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10 results for “square lattice”
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>
Lattice subpolygons of a square with given sidelength
<p>This record contains all lattice subpolygons of the square of sidelength \(m\) for \(1 \leq m \leq 10\). There is one file for each sidelength. We use the <a href="https://www.hdfgroup.org/solutions/hdf5/" target="_blank" rel="noopener">HDF5</a> file format to store the polygons. The polygons have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl" target="_blank" rel="noopener">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files</h3> <p>In each file, the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the subpolygons of the square of sidelength \(4\) with normalized area \(20\) having \(6\) vertices are located in "m4.h5" under the dataset "/a20/n6". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((x_1,y_1,x_2,y_2,x_3,y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p>
Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1−J2 Heisenberg Model Data
<p>This repository contains raw data for the article "Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1-J2 Heisenberg Model", Olivier Gauthé and Frédéric Mila, 2023.</p><p>Raw data is provided as json files into the archive data_PEPS_ferroJ1-J2/ subdirectory.zip. The file "data_mswt_ferroJ1-J2.json" contains modified spin wave theory data.</p><p><br>The jupyter notebook "plot_ferroJ1-J2.ipynb" provides scripts to load and visualize data, as well as reproducing figures from the paper.<br>It can be executed using<br>python version 3.9.17<br>numpy version 1.24.3<br>scipy version 1.10.1</p><p>All the data was generated using finite temperature PEPS. Refer to the paper for a complete methodological discussion. The source code to produce PEPS data is available upon reasonable request.</p><p>Olivier Gauthé<br>October 2023</p>
Figure data to "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice"
<p>This collection contains the data of the figures shown in the publication "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice" as txt files.</p> <p>The CST datasets "Fig1_Gap_CST.txt", "Fig1_Energy_CST.txt" and "Fig3_CST.txt" are already published in https://doi.org/10.5281/zenodo.7528316 and included here for the sake of completeness.</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>
Raw data from the pCUT+MC approach for the antiferromagnetic Heisenberg square lattice bilayer model with (non-frustrating) long-range interactions
<p>This directory contains the data from the pCUT+MC approach for the antiferromagnetic Heisenberg square lattice bilayer model with (non-frustrating) long-range interactions.</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.md file.</p> <p>The data is published in P. Adelhardt, J. A. Koziol, A. Langheld, and K. P. Schmidt, "Monte Carlo based techniques for quantum magnets with long-range interactions", <a href="https://arxiv.org/abs/2403.00421">arXiv:2403.00421</a></p>
Measured data for a paper titled "Room temperature superconductivity in the giant square lattice, confirmed by a third party"
<p>These are the experimental data used in Fig. 2(a) and Fig. 3 of the paper titled "Room temperature superconductivity in the giant square lattice, confirmed by a third party." Both sets of data were obtained by Quantum Design Japan Inc. The date of the commissioned measurements is October 11, 2023.</p>
Phase diagram of dipolar-coupled XY moments on disordered square lattices
<p>Open access data set for manuscript "Phase diagram of dipolar-coupled XY moments on disordered square lattices" published in Physical Review B (2018).</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square lattice on a (t,U) 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>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>greens 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>inverse temperature 10.0</li> <li>U 0.0 to 2.7 in steps of 0.1</li> <li>t from 1.0 to 1.48 in steps of 0.02</li> </ul> <p>All simulations are done for half filling.</p> <p>The data are used in the publication "Thermodynamics of the metal-insulator transition in the extended Hubbard model" available on the arXiv (arXiv:1903.09947). There it is used to do an extrapolation of finite size and finite trotter errors and calculate the free energy by integrating the double occupation.</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>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
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