diff --git a/doc/Ce-gamma.py b/doc/Ce-gamma.py
index 03c56b5c..a60a999e 100644
--- a/doc/Ce-gamma.py
+++ b/doc/Ce-gamma.py
@@ -130,7 +130,7 @@ for Iteration_Number in range(1,Loops+1):
 
 
         #Save stuff:
-        SK.save()
+        SK.save(['chemical_potential','dc_imp','dc_energ'])
         if (mpi.is_master_node()):
             print 'DC after solver: ',SK.dc_imp[SK.invshellmap[0]]
 
diff --git a/doc/DFTDMFTmain.rst b/doc/DFTDMFTmain.rst
index 738879db..4727a2ca 100644
--- a/doc/DFTDMFTmain.rst
+++ b/doc/DFTDMFTmain.rst
@@ -100,9 +100,9 @@ set up the loop over DMFT iterations and the self-consistency condition::
                            l=2,T=None, dim_reps=None, irep=None, n_cycles =10000,
                            length_cycle=200,n_warmup_cycles=1000)
 
-	  dm = S.G.density()                         # density matrix of the impurity problem  
+	  dm = S.G.density()                                                 # Density matrix of the impurity problem  
           SK.set_dc( dm, U_interact = U, J_hund = J, use_dc_formula = 0)     # Set the double counting term
-          SK.save()                                  # save everything to the hdf5 arxive
+          SK.save(['chemical_potential','dc_imp','dc_energ'])                # Save data in the hdf5 archive
 
 These basic steps are enough to set up the basic DMFT Loop. For a detailed description of the :class:`SumkDFT` routines,
 see the reference manual. After the self-consistency steps, the solution of the Anderson impurity problem is calculation by CTQMC. 
diff --git a/doc/advanced.rst b/doc/advanced.rst
index 9dc6eff2..10725193 100644
--- a/doc/advanced.rst
+++ b/doc/advanced.rst
@@ -144,7 +144,7 @@ previous section, with some additional refinement::
         SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
   
         #Save stuff:
-        SK.save()
+        SK.save(['chemical_potential','dc_imp','dc_energ'])
                                 
 This is all we need for the DFT+DMFT calculation. At the end, all results are stored in the hdf5 output file.
 
diff --git a/doc/selfcons.rst b/doc/selfcons.rst
index 6904f0c4..255399fb 100644
--- a/doc/selfcons.rst
+++ b/doc/selfcons.rst
@@ -36,7 +36,7 @@ Now we calculate the modified charge density::
   SK.put_Sigma(Sigma_imp = [ S.Sigma ])
   chemical_potential = SK.find_mu( precision = 0.000001 )
   dN, d = SK.calc_density_correction(filename = dft_filename+'.qdmft')
-  SK.save()
+  SK.save(['chemical_potential','dc_imp','dc_energ'])
 
 First we find the chemical potential with high precision, and after that the routine 
 ``SK.calc_density_correction(filename)`` calculates the density matrix including correlation effects. The result
diff --git a/lda_dmft_cthyb.py b/lda_dmft_cthyb.py
index 66dd5461..9ca48c0b 100644
--- a/lda_dmft_cthyb.py
+++ b/lda_dmft_cthyb.py
@@ -131,8 +131,8 @@ for iteration_number in range(1,loops+1):
       # Set the double counting
       SK.set_dc( dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
 
-      # Save stuff into the hdf5 archive:
-      SK.save()
+      # Save stuff into the dft_output group of hdf5 archive in case of rerun:
+      SK.save(['chemical_potential','dc_imp','dc_energ'])
 
 if mpi.is_master_node():
     ar = HDFArchive("dftdmft.h5",'w')
diff --git a/python/sumk_dft.py b/python/sumk_dft.py
index 92fff5f8..72e336f2 100644
--- a/python/sumk_dft.py
+++ b/python/sumk_dft.py
@@ -114,11 +114,6 @@ class SumkDFT:
             # Analyse the block structure and determine the smallest blocks, if desired
             if use_dft_blocks: dm = self.analyse_block_structure()
 
-            # Now save new things to HDF5: 
-            # FIXME WHAT HAPPENS TO h_field? INPUT TO __INIT__? ADD TO OPTIONAL_THINGS?
-            things_to_save = ['chemical_potential','dc_imp','dc_energ','h_field']
-            self.save(things_to_save)
-
 ################
 # HDF5 FUNCTIONS
 ################
@@ -171,7 +166,7 @@ class SumkDFT:
 
 
     def save(self,things_to_save):
-        """Saves some quantities into an HDF5 archive"""
+        """Saves given quantities into the 'dft_output' subgroup of the HDF5 archive"""
 
         if not (mpi.is_master_node()): return # do nothing on nodes
         ar = HDFArchive(self.hdf_file,'a')