GW tutorial. Convergence and approximations (BN) - Revision history

Fulvio: /* Plotting GW calculations. Scissor operator and GW band structure */

2022-03-31T14:19:14Z

Plotting GW calculations. Scissor operator and GW band structure

← Older revision		Revision as of 14:19, 31 March 2022
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	doing:		doing:

	~~<source lang="bash">~~		fig = plt.figure(figsize=(6,4))
	fig = plt.figure(figsize=(6,4))		ax = fig.add_axes( [ 0.20, 0.20, 0.70, 0.70 ])
	ax = fig.add_axes( [ 0.20, 0.20, 0.70, 0.70 ])
	~~</source>~~

	The file is structured as follows:		The file is structured as follows:
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	'''A. Define a path'''. Using qepy we can define a path to plot the band structure.		'''A. Define a path'''. Using qepy we can define a path to plot the band structure.

	~~<source lang="bash">~~		npoints = 10
	npoints = 10		path = Path([ [[ 0.0, 0.0, 0.0],'$\Gamma$'],
	path = Path([ [[ 0.0, 0.0, 0.0],'$\Gamma$'],		[[ 0.5, 0.0, 0.0],'M'],
	[[ 0.5, 0.0, 0.0],'M'],		[[1./3.,1./3., 0.0],'K'],
	[[1./3.,1./3., 0.0],'K'],		[[ 0.0, 0.0, 0.0],'$\Gamma$']], [int(npoints2),int(npoints),int(sqrt(5)npoints)] )
	[[ 0.0, 0.0, 0.0],'$\Gamma$']], [int(npoints2),int(npoints),int(sqrt(5)npoints)] )
	~~</source>~~

	'''B. Read Yambo lattice and QP databases.'''		'''B. Read Yambo lattice and QP databases.'''

	~~<source lang="bash">~~		lat = YamboSaveDB.from_db_file(folder='gw/SAVE',filename='ns.db1')
	lat = YamboSaveDB.from_db_file(folder='gw/SAVE',filename='ns.db1')		ydb = YamboQPDB.from_db(filename='ndb.QP',folder='gw/yambo')
	ydb = YamboQPDB.from_db(filename='ndb.QP',folder='gw/yambo')
	~~</source>~~

	'''C. Plot all QP eigenvalues.''' A very typical plot when analyzing GW calculations is the plot of the GW eigenvalues versus LDA eigenvalues. You just need to indicate which band index corresponds to the		'''C. Plot all QP eigenvalues.''' A very typical plot when analyzing GW calculations is the plot of the GW eigenvalues versus LDA eigenvalues. You just need to indicate which band index corresponds to the
	top of the valence band.		top of the valence band.

	~~<source lang="bash">~~		n_top_vb = 4
	n_top_vb = 4		ydb.plot_scissor_ax(ax,n_top_vb)
	ydb.plot_scissor_ax(ax,n_top_vb)
	~~</source>~~

	[[File:Scissor-gw.png\|GW results (dots) and linear fitting (solid lines) \|300px]]		[[File:Scissor-gw.png\|GW results (dots) and linear fitting (solid lines) \|300px]]
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	will find which k-points belong to a given path. We can add the LDA results for comparison.		will find which k-points belong to a given path. We can add the LDA results for comparison.

	~~<source lang="bash">~~		ks_bs_0, qp_bs_0 = ydb.get_bs_path(lat,path)
	ks_bs_0, qp_bs_0 = ydb.get_bs_path(lat,path)		ks_bs_0.plot_ax(ax,legend=True,color_bands='r',label='KS')
	ks_bs_0.plot_ax(ax,legend=True,color_bands='r',label='KS')		qp_bs_0.plot_ax(ax,legend=True,color_bands='b',label='QP-GW')
	qp_bs_0.plot_ax(ax,legend=True,color_bands='b',label='QP-GW')
	~~</source>~~

	[[File:Bands-LDA-GW.png\|GW and LDA band structures \|300px]]		[[File:Bands-LDA-GW.png\|GW and LDA band structures \|300px]]

