substeps=3, # MC steps taken w/o computing E_local samplesperthread=40 # No. of dmc walkers per thread ), dmc( # dmc parameters timestep=0.01, # dmc timestep (1/Ha) warmupsteps=50, # No. of MC steps before data is collected blocks=400, # No. of data blocks recorded in scalar.dat steps=5, # No. of steps per block nonlocalmoves=True # use Casula's T-moves ), # (retains variational principle for NLPP's) ], # return a list or object containing simulations return_list=False) #the project manager monitors all runs pm = ProjectManager() # give it the simulation objects pm.add_simulations(qsims.list()) # run all the simulations pm.run_project() # print out the total energy performed_runs = not settings.generate_only and not settings.status_only if performed_runs: # get the qmcpack analyzer object # it contains all of the statistically analyzed data from the run qa = qsims.qmc.load_analyzer_image() # get the local energy from dmc.dat le = qa.dmc[1].dmc.LocalEnergy # dmc series 1, dmc.dat, local energy
conv_thr=1e-6, # convergence threshold kgrid=kgrid, # supercell k-point grid kshift=(1, 1, 1), # grid centered at supercell L point pseudos=['Ge.pbe-kjpaw.UPF'], # PBE pseudopotential system=T_system # the interstitial system ) # link together the simulation cascade # current relax gets structure from previous if len(relaxations) > 0: # if it exists relax.depends(relaxations[-1], 'structure') #end if relaxations.append(relax) # add relax simulation to the list #end for # perform the simulations pm = ProjectManager() # start the project manager pm.add_simulations(relaxations) # give it the relax calculations pm.run_project() # run all the jobs # analyze the results performed_runs = not settings.generate_only and not settings.status_only if performed_runs: print print 'Relaxation results:' print '-------------------' print ' kgrid starting force max force # of cycles' for ik in range(len(supercell_kgrids)): kgrid = supercell_kgrids[ik] relax = relaxations[ik]