mirror of
https://github.com/triqs/dft_tools
synced 2024-06-02 11:25:29 +02:00
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
from pytriqs.applications.dft.sumk_lda_tools import *
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from pytriqs.applications.dft.converters.wien2k_converter import *
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from pytriqs.applications.impurity_solvers.hubbard_I.solver import Solver
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# Creates the data directory, cd into it:
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#Prepare_Run_Directory(DirectoryName = "Ce-Gamma")
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LDAFilename = 'Ce-gamma'
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Beta = 40
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Uint = 6.00
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JHund = 0.70
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DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
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load_previous = True # load previous results
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useBlocs = False # use bloc structure from LDA input
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useMatrix = True # use the U matrix calculated from Slater coefficients instead of (U+2J, U, U-J)
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ommin=-4.0
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ommax=6.0
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N_om=2001
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broadening = 0.02
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HDFfilename = LDAFilename+'.h5'
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# Convert DMFT input:
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# Can be commented after the first run
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Converter = Wien2kConverter(filename=LDAFilename,repacking=True)
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Converter.convert_dmft_input()
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Converter.convert_par_proj_input()
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#check if there are previous runs:
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previous_runs = 0
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previous_present = False
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if mpi.is_master_node():
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ar = HDFArchive(HDFfilename,'a')
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if 'iterations' in ar:
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previous_present = True
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previous_runs = ar['iterations']
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else:
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previous_runs = 0
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previous_present = False
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del ar
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mpi.barrier()
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previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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# if previous runs are present, no need for recalculating the bloc structure
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# It has to be commented, if you run this script for the first time, starting
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# from a converted h5 archive.
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# Init the SumK class
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SK = SumkLDATools(hdf_file=LDAFilename+'.h5',use_lda_blocks=False)
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if (mpi.is_master_node()):
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print 'DC after reading SK: ',SK.dc_imp[SK.invshellmap[0]]
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N = SK.corr_shells[0][3]
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l = SK.corr_shells[0][2]
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# Init the Solver:
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S = Solver(Beta = Beta, Uint = Uint, JHund = JHund, l = l)
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S.Nmoments=10
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# set atomic levels:
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eal = SK.eff_atomic_levels()[0]
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S.set_atomic_levels( eal = eal )
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S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om)
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S.Sigma.save('S.Sigma')
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SK.put_Sigma(Sigmaimp = [S.Sigma])
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SK.dos_partial(broadening=broadening)
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