Fulvio: /* Approximations of the dielectric function (COHSEX, PPA) */

2022-03-31T14:17:55Z

Approximations of the dielectric function (COHSEX, PPA)

← Older revision		Revision as of 14:17, 31 March 2022
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	We can use yambopy to examine different run levels. For instance, the approximations used to obtain the screening are the: (i) static screening or COHSEX, (ii) plasmon-pole approximations (PPA), or (iii) real axis integration. We have set the same parameters for the first two options, just changing the variable name for the number of bands and the cut-off of the screening.		We can use yambopy to examine different run levels. For instance, the approximations used to obtain the screening are the: (i) static screening or COHSEX, (ii) plasmon-pole approximations (PPA), or (iii) real axis integration. We have set the same parameters for the first two options, just changing the variable name for the number of bands and the cut-off of the screening.

	~~<source lang="bash">~~		COHSEX
	COHSEX		EXXRLvcs = 60 Ry
	EXXRLvcs = 60 Ry		BndsRnXs = 40 bands
	BndsRnXs = 40 bands		NGsBlkXs = 3 Ry
	NGsBlkXs = 3 Ry
			PPA
	PPA		EXXRLvcs = 60 Ry
	EXXRLvcs = 60 Ry		BndsRnXp = 40 bands
	BndsRnXp = 40 bands		NGsBlkXp = 3 Ry
	NGsBlkXp = 3 Ry
	~~</source>~~

	We have set the converged parameters and the function works by running:		We have set the converged parameters and the function works by running:

	~~<source lang="bash">~~python gw_conv_bn.py -x~~</source>~~		python gw_conv_bn.py -x

	We plot the band structure using the analyzer explained above.		We plot the band structure using the analyzer explained above.

	~~<source lang="bash">~~python gw_conv_bn.py -xp~~</source>~~		python gw_conv_bn.py -xp

	We observe a difference between COHSEX and PPA.		We observe a difference between COHSEX and PPA.

Fulvio: /* GW calculation in regular k-grid */

2022-03-31T14:17:02Z

GW calculation in regular k-grid

← Older revision		Revision as of 14:17, 31 March 2022
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	the accuracy of the convergence. In this tutorial, in order to make calculations lighter, we have chosen the following parameters:		the accuracy of the convergence. In this tutorial, in order to make calculations lighter, we have chosen the following parameters:

	~~<source lang="bash">~~		EXXRLvcs = 60 Ry
	EXXRLvcs = 60 Ry		BndsRnXp = 40 bands
	BndsRnXp = 40 bands		NGsBlkXp = 3 Ry
	NGsBlkXp = 3 Ry		GbndRnge = 30 bands
	GbndRnge = 30 bands		QPkrange = [1,19,2,6]
	QPkrange = [1,19,2,6]
	~~</source>~~

	We can change the <code>gs_bn.py</code> scripts to calculate a non self-consistent run for a larger '''k-grid''' (12x12x1 will do the job). We can also change the number of bands to 40 in order to speed up a bit the QE calculation.		We can change the <code>gs_bn.py</code> scripts to calculate a non self-consistent run for a larger '''k-grid''' (12x12x1 will do the job). We can also change the number of bands to 40 in order to speed up a bit the QE calculation.
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	We can just simply run the code to calculate the GW corrections for all the points of the Brillouin zone by setting the converged parameters in the script <code>gw_bn.py</code>. If we enter in the script we can check that we define a scheduler (the default is bash):		We can just simply run the code to calculate the GW corrections for all the points of the Brillouin zone by setting the converged parameters in the script <code>gw_bn.py</code>. If we enter in the script we can check that we define a scheduler (the default is bash):

	~~<source lang="bash">~~		scheduler = Scheduler.factory
	scheduler = Scheduler.factory
	~~</source>~~

