Previous experimental studies discovered universal growth of chains and nanowires of various chemical elements on a corrugated molecular network of Cu3N on the Cu(110). Herein, performing combined ab initio and quantum Hamiltonian studies we demonstrate that such chains can be used for a fast spin switching and entanglement generation by locally applied magnetic pulses. As an example, we show that in antiferromagnetic Co chains a strong entanglement between ends of chains occurs during spin switching. A novel parity effect in spin dynamics is reported. Even-numbered chains are found to exhibit significantly faster spin switching than odd-numbered counterparts. Moreover, at certain parameters of the system the dimerization effect in the spin dynamics of the chains was found. Our studies give a clear evidence that tailoring spin dynamics and entanglement can be achieved by magnetic fields and by tuning exchange interactions in supported chains.
We present a first-principles study of spin-dependent electron transport through gold nanochains suspended between cobalt electrodes aimed at elucidating the electronic origin of the high magnetoresistance recently observed experimentally in such nanojunctions. Our nonequilibrium Green's function based calculations confirm the occurrence of high spin polarization of conductance in Co/Au/Co nanocontacts, which in some cases reaches 90%. Our analysis allows to relate such behavior to the hybridization of electronic orbitals of neighboring cobalt and gold atoms of the nanocontact. The results obtained give clear evidence of the presence of spin injection from Co electrodes into paramagnetic gold contacts.
Several recent experiments have shown that long-range exchange interactions can determine collective magnetic ground states of nanostructures in bulk and on surfaces. The ability to generate and control entanglement in a system with long-range interaction will be of great importance for future quantum technology. An important step forward to reach this goal is the creation of entangled states for spins of distant magnetic atoms. Herein, the generation of long-distance entanglement between remote spins at large separations in bulk and on surface is studied theoretically, based on a quantum spin Hamiltonian and time-dependent Schrödinger equation for experimentally realized conditions. We demonstrate that long-distance entanglement can be generated between remote spins by using an appropriate quantum spin chain (a quantum mediator), composed by sets of antiferromagnetically coupled spin dimers. Ground state properties and quantum spin dynamics of entangled atoms are studied. We demonstrate that one can increase or suppress entanglement by adding a single spin in the mediator. The obtained result is explained by monogamy property of entanglement distribution inside a quantum spin system. We present a novel approach for non-local sensing of remote magnetic adatoms via spin entanglement.
Gadolinium nitride (GdN) nanocontacts were recently experimentally shown to be efficient spin filters. Our study is aimed at identifying and analyzing the physical processes responsible for the high spin polarization of the tunneling current in GdN nanostructures. By the example of planar contacts and atomic chains attached to Cu electrodes we assert, using first principle techniques, that a 100% spin-filtering effect can be indeed achieved in GdN nanocontacts. Our analysis shows that the spin filtering is due to the predominant role of nitrogen majority p states in the electron transport, while minority conductance decays exponentially with contact size due to the presence of a minority band gap at the Fermi level. Additionally, GdN zigzag infinite chains are found to be as efficient spin filters as their planar contact counterparts, also exhibiting a 100% spin-filtering effect, which is robust against chain geometry changes.
Advanced algorithms for large-scale electronic structure calculations are mostly based on processing multi-dimensional sparse data. Examples are sparse matrix-matrix multiplications in linear-scaling Kohn-Sham calculations or the efficient determination of the exact exchange energy. When going beyond mean field approaches, e.g. for Moller-Plesset perturbation theory, RPA and Coupled-Cluster methods, or the GW methods, it becomes necessary to manipulate higher-order sparse tensors. Very similar problems are also encountered in other domains, like signal processing, data mining, computer vision, and machine learning. With the idea that the most of the tensor operations can be mapped to matrices, we have implemented sparse tensor algebra functionalities in the frames of the sparse matrix linear algebra library DBCSR (Distributed Block Compressed Sparse Row). DBCSR has been specifically designed to efficiently perform blocked-sparse matrix operations, so it becomes natural to extend its functionality to include tensor operations. We describe the newly developed tensor interface and algorithms. In particular, we introduce the tensor contraction based on a fast rectangular sparse matrix multiplication algorithm.
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