Yambopy tutorial: band structures - Revision history

FulvioPaleari: /* Tutorial 2. Iron. Ferromagnetic metallic material */

2026-03-11T12:14:18Z

Tutorial 2. Iron. Ferromagnetic metallic material

← Older revision		Revision as of 12:14, 11 March 2026
Line 103:		Line 103:
	==Tutorial 2. Iron. Ferromagnetic metallic material==		==Tutorial 2. Iron. Ferromagnetic metallic material==

	In the case of spin-polarized calculations we can plot the spin up and down band structures. We have included in the tutorial a small workflow example to run quantum espresso calculations from scratch. This is done using the classes Tasks and Flows developed in yambopy. You can check the file flow-iron.py for an example.		In the case of spin-polarized calculations we can plot the spin up and down band structures.
	~~Yambopy includes basic predefined workflows to run the common QE and Yambo calculations. In this case we are using the class '''PwBandsTasks'''~~.

	~~python flow-~~iron~~.py~~		After running a band structure calculation for iron (the results are in the <code>bands</code> folder), we can load the data from the QE xml output files and make the band plot by running the script:

	~~In order to plot~~ the ~~spin-polarized~~ bands~~. After doing all the calculations from scratch with the flows (flow-iron.py~~), we can make the band plot by running the script:

	python plot-qe-bands.py		python plot-qe-bands.py

	The class PwXML automatically detects the spin polarized case (nspin=2 in the QE input file). The spin up channel is displayed with red and the spin down channel with blue. In the case of iron we have selected this k-point path:		The class <code>PwXML</code> automatically detects the spin polarized case (nspin=2 in the QE input file). The spin up channel is displayed with red and the spin down channel with blue. In the case of iron we have selected this k-point path:

	npoints = 50		npoints = 50
Line 130:		Line 127:
	Let us look first at the file '''plot-qe-orbitals-size'''. <br>		Let us look first at the file '''plot-qe-orbitals-size'''. <br>

	'''NB: If you generated ~~the quantum espresso databases using the flows~~ instead of relying on the precomputed databases, you ~~also need to rerun~~ the quantum espresso executable projwfc.x to ~~recompute~~ the orbital projections. In this case, please uncomment the following lines in the script''' (plot-qe-orbitals-size.py) :		'''NB: If you generated your own qe band structure instead of relying on the precomputed databases, you can run the quantum espresso executable projwfc.x to compute the orbital projections using yambopy. In this case, please uncomment the following lines in the script''' (plot-qe-orbitals-size.py) :
	#proj = ProjwfcIn(prefix='pw')		#proj = ProjwfcIn(prefix='pw')
	#proj.run(folder='bands~~/t0~~')		#proj.run(folder='bands')

	Now, we can use the dot size as a function of the weight of the orbitals		Now, we can use the dot size as a function of the weight of the orbitals
Line 141:		Line 138:

	and the plots are done with		and the plots are done with
	band = ProjwfcXML(prefix='pw',path='bands~~/t0~~',qe_version='7.0')		band = ProjwfcXML(prefix='pw',path='bands',qe_version='7.0')
	band.plot_eigen(ax,path_kpoints=path_kpoints,<span style="color:red">selected_orbitals=s,</span>color='pink',color_2='black')		band.plot_eigen(ax,path_kpoints=path_kpoints,<span style="color:red">selected_orbitals=s,</span>color='pink',color_2='black')
	band.plot_eigen(ax,path_kpoints=path_kpoints,<span style="color:red">selected_orbitals=p,</span>color='green',color_2='orange')		band.plot_eigen(ax,path_kpoints=path_kpoints,<span style="color:red">selected_orbitals=p,</span>color='green',color_2='orange')
Line 164:		Line 161:
	p = band.get_states_helper(orbital_query=['p'])		p = band.get_states_helper(orbital_query=['p'])
	d = band.get_states_helper(orbital_query=['d'])		d = band.get_states_helper(orbital_query=['d'])
	band = ProjwfcXML(prefix='pw',path='bands~~/t0~~',qe_version='7.0')		band = ProjwfcXML(prefix='pw',path='bands',qe_version='7.0')
	band.plot_eigen(ax,path_kpoints=path_kpoints,cmap='viridis',cmap2='rainbow',selected_orbitals=p,selected_orbitals_2=d)		band.plot_eigen(ax,path_kpoints=path_kpoints,cmap='viridis',cmap2='rainbow',selected_orbitals=p,selected_orbitals_2=d)