	We just need to define the run level and the variables we are going to ~~chang~~:		We just need to define the run level and the variables we are going to change:

			y = YamboIn.from_runlevel('%s -p p -g n -V all'%yambo,folder='gw')

			y['EXXRLvcs'] = [60,'Ry'] # Self-energy. Exchange
			y['BndsRnXp'] = [1,40] # Screening. Number of bands
			y['NGsBlkXp'] = [3,'Ry'] # Cutoff Screening
			y['GbndRnge'] = [1,30] # Self-energy. Number of bands
			y['QPkrange'] = [kpoint_start,kpoint_end,2,6]

	~~<source lang="bash">~~
	~~y = YamboIn.from_runlevel('%s -p p -g n -V all'%yambo,folder='gw')~~

	~~y['EXXRLvcs'] = [60,'Ry'] # Self-energy. Exchange~~
	~~y['BndsRnXp'] = [1,40] # Screening. Number of bands~~
	~~y['NGsBlkXp'] = [3,'Ry'] # Cutoff Screening~~
	~~y['GbndRnge'] = [1,30] # Self-energy. Number of bands~~
	~~y['QPkrange'] = [kpoint_start,kpoint_end,2,6]~~
	~~</source>~~

	We can now run the script and obtain the GW in the full '''k-grid'''.		We can now run the script and obtain the GW in the full '''k-grid'''.

	~~<source lang="bash">~~		python gw_bn.py
	python gw_bn.py
	~~</source>~~

	If everything has worked fine now we will have inside the folder <code>gw/yambo</code> the netCDF file <code>ndb.QP</code> with the results of the GW calculation. We can analyze the results and/or use them in BSE calculations.		If everything has worked fine now we will have inside the folder <code>gw/yambo</code> the netCDF file <code>ndb.QP</code> with the results of the GW calculation. We can analyze the results and/or use them in BSE calculations.

Fulvio: /* GW convergence of the bandgap */

2022-03-31T14:15:49Z

GW convergence of the bandgap

← Older revision		Revision as of 14:15, 31 March 2022
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	We can select the GW calculation by calling the <code>YamboIn</code> with the corresponding arguments:		We can select the GW calculation by calling the <code>YamboIn</code> with the corresponding arguments:

	~~<source lang="python">~~python gw_conv_gw.py -c ~~</source>~~		python gw_conv_gw.py -c

	~~<source lang="python">~~y = YamboIn.from_runlevel('yambo -d -p p -g n -V all',folder='gw_conv')~~</source>~~		y = YamboIn.from_runlevel('yambo -d -p p -g n -V all',folder='gw_conv')

	The main variables of a GW calculation are:		The main variables of a GW calculation are:
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	We define a dictionary with all the parameters that we want to converge. Be aware of setting the right units and format for each parameter.		We define a dictionary with all the parameters that we want to converge. Be aware of setting the right units and format for each parameter.

	~~<source lang="python">~~conv = { 'EXXRLvcs': [[10,10,20,30,40,50,60,70,80,90,100],'Ry'],		conv = { 'EXXRLvcs': [[10,10,20,30,40,50,60,70,80,90,100],'Ry'],
	'NGsBlkXp': [[0,0,1,2,3,4,5,6], 'Ry'],		'NGsBlkXp': [[0,0,1,2,3,4,5,6], 'Ry'],
	'BndsRnXp': [[[1,10],[1,10],[1,20],[1,30],[1,40],[1,50],[1,60],[1,70]],''] ,		'BndsRnXp': [[[1,10],[1,10],[1,20],[1,30],[1,40],[1,50],[1,60],[1,70]],''] ,
	'GbndRnge': [[[1,10],[1,10],[1,20],[1,30],[1,40],[1,50],[1,60],[1,70]],''] }~~</source>~~		'GbndRnge': [[[1,10],[1,10],[1,20],[1,30],[1,40],[1,50],[1,60],[1,70]],''] }

	The class <code>YamboIn</code> includes the function <code>optimize</code>, which is called here:		The class <code>YamboIn</code> includes the function <code>optimize</code>, which is called here:

	~~<source lang="python">~~		y.optimize(conv,folder='gw_conv',run=run,ref_run=False)
	y.optimize(conv,folder='gw_conv',run=run,ref_run=False)
	~~</source>~~

