Vienna

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There is a dictum to "never change a running system." New methods can however be far superior to older ones. While to date chemical reactions are mainly accelerated by catalytic materials that comprise several hundreds of atoms, the use of single atoms could provide a new approach for catalysis.

Researchers at the University of Vienna's Faculty of Physics in collaboration with colleagues from the Oak Ridge National Laboratory in the U.S. have uncovered a non-destructive mechanism to manipulate donor impurities within silicon using focused electron irradiation. In this novel indirect exchange process not one but two neighboring silicon atoms are involved in a coordinated atomic "waltz," which may open a path for the fabrication of solid-state qubits. The results have been published in the Journal of Physical Chemistry.

Three dimensional (3D) nano-networks promise a new era in modern solid state physics with numerous applications in photonics, bio-medicine, and spintronics. The realization of 3D magnetic nano-architectures could enable ultra-fast and low-energy data storage devices. Due to competing magnetic interactions in these systems, magnetic charges or magnetic monopoles can emerge, which can be utilized as mobile, binary information carriers. Researchers at University of Vienna have now designed the first 3D artificial spin ice lattice hosting unbound magnetic charges. The results published in the journal npj Computational Materials present a first theoretical demonstration that, in the new lattice, the magnetic monopoles are stable at room temperature and can be steered on-demand by external magnetic fields.

3D configurations of atoms dictate all materials properties. Quantitative predictions of accurate equilibrium structures, 3D coordinates of all atoms, from a chemical graph, a representation of the structural formula, is a challenging and computationally expensive task which is at the beginning of practically every computational chemistry workflow. Researchers at the University of Vienna have now developed a new machine learning based model to shortcut expensive calculations to directly predict structures from graphs. The new method for "Machine learning based energy-free structure predictions of molecules, transition states, and solids" is presented in the latest issue of Nature Communications.