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17 results for “nanomagnets”
Dataset for the publication "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory"
<p>Raw data associated to the manuscript ‘’Reversal of nanomagnets by propagating<br> magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory‘’, Nature Communications (2023); doi: <a href="https://deref-web.de/mail/client/P1XojCfdiYA/dereferrer/?redirectUrl=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-023-37078-8">https://doi.org/10.1038/s41467-023-37078-8</a><br> Information about file formats and measurement parameters are described in text files in the specific folders. For micromagnetic simulations Mumax 3.10 was used. The simulation scripts (*.mx3 files) and exemplary plotting scripts in Python 3.9 (*.py files) are included.</p> <p>Paper abstract:<br> Despite the unprecedented downscaling of CMOS integrated circuits, memory-intensive machine learning and artificial intelligence applications are limited by data conversion between memory and processor. There is a challenging quest for novel approaches to overcome this so-called von Neumann bottleneck. Magnons are the quanta of spin waves. Their angular momentum enables power-efficient computation without charge flow. The conversion problem would be solved if spin wave amplitudes could be stored directly in a<br> magnetic memory. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate in an underlying spin-wave bus. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that the spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level. Combined with the already existing wave logic, our discovery is path-breaking for the new era of magnonics-based in-memory computation and beyond von Neumann computer architectures</p>
Entropy driven order in an array of nanomagnets
<p>Long-range ordering, while typically understood as a decrease in entropy, can also be driven by increasing system entropy in certain special cases. We demonstrate that artificial spin ice arrays of single-domain nanomagnets can be designed to produce entropy-driven order. We probe thermally active tetris artificial spin ice, known to have a zero point Pauli entropy, both experimentally and through simulations. We find two-dimensional magnetic ordering in one subset of the nanomagnet moments, which we demonstrate to be induced by disorder (i.e., increased entropy) in another subset of moments. Contrasting with other entropy-driven systems, the degrees of freedom in artificial spin ice are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This robust example, in which the system's interactions and ground state entropy are well-defined, significantly expands the experimental landscape for the study of entropy-driven ordering.</p>
Topological kinetic crossover in a nanomagnet array
<p>Common explanations of equilibrium thermodynamics depend critically on ergodicity, i.e., the ability to explore all available states, but ergodic kinetics can be constrained by a system's topology. We report an experimental study of a model nanomagnetic array, in which such constraints visibly affect the behavior. In this system, magnetic excitations are connected in thermally active one-dimensional strings whose motion can be imaged in real-time. At high temperatures, the kinetics include the merging, breaking, and reconnecting of strings, resulting in the system transitioning between topologically distinct configurations. Below a crossover temperature, however, the string motion is dominated by simple changes in length and shape. In this low-temperature regime, the system is energetically stable because of the inability to explore the full set of possible topological configurations. This kinetic crossover suggests a generalizable conception of topologically broken ergodicity and limited equilibration.</p>
Insights on the coupling between vibronically active molecular vibrations and lattice phonons in molecular nanomagnets
<p>Spin–lattice relaxation is a key open problem to understand the spin dynamics of single-molecule magnets and molecular spin qubits. While modelling the coupling between spin states and local vibrations allows to determine the more relevant molecular vibrations for spin relaxation, this is not sufficient to explain how energy is dissipated towards the thermal bath. Herein, we employ a simple and efficient model to examine the coupling of local vibrational modes with long-wavelength longitudinal and transverse phonons in the clock-like spin qubit [Ho(W<sub>5</sub>O<sub>18</sub>)<sub>2</sub>]<sup>9−</sup>. We find that in crystals of this polyoxometalate the vibrational mode previously found to be vibronically active at low temperature does not couple significantly to lattice phonons. This means that further intramolecular energy transfer <em>via</em> anharmonic vibrations is necessary for spin relaxation in this system. Finally, we discuss implications for the spin–phonon coupling of [Ho(W<sub>5</sub>O<sub>18</sub>)<sub>2</sub>]<sup>9−</sup> deposited on a MgO (001) substrate, offering a simple methodology that can be extrapolated to estimate the effects on spin relaxation of different surfaces, including 2D materials.</p>
