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40 results for “Hubbard model”
Hubbard Brook Experimental Forest: 1 meter LiDAR-derived and Hydro-enforced Digital Elevation Models, 2012
This data package contains a 1 m LiDAR-derived digital elevation model (DEM) and a 1 m hydro-enforced DEM across Hubbard Brook EF. The LiDAR was collected during leaf-off and snow-free conditions by Photo Science, Inc. in April 2012 for the White Mountain National Forest (WMNF). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Modelled root zone storage capacities Hubbard Brook WS5
Modelled root zone storage capacities for the Hubbard Brook Experimental Forest - Watershed 5. Root zone storage capacities were derived based on a simple water balance based model (Nijzink et al. 2016). Long term equilibrium root zone storage capacities yearly root zone storage capacities were determined.
Modelled root zone storage capacities Hubbard Brook
Modelled root zone storage capacities for the Hubbard Brook Forest - Watershed 2. Root zone storage capacities were derived based on a simple water balance based model (Nijzink et al. 2016). Long term equilibrium root zone storage capacities yearly root zone storage capacities were determined.
Dynamical mean field theory data for single band Hubbard model
<p>Dynamical mean field theory (DMFT) data for the half filled repulsive single band Hubbard model on four lattices: cubic, diamond, hypercubic in <span class="math-tex">\(d=\infty\)</span>, and hyperdiamond in <span class="math-tex">\(d=\infty\)</span>.</p> <p>It is allowed for long range antiferromagnetic ordering, i.e., the self-consistency condition as seen in Eq. 97 of Georges et al., Rev. Mod. Phys. 68, 13 is used.</p> <p>The simulations are done for different temperatures between <span class="math-tex">\(\beta t = 3\)</span> and <span class="math-tex">\(\beta t = 40\)</span>.</p> <p>The interaction <span class="math-tex">\(U\)</span> is chosen in steps of 0.1 centered around the respective Mott transitions.</p> <p>Available data are</p> <ul> <li>interacting Greens function on Matsubara frequencies</li> <li>self energy on Matsubara frequencies</li> <li>double occupation</li> <li>spin up and spin down occupation</li> </ul> <p>Data is generated using triqs 1.4 and the continous time quantum Monte Carlo application 1.4, compare homepage at https://triqs.ipht.cnrs.fr</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to calculate derivatives of the double occupancy w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p> </p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square and honeycomb lattice.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>Data for the honeycomb lattice calculated for</p> <ul> <li>lattice sizes 6x6, 9x9, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>All energies are in units of the hopping parameters which is set to t=1. All simulations are done for half filling.</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to do an extrapolation of finite size and finite trotter errors and finally calculate derivatives of charge correlation functions w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>This version (v2) includes the number of bins used in each simulation and a slighlty changed python script to read the data.</p>
Model Results Hubbard Brook WS2
Model results for four hydrological models (FLEX, HYMOD, TUW and HYPE) for Hubbard Brook WS2
Model Results Hubbard Brook WS5
Model results for four hydrological models (FLEX, HYMOD, TUW and HYPE) for Hubbard Brook WS5.
Supporting data for "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array"
<p>Supporting data and analysis scripts for Fig. 3b of "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array", ArXiv:1702.07511 (preprint) and 10.1038/nature23022 (publication)</p> <p>This dataset contains a readme file as well as three zipped folders that contain (1) raw data sets of all relevant measurements, as well as (2) matlab files to plot fitted data and the extracted parameters and (3) the code that uses the extracted parameters to plot the fan diagram.</p>
Dataset: Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model
<p>Dataset</p> <p>Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model</p> <p>Ryui Kaneko, Ippei Danshita</p>
Data for "Antiferromagnetic phase transition in a 3D fermionic Hubbard model"
<p>This dataset is for research article "Antiferromagnetic phase transition in a 3D fermionic Hubbard model".</p>
Data for Superconductivity in the Hubbard model and its interplay with next-nearest hopping t'
<p>The results for <a href="https://arxiv.org/abs/1806.01465">https://arxiv.org/abs/1806.01465</a></p> <p>"Superconductivity in the Hubbard model and its interplay with next-nearest hopping t'",</p> <p>including both manuscript and supplemental material.</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the half filled square lattice, on a (U,B)-grid
