 2 is somewhat larger compared to LDA but not anomalously large. The effects of doping on magnetic moment and exchange coupling were(More)
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