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T18:45:28Z

Tutorial 3: GW bands

← Older revision		Revision as of 18:45, 9 March 2026
Line 182:		Line 182:

	from yambopy import YamboLatticeDB,YamboQPDB # Load yambo netcdf databases		from yambopy import YamboLatticeDB,YamboQPDB # Load yambo netcdf databases
	from qepy import Path # Define path in k-space		from qepy import Path # Define path in k-space
	import numpy as np		import numpy as np
	import matplotlib.pyplot as plt		import matplotlib.pyplot as plt

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T18:44:51Z

Tutorial 3: GW bands

← Older revision		Revision as of 18:44, 9 March 2026
Line 181:		Line 181:
	See below for an explanation of the tutorial. As usual, we can import the qepy and yambopy libraries (in addition to the standard modules we will need):		See below for an explanation of the tutorial. As usual, we can import the qepy and yambopy libraries (in addition to the standard modules we will need):

	from ~~qepy~~ import ~~Path~~		from yambopy import YamboLatticeDB,YamboQPDB # Load yambo netcdf databases
	from ~~yambopy~~ import ~~YamboLatticeDB,YamboQPDB~~		from qepy import Path # Define path in k-space
	import numpy as np		import numpy as np
	import matplotlib.pyplot as plt		import matplotlib.pyplot as plt

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T18:43:37Z

Tutorial 3: GW bands

← Older revision		Revision as of 18:43, 9 March 2026
Line 179:		Line 179:
	python plot-qp.py		python plot-qp.py

	See below for an explanation of the tutorial. As usual, we can import the qepy and yambopy libraries:		See below for an explanation of the tutorial. As usual, we can import the qepy and yambopy libraries (in addition to the standard modules we will need):

	from qepy import *		from qepy import Path
	from yambopy import *		from yambopy import YamboLatticeDB,YamboQPDB
			import numpy as np
	import matplotlib.pyplot as plt		import matplotlib.pyplot as plt
			from math import sqrt

	We define the k-points path.		We define the k-points path.

FulvioPaleari: /* Tutorial 1. BN (semiconductor). Band structure */

2026-03-09T18:42:05Z

Tutorial 1. BN (semiconductor). Band structure

← Older revision		Revision as of 18:42, 9 March 2026
Line 16:		Line 16:

	from qepy import *		from qepy import *

			However, for this plot we just need two classes, namely:

			from qepy import Path,PwXML

	===Plot Band structure===		===Plot Band structure===

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T13:56:53Z

Tutorial 3: GW bands

← Older revision		Revision as of 13:56, 9 March 2026
Line 239:		Line 239:
	'''What to do if the interpolation does not seem to work well'''		'''What to do if the interpolation does not seem to work well'''

	- First of all, you can try to modify the '''lpratio''' parameter and see if it improves.		* First of all, you can try to modify the '''lpratio''' parameter and see if it improves.

	- If it does not, often you can obtain better results by just interpolating the '''differences''' between QP and DFT energies. This can be done by first subtracting the DFT value from the QP energies, then interpolate, and finally re-add the DFT interpolated values. You can also run a separate band structure calculation in DFT on a dense grid (so that you don't need to interpolate the DFT energies) and read it into your script either with <code>qepy</code> or <code>YamboElectronsDB</code> after creating a SAVE directory. If you have (i) a QP calculation and SAVE on the original grid and (ii) a DFT SAVE on a denser grid, you could do this directly via the <code>interpolate_QP_corrections</code> method in <code>YamboQPDB</code>.		* If it does not, often you can obtain better results by just interpolating the '''differences''' between QP and DFT energies. This can be done by first subtracting the DFT value from the QP energies, then interpolate, and finally re-add the DFT interpolated values. You can also run a separate band structure calculation in DFT on a dense grid (so that you don't need to interpolate the DFT energies) and read it into your script either with <code>qepy</code> or <code>YamboElectronsDB</code> after creating a SAVE directory. If you have (i) a QP calculation and SAVE on the original grid and (ii) a DFT SAVE on a denser grid, you could do this directly via the <code>interpolate_QP_corrections</code> method in <code>YamboQPDB</code>.