	The function optimize has two main arguments: the convergence dictionary, and a function '''run''' with the instructions to run		The function <code>optimize</code> has two main arguments: the convergence dictionary, and a function '''run''' with the instructions to run
	the calculation, defined in our case like:		the calculation, defined in our case like:

	~~<source lang="python">~~
	def run(filename):		def run(filename):
	""" Function to be called by the optimize function """		""" Function to be called by the optimize function """
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	shell.run()		shell.run()
	shell.clean()		shell.clean()
	~~</source>~~

	We have defined interactive run, in the folder <code>gw_conv</code>. We have also defined the name for each job, associated with the variable and its value.		We have defined interactive run, in the folder <code>gw_conv</code>. We have also defined the name for each job, associated with the variable and its value.
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	the python module, <code>yambopy</code> can also be called in the terminal to perform some post-analysis tasks:		the python module, <code>yambopy</code> can also be called in the terminal to perform some post-analysis tasks:

	~~<source lang="python">~~
	$ yambopy		$ yambopy
	analysebse -> Using ypp, you can study the convergence of BSE calculations in 2 ways:		analysebse -> Using ypp, you can study the convergence of BSE calculations in 2 ways:
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	test -> Run yambopy tests		test -> Run yambopy tests
	plotexcitons -> Plot excitons calculation		plotexcitons -> Plot excitons calculation
	~~</source>~~

	Calling <code>yambopy analysegw</code> will display the help of the function:		Calling <code>yambopy analysegw</code> will display the help of the function:

	~~<source lang="python">~~

	Study the convergence of GW calculations by looking at the change in bandgap value.		Study the convergence of GW calculations by looking at the change in bandgap value.

	The script reads from <folder> all results from <variable> calculations and display them.		The script reads from <folder> all results from <variable> calculations and display them.

	Use the band and k-point options according to the size of your k-grid		Use the band and k-point options according to the size of your k-grid
	and the location of the band extrema.		and the location of the band extrema.

	Mandatory arguments are:		Mandatory arguments are:
	folder -> Folder containing SAVE and convergence runs.		folder -> Folder containing SAVE and convergence runs.
	var -> Variable tested (e.g. FFTGvecs)		var -> Variable tested (e.g. FFTGvecs)

	Optional variables are:		Optional variables are:
	-bc, --bandc (int) -> Lowest conduction band number		-bc, --bandc (int) -> Lowest conduction band number
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	-nd, --nodraw (flag) -> Do not draw (plot) the result		-nd, --nodraw (flag) -> Do not draw (plot) the result

	~~</source>~~

	Running the function selecting the bands and k-points, together with the parameter of convergence we will obtain the convergence plot.		Running the function selecting the bands and k-points, together with the parameter of convergence we will obtain the convergence plot.

	~~<source lang="python">~~		yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv EXXRLvcs
	yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv EXXRLvcs		yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv NGsBlkXp
	yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv NGsBlkXp		yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv BndsRnXp
	yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv BndsRnXp		yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv GbndRnge
	yambopy analysegw -bc 5 -kc 7 -bv 4 -kv 7 gw_conv GbndRnge
	~~</source>~~

	From the convergence plots, we can choose the set of converged parameters and repeat the calculation for finer k-grids until we reach convergence with the k-points. We have		From the convergence plots, we can choose the set of converged parameters and repeat the calculation for finer k-grids until we reach convergence with the k-points. We have

Fulvio: /* Initial steps with Quantum Espresso */

2022-03-31T14:09:08Z

Initial steps with Quantum Espresso

← Older revision		Revision as of 14:09, 31 March 2022
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	The initial step consists of a self-consistent (scf) ground state calculation together with a non-self-consistent (nscf) calculation with QE using the <code>gs_bn.py</code> file:		The initial step consists of a self-consistent (scf) ground state calculation together with a non-self-consistent (nscf) calculation with QE using the <code>gs_bn.py</code> file:

	~~<source lang="bash">~~python gs_bn.py		python gs_bn.py
	python gs_bn.py -sn~~</source>~~		python gs_bn.py -sn