Entropy driven order in an array of nanomagnets
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Topological kinetic crossover in a nanomagnet array
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Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network
<p>Source files used to produce the publication "Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network."</p>
Chirally Coupled Nanomagnets
<p>Open access data set for manuscript "Chirally Coupled Nanomagnets<br> Zhaochu Luo1,2*, Trong Phuong Dao1,3, Aleš Hrabec1,2,3, Jaianth Vijayakumar2, Armin Kleibert2, Manuel Baumgartner3, Eugenie Kirk1,2, Jizhai Cui1,2, Tatiana Savchenko2, Gunasheel Krishnaswamy3, Laura Heyderman1,2*, Pietro Gambardella3*."</p>
Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices
<p>The file ManscuriptDataFiles.7z contains RAW spectroscopy data, origin plot file, micromagnetic simulation MIF files at 100 mT used in the manuscript entitiled "Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices" that appeared in Physical Review B, <strong>98</strong>, 174408 (2018).</p> <p>Manuscript Abstract: We report angle-dependent spin-wave spectroscopy on aperiodic quasicrystalline magnetic lattices, i.e., Ammann, Penrose P2 and P3 lattices made of large arrays of interconnected Ni<sub>80</sub>Fe<sub>20</sub> nanobars. Spin-wave spectra obtained in the nearly saturated state contain distinct sets of resonances with characteristic angular dependencies for applied in-plane magnetic fields. Micromagnetic simulations allow us to attribute detected resonances to mode profiles with specific mirror symmetries. Spectra in the reversal regime show systematic emergence and disappearance of spin-wave modes indicating reprogrammable magnonic characteristics.</p>
Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets
<p>Transmission electron microscopy data and processing scripts related to the journal article "Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets": <a href="https://doi.org/10.1002/smll.201904738">https://doi.org/10.1002/smll.201904738</a></p> <p><strong>Data files</strong></p> <p>The data is contained within the 00N_....hdf5 files, which can be accessed using an HDF5 reader. Note that these datasets are very large, and trying to load one of them directly will most likely lead to your computer crashing.</p> <p>Loading the data in python with h5py:</p> <pre><code class="language-python">import h5py f = h5py.File('003_stripe1.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>Exploring the datasets lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: STEM-DPC processing</li> <li>l00N_...: lattice size processing</li> <li>s00N_...: rotation "simulations" to find the relation between the scan and detector rotation</li> </ul> <p>Several of the scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p>This will most likely take several hours to complete.</p>
Quantum coherent spin-electric control in a molecular nanomagnet at clock transitions. Open data set
<p>Data supporting the related publication.</p>
Modular nanomagnet design for spin qubits confined in a linear chain
<p>supporting data</p>
Nanomagnet shape effects on magnetic reversal in artificial spin ice
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Computational Logic with Square Rings of Nanomagnets
<p>Open access data set for manuscript " Computational Logic with Square Rings of Nanomagnets" published in Nanotechnology 2018.</p>
Electroless Deposition of Ni-Fe alloys on Scaffolds for 3D Nanomagnetism
<p>Open data for "Electroless Deposition of Ni-Fe alloys on Scaffolds for 3D Nanomagnetism ", published in Small (2020), <a href="https://doi.org/10.1002/smll.202004099">https://doi.org/10.1002/smll.202004099</a></p>
Counteracting dephasing in Molecular Nanomagnets by optimized qudit encodings. Open data set
<p>Data supporting the original figures 1, 2, 3, 4, 5, 7 of the related publication.</p>
Molecular nanomagnets with competing interactions as optimal units for qudit-based quantum computation. Open data set
<p>Data supporting the original figures 1, 3, 4, 5, 6 and 7 of the related publication.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.