	- If this also does not improve anything, consider that the quality of the interpolation improves the denser your actual calculated kmesh is. Thus, as a last resort, you can try to run an '''additional''' GW calculation on a '''shifted k-point grid'''. For example, you shift it by (0.5,0.5,0.5) in fractional coordinates. Yambo is able to calculate the self-energy corrections on this grid without recalculating the screening. Now, you have effectively doubled the k-density of your QP corrections. In this case, you have to do a bit of yambopy scripting yourself to merge the two databases and obtain the band plot: this is an advanced application but you can do everything with the tools yambopy makes available.		* If this also does not improve anything, consider that the quality of the interpolation improves the denser your actual calculated kmesh is. Thus, as a last resort, you can try to run an '''additional''' GW calculation on a '''shifted k-point grid'''. For example, you shift it by (0.5,0.5,0.5) in fractional coordinates. Yambo is able to calculate the self-energy corrections on this grid without recalculating the screening. Now, you have effectively doubled the k-density of your QP corrections. In this case, you have to do a bit of yambopy scripting yourself to merge the two databases and obtain the band plot: this is an advanced application but you can do everything with the tools yambopy makes available.

	==Links==		==Links==
	* Back to [[Rome 2023#Tutorials]]		* Back to [[Rome 2023#Tutorials]]
	* Back to [[ICTP 2022#Tutorials]]		* Back to [[ICTP 2022#Tutorials]]

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T13:56:06Z

Tutorial 3: GW bands

← Older revision		Revision as of 13:56, 9 March 2026
Line 240:		Line 240:

	- First of all, you can try to modify the '''lpratio''' parameter and see if it improves.		- First of all, you can try to modify the '''lpratio''' parameter and see if it improves.

	- If it does not, often you can obtain better results by just interpolating the '''differences''' between QP and DFT energies. This can be done by first subtracting the DFT value from the QP energies, then interpolate, and finally re-add the DFT interpolated values. You can also run a separate band structure calculation in DFT on a dense grid (so that you don't need to interpolate the DFT energies) and read it into your script either with <code>qepy</code> or <code>YamboElectronsDB</code> after creating a SAVE directory. If you have (i) a QP calculation and SAVE on the original grid and (ii) a DFT SAVE on a denser grid, you could do this directly via the <code>interpolate_QP_corrections</code> method in <code>YamboQPDB</code>.		- If it does not, often you can obtain better results by just interpolating the '''differences''' between QP and DFT energies. This can be done by first subtracting the DFT value from the QP energies, then interpolate, and finally re-add the DFT interpolated values. You can also run a separate band structure calculation in DFT on a dense grid (so that you don't need to interpolate the DFT energies) and read it into your script either with <code>qepy</code> or <code>YamboElectronsDB</code> after creating a SAVE directory. If you have (i) a QP calculation and SAVE on the original grid and (ii) a DFT SAVE on a denser grid, you could do this directly via the <code>interpolate_QP_corrections</code> method in <code>YamboQPDB</code>.

	- If this also does not improve anything, consider that the quality of the interpolation improves the denser your actual calculated kmesh is. Thus, as a last resort, you can try to run an '''additional''' GW calculation on a '''shifted k-point grid'''. For example, you shift it by (0.5,0.5,0.5) in fractional coordinates. Yambo is able to calculate the self-energy corrections on this grid without recalculating the screening. Now, you have effectively doubled the k-density of your QP corrections. In this case, you have to do a bit of yambopy scripting yourself to merge the two databases and obtain the band plot: this is an advanced application but you can do everything with the tools yambopy makes available.		- If this also does not improve anything, consider that the quality of the interpolation improves the denser your actual calculated kmesh is. Thus, as a last resort, you can try to run an '''additional''' GW calculation on a '''shifted k-point grid'''. For example, you shift it by (0.5,0.5,0.5) in fractional coordinates. Yambo is able to calculate the self-energy corrections on this grid without recalculating the screening. Now, you have effectively doubled the k-density of your QP corrections. In this case, you have to do a bit of yambopy scripting yourself to merge the two databases and obtain the band plot: this is an advanced application but you can do everything with the tools yambopy makes available.

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T13:55:48Z

Tutorial 3: GW bands

← Older revision		Revision as of 13:55, 9 March 2026
Line 237:		Line 237:
	As always, remember that you can do your own script, the one provided is just an example.		As always, remember that you can do your own script, the one provided is just an example.