	Here you will be using a predefined lattice parameter for the calculations. You can, however, run a geometry optimisation calculation with		Here you will be using a predefined lattice parameter for the calculations. You can, however, run a geometry optimisation calculation with

	~~<source lang="bash">~~python gs_bn.py -rsn~~</source>~~		python gs_bn.py -rsn

	which at the end will update the input files for <code>pw.x</code> with the relaxed lattice parameters and atomic coordinates.		which at the end will update the input files for <code>pw.x</code> with the relaxed lattice parameters and atomic coordinates.
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	A quick note: almost all python scripts in this tutorial can be run without any options, but they will only print out the help message. In the case of <code>gs_bn.py</code> it will print:		A quick note: almost all python scripts in this tutorial can be run without any options, but they will only print out the help message. In the case of <code>gs_bn.py</code> it will print:

	~~<source lang="bash">~~python gs_bn.py		python gs_bn.py
	~~usage: gs_bn.py [-h] [-r] [-s] [-n] [-n2] [-b] [-l] [-o] [-p] [-d]~~
	~~[-t NTHREADS]~~

	Test the yambopy script.		usage: gs_bn.py [-h] [-r] [-s] [-n] [-n2] [-b] [-l] [-o] [-p] [-d] [-t NTHREADS]
			Test the yambopy script.
	optional arguments:		optional arguments:
	-h, --help show this help message and exit		-h, --help show this help message and exit
	-r, --relax Structural relaxation		-r, --relax Structural relaxation
	-s, --scf Self-consistent calculation		-s, --scf Self-consistent calculation
	-n, --nscf Non-self consistent calculation		-n, --nscf Non-self consistent calculation
	-n2, --nscf_double Non-self consistent calculation for the double grid		-n2, --nscf_double Non-self consistent calculation for the double grid
	-b, --bands Calculate band-structure		-b, --bands Calculate band-structure
	-l, --plot Plot band-structure		-l, --plot Plot band-structure
	-o, --orbitals Plot atomic orbital projected band-structure		-o, --orbitals Plot atomic orbital projected band-structure
	-p, --phonon Phonon calculation		-p, --phonon Phonon calculation
	-d, --dispersion Phonon dispersion		-d, --dispersion Phonon dispersion
	-t NTHREADS, --nthreads NTHREADS		-t NTHREADS, --nthreads NTHREADS
	Number of threads		Number of threads
	~~</source>~~

	We have set the non-self-consistent run with a wave-function cutoff of '''60 Ry, 70 bands and a k-grid of <code>12x12x1</code>''' for Yambo calculations.		We have set the non-self-consistent run with a wave-function cutoff of '''60 Ry, 70 bands and a k-grid of <code>12x12x1</code>''' for Yambo calculations.
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	In addition, we can check if we get a reasonable band structure for our many-body calculations. We can define a path, for instance, in an hexagonal Brillouin zone:		In addition, we can check if we get a reasonable band structure for our many-body calculations. We can define a path, for instance, in an hexagonal Brillouin zone:

	~~<source lang="bash">~~
	p = Path([ [[0.0, 0.0, 0.0],'$\Gamma$'],		p = Path([ [[0.0, 0.0, 0.0],'$\Gamma$'],
	[[0.5, 0.0, 0.0],'M'],		[[0.5, 0.0, 0.0],'M'],
	[[1./3,1./3,0.0],'K'],		[[1./3,1./3,0.0],'K'],
	[[0.0, 0.0, 0.0],'$\Gamma$']], [int(102),int(10),int(sqrt(5)10)])		[[0.0, 0.0, 0.0],'$\Gamma$']], [int(102),int(10),int(sqrt(5)10)])
	~~</source>~~

	We obtain the band structure by doing:		We obtain the band structure by doing:

	~~<source lang="bash">~~python gs_bn.py -b~~</source>~~		python gs_bn.py -b

	[[File:Plot-bn-band.png\|BN Band Structure\|500px]]		[[File:Plot-bn-band.png\|BN Band Structure\|500px]]

