GW tutorial. Convergence and approximations (BN)
We chosen hexagonal boron nitride to explain the use of yambopy. Along this tutorial we show how to use yambopy to make efficient convergence tests, to compare different approximations and to analyze the results.
The initial step is the ground state calculation and the non self-consistent calculation using the gs_bn.py file:
<source lang="bash">python gs_bn.py
python gs_bn.py -sn</source>
We have set 50 bands and the k-grid 12x12x1.
1. GW convergence
(a) Calculations
We converge the main parameters of a GW calculation independently. We make use of the plasmon pole approximation for the dielectric function and the newton solver to find the GW correction to the LDA eigenvalues. The magnitude to converge is the band gap of the BN (conduction and valence band at the K point of the Brillouin zone). We can select this calculation by calling the YamboIn with the right arguments:
<source lang="python">y = YamboIn('yambo -d -g n -p p -V all',folder='gw_conv')</source> The main variables are:
- FFTGvecs: Global cutoff
- BndsRnXp: Number of bands in the calculation of the dielectric function (PPA).
- NGsBlkXp: Cutoff of the dielectric function.
- GbndRnge: Self-energy. Number of bands.
The convergence with the k-grid is done after these variables are converged and in principle is also independent of them. The convergence is set with a dictionary in which we choose the parameter and the values. Be aware of setting the right units and format for each parameter.
<source lang="python">conv = { 'FFTGvecs': [[2,5,10,15,20],'Ha'],
'NGsBlkXp': [[0,500,1000,1500,2000], 'mHa'],
'BndsRnXp': [[[1,5],[1,10],[1,20],[1,30],[1,40],[1,50]],] ,
'GbndRnge': [[[1,5],[1,10],[1,20],[1,30],[1,40],[1,50]],] }</source>
The class YamboIn includes the function optimize, which is call here:
<source lang="python">y.optimize(conv,run=run,ref_run=False)</source> This optimization function just need the convergence dictionary and the run instructions, given by the function:
<source lang="python">def run(filename):
""" Function to be called by the optimize function """
folder = filename.split('.')[0]
print(filename,folder)
shell = bash()
shell.add_command('cd gw_conv; %s -F %s -J %s -C %s 2> %s.log'%(yambo,filename,folder,folder,folder))
shell.run()
shell.clean()</source>
We set an interactive run, in the folder gw_conv. All the calculations will be made there with the corresponding jobname.
(b) Analysis
Once all the calculations are finished it's time to pack all the files in the json format for posterior analysis. For this use the YamboOut() class:
<source lang="python">#pack the files in .json files for dirpath,dirnames,filenames in os.walk('gw_conv'):
#check if there are some output files in the folder
if ([ f for f in filenames if 'o-' in f ]):
y = YamboOut(dirpath,save_folder=dirpath)
y.pack()</source>
This snippet of code can be called using the function:
<source lang="python">pack_files_in_folder('gw_conv',save_folder='gw_conv')</source>
Yambopy provides the function analyse_gw.py to perform the analysis of the json files in an automatic way. By running the script selecting the bands and kpoints, together with the parameter we will obtain the convergence plot.
<source lang="python">python analyse_gw.py -bc 5 -kc 19 -bv 4 -kv 19 gw_conv FFTGvecs</source> image
From the convergence plot we can choose now a set of parameters and repeat the calculation for finer k-grids until we reach convergence with the k-points. The convergence criteria are left to the user.