ru-ccmt / eDMFT

Haule's Repository for combination of Density Functional Theory and Embedded Dynamical Mean Field Theory implementation (Python3 version)
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DMFT installation instructions:

  1. From provided configuration files create your own "configure.py". You can start by creating a link to one of the provided files, i.e.,

    ln -s configure.py.intel configure.py and if necessary edit the file by specifying precise compiler names, and if needed for compilation, their paths.

    If you are going to use intel fortran and intel C++ compiler, you should use "configure.py.intel". If you have gnu compilers, you should choose "configure.py.gnu". You could also mix the two if desired.

  2. Execute `setup.py' and you should be guided through installation process. Please make sure that you have /usr/bin/time installed.

  3. Learn how to use the code by reading "http://hauleweb.rutgers.edu/tutorials/"

    Please consider citing the basic DFT+DMFT algorithm: [1] Kristjan Haule, Chuck-Hou Yee, Kyoo Kim, Phys. Rev. B 81, 195107 (2010).

    and DFT part, which is based on Wien2k code [2] P. Blaha, K. Schwarz, G. Madsen, D. Kvasnicka and J. Luitz, WIEN2k, An Augmented Plane Wave + Local Orbitals Program for Calculating Crystal Properties (Karlheinz Schwarz, Techn. Universität Wien, Austria), 2001. ISBN 3-9501031-1-2

    Consider citing, if you use some of these items Continuous time Quantum Monte Carlo: [3] Kristjan Haule, Phys. Rev. B 75, 155113 (2007).

    Free energy implementation : [4] Kristjan Haule, Turan Birol, Phys. Rev. Lett. 115, 256402 (2015).

    Forces and structural optimization : [5] Kristjan Haule, Gheorghe L. Pascut, arXiv:1602.02819.

The code is developed by support from National Science Foundation : NSF-DMR 1405303 and NSF DMR-0746395 and by support of A. P. Sloan Foundation, Simons foundation, and Blavatnik foundation.

The documentation at "http://hauleweb.rutgers.edu/tutorials/" is developed by the support of DOE.

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