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>The simulations are done at half filling on a square lattice, with the following parameters:</p> <ul> <li>Lattice sizes: 4x4, 6x6, 8x8, 10x10, 12x12, periodic boundary conditions</li> <li>Trotter discretizations: 0.1 and 0.2</li> <li>Inverse temperature beta = 10.0</li> <li>48 values for the on-site interaction U from 0.0 to 10.0</li> <li>48 values for the magnetic field (in z-direction) B from 0.0 to 4.0</li> <li>10000 warmup sweeps, 30000 measurement sweeps</li> </ul> <p>The following data from equal time measurements are available:</p> <ul> <li>Charge-Charge Correlation (next neighbors)</li> <li>Greens Function (n.n.)</li> <li>Magnetization</li> <li>Double Occupancy</li> <li>Kinetic Energy</li> <li>Total Energy</li> <li>Spin-Spin Correlation (n.n.)</li> <li>Spin-Spin Correlation (only ZZ) (n.n.)</li> <li>Ferromagnetic Structure Factor (ZZ)</li> <li>Antiferromagnetic Structure Factor (ZZ)</li> </ul> <p>The data are available as a hdf5 archive. The python script 'extract.py' illustrates the access with h5py. Relevant QUEST input parameters are provided in the group 'parameters' within the archive.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Dataset to run the NeuralFRG software for the t-t' Hubbard model on the square lattice "Phys. Rev. Lett. 129, 136402 (2022)"
<p>This hdf5 repository contains the fRG vertices for the t-t' Hubbard model on the square lattice required to reproduce the results shown in the publication</p> <p>Di Sante et al., Phys. Rev. Lett. 129, 136402 (2022)</p> <p>by means of the NeuralFRG software (https://github.com/BITMAPdds/NeuralFRG).</p> <p>A train-test split can be performed with:</p> <p>python3 train_validation_split.py NeuralFRG_train_and_validation_data.h5 --verbose</p> <p>and training can be started with:</p> <p>python3 train.py path/to/file_train.h5 (...) #Additional flags here</p>
Applicability and limiations of Cluster Perturbation Theory for Hubbard models
<p>These are the Cluster Greensfunctions that were used in the paper "Applicability and limiations of Cluster<br> Perturbation Theory for Hubbard models" published as part of the special edition “S.I.: Non-Equilibrium Quantum<br> Physics, Many Body Systems, and Foundations of Quantum Mechanics” in the European Journal of Phyiscs in 2023.<br> The Greensfunctions were generated via a Chebyshev expansion and are currently in a real space representation.<br> You may use python and import them via numpy as follows:</p> <p>```console<br> import numpy as np</p> <p>MC = <number_of_sites> # Here you have to add the number of cluster sites (e.g. 16 for a 4x4 cluster)</p> <p>greensfunctions = np.genfromtxt("<file_name>")<br> greensfunctions = greensfunctions.reshape(greensfunctions.shape[0], MC, MC)<br> ```</p> <p>This way you obtain a tensor where the first dimension corresponds to the frequency and the other two<br> to the real space indices.</p> <p>For further questions please contact the corresponding author Nicklas Enenkel via E-mail<br> (nicklas.enenkel@quantumsimulations.de)</p>
Nitrification and denitrification in the Community Land Model compared to observations at Hubbard Brook Forest
Open the record for dataset details and reuse information.
Benchmarking a wide range of optimisers for solving the Fermi-Hubbard model using the variational quantum eigensolver
<p>Data used in the paper "Benchmarking a wide range of optimisers for solving the Fermi-Hubbard model using the variational quantum eigensolver" by Benjamin D.M. Jones, Lana Mineh, and Ashley Montanaro, available here: <a href="https://arxiv.org/abs/2411.13742" target="_blank" rel="noopener">https://arxiv.org/abs/2411.13742</a>.</p> <p>See the <code>README.md</code> file for details on how the data is organised, and <code>data.zip</code> for the full data (~37GB when uncompressed).</p>
Thermodynamics of the metal-insulator transition in the extended Hubbard model from determinantal quantum Monte Carlo
<p>This zip archive contains the DQMC data sets for the \beta=1/T resolved double<br> occupancy, internal energy per site, antiferromagnetic structure factor and<br> charge density wave structure factor obtained with the ALF Code.<br> The computations have been carried out on a square lattice at half filling for<br> the Hubbard model, the U-V model and the long-range Coulomb(LRC)-Hubbard model.<br> Every model is computed at fixed U/t=1.9.</p> <p>The naming convention of the csv-files is the following:<br> V0.0 = Hubbard model<br> V0.x = U-V model with V/t=0.x<br> Vcx.x = LRC-Hubbard model with V_C/t=x.x<br> <br> - dat_docc_{\Delta\tau}_V{x}.csv = double occupancy at \Delta\tau={0.05, 0.1, 0.2}<br> - dat_docc_{\Delta\tau}_V{x}_err.csv = corresponding statistical error<br> - dat_energy_0.1_V{x}.csv = internal energy at \Delta\tau=0.1<br> - dat_energy_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_saf_0.1_V{x}.csv = antiferromagnetic structure factor at \Delta\tau=0.1<br> - dat_saf_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_scdw_0.1_V{x}.csv = charge density wave structure factor at \Delta\tau=0.1<br> - dat_scdw_0.1_V{x}_err.csv = corresponding statistical error</p> <p>The structure of each file is to be read column-wise:<br> \beta, L=8, L=10, L=12, L=16, L=18, L=20</p> <p> </p>
Data for Nguyen Le at al. ""Topological phases of a dimerized Fermi-Hubbard model for semiconductor nano-lattices"
<p>Codes and simulation data used in Nguyen Le at al. "“Topological phases of a dimerized Fermi-Hubbard model for semiconductor nano-lattices."</p>
Strange metallicity in the doped Hubbard model
<p>Data and code for main and supplementary figures of the paper "Strange metallicity in the doped Hubbard model".</p> <p>preprint: <a href="https://arxiv.org/abs/1806.08346">https://arxiv.org/abs/1806.08346</a></p>
Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model
<p>Aggregated numerical data and analysis routines required to reproduce the figures in "Particle-hole asymmetric ferromagnetism and spin textures in the triangular Hubbard-Hofstadter model"</p>
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