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2 results for “honeycomb 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>
Data files for S. Malkaruge Costa et al., "Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger model and its relevance to graphene"
<p>Data files for the paper <em>Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger</em><br><em>model and its relevance to graphene </em>by Sohan Malkaruge Costa, Benjamin Cohen-Stead, and Steven Johnston. </p> <p>Reference: S. Malkaruge Costa, B. Cohen-Stead , and S. Johnston, Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger<br>model and its relevance to graphene. Physical Review B <strong>110</strong>, 115130 (2024). </p> <p>https://journals.aps.org/prb/pdf/10.1103/PhysRevB.110.115130</p> <p>Preprint available at: https://arxiv.org/abs/2407.09366</p> <p>This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.</p> <p> </p>
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