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16 results for “quantum Monte Carlo”
Quantum Monte Carlo data for 4He inside Ar-Preplated MCM-41 Nanopores
<p>Raw quantum Monte Carlo data for argon pre-plated MCM-41 nanopores with L = 50 Å.</p> <p>Details of the simulations are published here: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.102.144505</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>
TREXIO files used for the validation tests in the paper entitled 'TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods'.
<p>The TREXIO files used for the validation tests in the paper entitled TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The detail about the TREXIO library is described in the JCP article [J. Chem. Phys. 158, 174801 (2023)] and the GitHub repository [https://github.com/TREX-CoE/trexio]. The TREXIO files were generated using TREXIO version 2.3.2 (and the corresponding Python API version 1.3.2).</p>
Data for "Quantum Monte Carlo studies of a trimer scaling function with microscopic two- and three-body interactions"
<p>Data for "Quantum Monte Carlo studies of a trimer scaling function with microscopic two- and three-body interactions", Phys. Rev. A 104, 033301 (2021).</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>
Data for: Quantum Monte Carlo study of the role of p-wave interactions in ultracold repulsive Fermi gases
<p>Dataset to reproduce the figures of the article</p> <p>G. Bertaina, M. G. Tarallo, and S. Pilati, <em>Quantum Monte Carlo Study of the Role of $p$-Wave Interactions in Ultracold Repulsive Fermi Gases</em>, Phys. Rev. A <strong>107</strong>, 053305 (2023).</p> <p>https://link.aps.org/doi/10.1103/PhysRevA.107.053305</p> <p>https://arxiv.org/abs/2212.09150</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>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019,
<p>SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019. This compliments the data set at doi: 10.17863/CAM.30358 (copy these SPAWN files here into relevant folder at histograms_toc_figs_1_2/H2O_3_ccpVDZ/).</p>
QMC Raw Data for Stable Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble
<p><strong>Data Summary</strong></p> <p>Raw data of 'A Stable, Recursive Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble: Applications to Thermometry and the Hubbard Model'.</p> <p>Random seeds are generated using the default Julia RNG, with the seed number being 1234+the file ID.</p> <p>For more details, please check README.md on the GitHub repository.</p> <p> </p> <p><strong>Fidelity_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for fidelity measurements that correspond to plot_fidelity.ipynb</li> <li>The random seeds for numerator measurements ('Fidelity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>Purity_(CE|GCE)_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for purity measurements that correspond to plot_purity.ipynb</li> <li>The random seeds for numerator measurements ('Purity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>StructFactGCE_Lx6_Ly6_U2.0.zip</strong></p> <ul> <li>Raw data for the momentum distribution and structural factor measurements that correspond to plot_nk.ipynb and plot_Cq.ipynb</li> </ul>
Data for "A Phaseless Auxiliary-Field Quantum Monte Carlo Perspective on the UniformElectron Gas at Finite Temperatures: Issues, Observations, and Benchmark Study"
<p>Phaseless AFQMC data (input and output) for "A Phaseless Auxiliary-Field Quantum Monte Carlo Perspective on the UniformElectron Gas at Finite Temperatures: Issues, Observations, and Benchmark Study" </p> <p> </p> <p>data: contains raw qmc data</p> <p>figures: contains analysed data + plotting scripts.</p>
Understanding and Improving the Efficiency of Full Configuration Interaction Quantum Monte Carlo
<p>Data and Python Scripts required to produce the figures in:</p> <p>Understanding and Improving the Efficiency of Full Configuration Interaction Quantum Monte Carlo</p> <p>http://arxiv.org/abs/1601.00865</p> <p>Reanaylisis requires pyhande (part of the HANDE package) available from:</p> <p>https://github.com/hande-qmc/hande.git</p> <p>To reproduce the figures by reanalysing the data from scratch modify sys.path.append() in ./bin/Efficiency.py and in ./figure4/figure4.py. To point to hande_top_level_dir/tools.</p> <p> </p>
Data for Unbiasing fermionic quantum Monte Carlo with a quantum computer
<p>Wavefunctions and Hamiltonians from "Unbiasing fermionic quantum Monte Carlo with a quantum computer"</p>
Data repository for "Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions"
<p>This Zenodo deposit provides a comprehensive collection of raw data, processed data, and scripts associated with the research paper available at <a href="https://arxiv.org/abs/2306.15186" target="_new">https://arxiv.org/abs/2306.15186</a>.</p> <p>Contents:</p> <p>- data.zip includes all the QMCpack inputs and outputs, which can be used in the blocking analysis to obtain the AFQMC energy. Processed Data "SI for DP-MSD-AFQMC.xlsx" contains the AFQMC results after the blocking analysis in XLSX format. "SI for DP-MSD-AFQMC.xlsx" also includes the energies from all other methods presented in the paper.</p> <p>- scripts.zip contains all the Python scripts used in running calculations for the paper. This includes scripts to generate LAS-AFQMC and CAS-AFQMC trials (under scripts/utils) in QMCpack format, as well as inputs for each compound.</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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