Newer Python3 code is now available at https://github.com/ru-ccmt/eDMFT

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 "https://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 "https://hauleweb.rutgers.edu/tutorials/" is developed by the 
support of DOE.

New in 2024:
- cif2struct.py added, which converts cif files to struct as needed by wien2k.
 This code takes care of monoclinic structures in which cif2struct in wien code
 fails, and converts any cif setting into wien accepted setting (for example CXZ,gamma!=90)
- added run_dft.py, which is similar to run_lapw, but uses internal lapw1, and lapwso, 
  which are faster than wien/lapw[1|so] on parallel machines.