Alejandro at 08:45, 31 January 2020

2020-01-31T08:45:39Z

← Older revision		Revision as of 08:45, 31 January 2020
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	We can select the GW calculation by calling the <code>YamboIn</code> with the corresponding arguments:		We can select the GW calculation by calling the <code>YamboIn</code> with the corresponding arguments:

			<source lang="python">python gw_conv_gw.py -c </source>

	<source lang="python">y = YamboIn.from_runlevel('yambo -d -p p -g n -V all',folder='gw_conv')</source>		<source lang="python">y = YamboIn.from_runlevel('yambo -d -p p -g n -V all',folder='gw_conv')</source>

Pedro at 23:30, 30 January 2020

2020-01-30T23:30:41Z

← Older revision		Revision as of 23:30, 30 January 2020
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	<source lang="bash">python gw_conv_bn.py -xp</source>		<source lang="bash">python gw_conv_bn.py -xp</source>

	We observe a ~~large~~ difference between COHSEX and PPA.		We observe a difference between COHSEX and PPA.

	[[File:Gw-chi-s.png\|GW bands calculated with the COHSEX and PPA\|300px]]		[[File:Gw-chi-s.png\|GW bands calculated with the COHSEX and PPA\|300px]]

			You are welcome to refine the calculations using better parameters and check how this difference changes. In the next tutorial, [[Bethe-Salpeter equation tutorial. Optical absorption (BN)]], you can use the scissor operator computed in section [[#GW_calculation_in_regular_k-grid\|GW calculation in regular k-grid]].

Pedro at 23:08, 30 January 2020

2020-01-30T23:08:49Z

@@ Line 10: / Line 10: @@
 <source lang="bash">python gs_bn.py
 python gs_bn.py -sn</source>
 We have set the non-self-consistent run with a wave-function cutoff of '''60 Ry, 70 bands and a k-grid of <code>12x12x1</code>''' for Yambo calculations.

Alejandro: /* Plotting GW calculations. Scissor operator and GW band structure */

2020-01-23T16:46:47Z

Plotting GW calculations. Scissor operator and GW band structure

← Older revision		Revision as of 16:46, 23 January 2020
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	</source>		</source>

	[[File:~~bands~~-~~not~~-~~interpolated~~.png\|GW and LDA band structures \|300px]]		[[File:Bands-LDA-GW.png\|GW and LDA band structures \|300px]]

	'''E. Plot interpolated QP-GW eigenvalues in a path.''' In order to obtain results ready for publication or presentation, we can interpolate the GW calculations.		'''E. Plot interpolated QP-GW eigenvalues in a path.''' In order to obtain results ready for publication or presentation, we can interpolate the GW calculations.

	[[File:~~bands~~-interpolated.png\| Interpolated GW and LDA band structures \|300px]]		[[File:Bands-LDA-GW-interpolated.png\| Interpolated GW and LDA band structures \|300px]]

	'''F. Comparison non-interpolated and interpolated results.''' It is convenient to check how good is the interpolation of GW results.		'''F. Comparison non-interpolated and interpolated results.''' It is convenient to check how good is the interpolation of GW results.

	[[File:~~check~~-~~interpolation~~.png\| Exact and interpolated GW and LDA band structures \|300px]]		[[File:Lda-gw-comparison.png\| Exact and interpolated GW and LDA band structures \|300px]]

	=== GW convergence for all k-points ===		=== GW convergence for all k-points ===

Alejandro: /* Approximations of the dielectric function (COHSEX, PPA) */

2020-01-23T15:42:05Z

Approximations of the dielectric function (COHSEX, PPA)

← Older revision		Revision as of 15:42, 23 January 2020
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	We observe a large difference between COHSEX and PPA.		We observe a large difference between COHSEX and PPA.

	[[File:GW-~~COHSEX~~-~~PPA~~.png\|GW bands calculated with the COHSEX and PPA\|300px]]		[[File:Gw-chi-s.png\|GW bands calculated with the COHSEX and PPA\|300px]]