	'''		'''What to do if the interpolation does not seem to work well'''
	What to do if the interpolation does not seem to work well
	'''		- First of all, you can try to modify the '''lpratio''' parameter and see if it improves.
	- First of all, you can try to modify the '''lpratio''' parameter.		- If it does not, often you can obtain better results by just interpolating the '''differences''' between QP and DFT energies. This can be done by first subtracting the DFT value from the QP energies, then interpolate, and finally re-add the DFT interpolated values. You can also run a separate band structure calculation in DFT on a dense grid (so that you don't need to interpolate the DFT energies) and read it into your script either with <code>qepy</code> or <code>YamboElectronsDB</code> after creating a SAVE directory. If you have (i) a QP calculation and SAVE on the original grid and (ii) a DFT SAVE on a denser grid, you could do this directly via the <code>interpolate_QP_corrections</code> method in <code>YamboQPDB</code>.
			- If this also does not improve anything, consider that the quality of the interpolation improves the denser your actual calculated kmesh is. Thus, as a last resort, you can try to run an '''additional''' GW calculation on a '''shifted k-point grid'''. For example, you shift it by (0.5,0.5,0.5) in fractional coordinates. Yambo is able to calculate the self-energy corrections on this grid without recalculating the screening. Now, you have effectively doubled the k-density of your QP corrections. In this case, you have to do a bit of yambopy scripting yourself to merge the two databases and obtain the band plot: this is an advanced application but you can do everything with the tools yambopy makes available.

	==Links==		==Links==
	* Back to [[Rome 2023#Tutorials]]		* Back to [[Rome 2023#Tutorials]]
	* Back to [[ICTP 2022#Tutorials]]		* Back to [[ICTP 2022#Tutorials]]

FulvioPaleari: /* Tutorial 3: GW bands */

2026-03-09T12:24:12Z

Tutorial 3: GW bands

← Older revision		Revision as of 12:24, 9 March 2026
Line 227:		Line 227:
	qp_bs.plot_ax(ax,legend=True,c_bands='b',label='QP-GW')		qp_bs.plot_ax(ax,legend=True,c_bands='b',label='QP-GW')

	The '''lpratio''' can be increased if the interpolation does not work as well as intended. The SKW interpolation scheme is the same on implemented in ~~abipy~~.		The '''lpratio''' can be increased if the interpolation does not work as well as intended. The SKW interpolation scheme is the same on implemented in Abipy.

	[[File:Figure 8-GW-band-structure-interpolated.png\|400px\|center]]		[[File:Figure 8-GW-band-structure-interpolated.png\|400px\|center]]
Line 234:		Line 234:

	[[File:Figure 8-GW-band-structure-comparison.png\|400px\|center]]		[[File:Figure 8-GW-band-structure-comparison.png\|400px\|center]]

			As always, remember that you can do your own script, the one provided is just an example.

			'''
			What to do if the interpolation does not seem to work well
			'''
			- First of all, you can try to modify the '''lpratio''' parameter.

	==Links==		==Links==
	* Back to [[Rome 2023#Tutorials]]		* Back to [[Rome 2023#Tutorials]]
	* Back to [[ICTP 2022#Tutorials]]		* Back to [[ICTP 2022#Tutorials]]

FulvioPaleari: /* Tutorial 3: GW bands */

2025-06-20T16:33:20Z

Tutorial 3: GW bands

← Older revision		Revision as of 16:33, 20 June 2025
Line 191:		Line 191:

	# Read Lattice information from SAVE		# Read Lattice information from SAVE
	lat = ~~YamboSaveDB~~.from_db_file(~~folder~~='SAVE~~',filename='~~ns.db1')		## Note: we do not expand the kpts because QP database is in the IBZ
			lat = YamboLatticeDB.from_db_file(filename='SAVE/ns.db1',Expand=False)
	# Read QP database		# Read QP database
	ydb = YamboQPDB.from_db(filename='ndb.QP',folder='qp-gw')		ydb = YamboQPDB.from_db(filename='ndb.QP',folder='qp-gw')
	~~(in the yambopy module, each class is specialised to read a specific Yambo database)~~

	The first option is to plot the energies and calculate the ideal Kohn-Sham to GW scissor operator. We need to select the index of the top valence band:		The first option is to plot the energies and calculate the ideal Kohn-Sham to GW scissor operator. We need to select the index of the top valence band: