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mVMC

  • 公開度：3 ★★★
  • ドキュメント充実度：2 ★★☆

A low-energy solver for a wide ranger of quantum lattice models (multi-orbital Hubbard model, Heisenberg model, Kondo-lattice model) by using variational Monte Carlo method. User can obtain high-accuracy wave functions for ground states of above models. Users flexibly choose the correlation factors in wavefunctions such as Gutzwiller, Jastrow, and doublon-holon binding factors and optimize more the ten thousand variational parameters. It is also possible to obtain the low-energy excited states by specifying the quantum number using the quantum number projection.

基本情報
Last Update:2024/06/14

公式サイト

http://www.pasums.issp.u-tokyo.ac.jp/mvmc/en/

ライセンス

GNU General Public License version 3

We hope that you cite the following paper and repository when you publish the results using mVMC.

paper: https://www.sciencedirect.com/science/article/pii/S0010465518303102

repository: https://github.com/issp-center-dev/mVMC

利用環境

Linux,Mac
開発者

Takahiro Misawa (Institute for Solid State Physics ,The University of Tokyo), Satoshi Morita (Institute for Solid State Physics ,The University of Tokyo) Takahiro Ohgoe (Department of Applied Physics ,The University of Tokyo), Kota Ido (Department of Applied Physics ,The University of Tokyo), Masatoshi Imada (Department of Applied Physics ,The University of Tokyo), Kazuyoshi Yoshimi (Institute for Solid State Physics ,The University of Tokyo), Mituaki Kawamura (Institute for Solid State Physics ,The University of Tokyo), Takeo Kato (Institute for Solid State Physics ,The University of Tokyo)
並列化対応

OpenMP, MPI, and hybrid parallelization are supported. Massively parallelized simulation on the K-computer has been reported.
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References
その他
  • By using mVMC, it is possible to perform the high-accuracy calculations for ground states in a wide range of the quantum lattice models. In addition to the conventional orders such as magnetic and/or charge orders, user can treat the anisotropic superconductivities within the same framework. This flexibility is the main advantage of mVMC.
  • 2023 HPCI Software Award【Development Category】Best Award  https://www.hpci-c.jp/hrdevelop/award.html (in Japanese)
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