import numpy as np
import pickle
import banduppy
# supercell : 4X4X2 supercell == np.diag([4,4,2]) or
super_cell_size = [[-1, 1, 1], [1, -1, 1], [1, 1, -1]]
# k-path: L-G-X-U,K-G. If the segmant is skipped, put a None between nodes.
PC_BZ_path = [[1/2,1/2,1/2], [0,0,0],[1/2,0,1/2], [5/8,1/4,5/8], None, [3/8,3/8,3/4], [0,0,0]]
# Number of k-points in each path segments. or, one single number if they are same.
npoints_per_path_seg = (23,27,9,29)
# Labels of special k-points: list or string. e.g ['L','G','X','U','K','G'] or 'LGXUKG'
special_k_points = "LGXUKG"
# Weights of the k-points to be appended in the final generated k-points files
kpts_weights = 1
# Save the SC kpoints in a file
save_to_file = True
# Directory to save file
save_to_dir = '<directory to save files>'
# File format of kpoints file that will be created and saved
kpts_file_format = 'vasp' # This will generate vasp KPOINTS file format
1. Estimate the best choice of number of kpoints to use in effective band structure each k-path segments considering maximizing or minimizing folding in the supercell.
Definition:
band_unfold = banduppy.Unfolding(supercell=super_cell_size,
print_log='high')
propose_folding_results = \
band_unfold.propose_maximum_minimum_folding(PC_BZ_path, min_num_pts=10, max_num_pts=50,
serach_mode='brute_force', draw_plots=True,
save_plot=False, save_dir='.',
save_file_name=None)
Note: band_unfold.generate_SC_Kpts_from_pc_kpts() can be used to generate SC Kpoints from PC kpoints list.
kpointsPBZ_full, kpointsPBZ_unique, kpointsSBZ, \
SBZ_PBZ_kpts_mapping, special_kpoints_pos_labels \
= band_unfold.generate_SC_Kpts_from_pc_k_path(pathPBZ = PC_BZ_path,
nk = npoints_per_path_seg,
labels = special_k_points,
kpts_weights = kpts_weights,
save_all_kpts = save_to_file,
save_sc_kpts = save_to_file,
save_dir = save_to_dir,
file_name_suffix = '',
file_format=kpts_file_format)
read_dir = '<path where the vasp output files are>'
bands = banduppy.BandStructure(code="vasp", spinor=False,
fPOS = f"{read_dir}/POSCAR",
fWAV = f"{read_dir}/WAVECAR")
# save2file : Save unfolded kpoints or not?
# fdir : Directory path where to save the file.
# fname : Name of the file.
# fname_suffix : Suffix to add to the file name.
Option 1: Continue with previous instance.
unfolded_bandstructure_, kpline \
= band_unfold.Unfold(bands, kline_discontinuity_threshold = 0.1,
save_unfolded_kpts = {'save2file': True,
'fdir': save_to_dir,
'fname': 'kpoints_unfolded',
'fname_suffix': ''},
save_unfolded_bandstr = {'save2file': True,
'fdir': save_to_dir,
'fname': 'bandstructure_unfolded',
'fname_suffix': ''})
Option 2: If this part is used independently from the above instances re-initiate the Unfolding module.
# --------------------- Initiate Unfolding method --------------------------
band_unfold = banduppy.Unfolding(supercell=super_cell_size, print_info='high')
# ----------------- Unfold the band structures ------------------------------
unfolded_bandstructure_, kpline \
= band_unfold.Unfold(bands, PBZ_kpts_list_full=kpointsPBZ_full,
SBZ_kpts_list=kpointsSBZ,
SBZ_PBZ_kpts_map=SBZ_PBZ_kpts_mapping,
kline_discontinuity_threshold = 0.1,
save_unfolded_kpts = {'save2file': True,
'fdir': save_to_dir,
'fname': 'kpoints_unfolded',
'fname_suffix': ''},
save_unfolded_bandstr = {'save2file': True,
'fdir': save_to_dir,
'fname': 'bandstructure_unfolded',
'fname_suffix': ''})
Band ceneters are determined using the SCF algorithm of automatic band center determination from Paulo V. C. Medeiros, Sven Stafström, and Jonas Björk, Phys. Rev. B 89, 041407(R) (2014) paper.
# -------------------- Initiate Properties method -----------------------------
unfolded_band_properties = banduppy.Properties(print_log='high')
#===================================
min_dN = 1e-5 # get rid of small weights bands
threshold_dN_2b_trial_band_center = 0.05 # initial guess of the band centers based on the threshold wights.
min_sum_dNs_for_a_band = 0.05 # Cut off criteria for minimum weights that a band center should have.
#===================================
err_tolerance = 1e-8 # The tolerance to group the bands set per unique kpoints value.
prec_pos_band_centers = 1e-5 # in eV # Precision when compared band centers from previous and current SCF
#===================================
unfolded_bandstructure_properties, all_scf_data = \
unfolded_band_properties.band_centers_broadening_bandstr(unfolded_bandstructure_,
min_dN_pre_screening=min_dN,
threshold_dN_2b_trial_band_center=
threshold_dN_2b_trial_band_center,
min_sum_dNs_for_a_band=min_sum_dNs_for_a_band,
precision_pos_band_centers=prec_pos_band_centers,
err_tolerance_compare_kpts_val=err_tolerance,
collect_scf_data=False)
5. Determine effective mass (parabolic and non-parabolic) from part of the band structure or band center data
m_star, optimized_parameters, convergence_measure = \
unfolded_band_properties.calculate_effecfive_mass(kpath, band_energy,
initial_guess_params=None,
params_bounds = (-np.inf, np.inf),
fit_weights=None, absolute_weights=False,
parabolic_dispersion=True,
hyperbolic_dispersion_positive=False,
hyperbolic_dispersion_negative=False,
params_name = ['alpha', 'kshift', 'cbm', 'gamma'])
#===================================
band_energy_fit = unfolded_band_properties.fit_functions(kpath, optimized_parameters,
parabolic_dispersion=True,
hyperbolic_dispersion_positive=False,
hyperbolic_dispersion_negative=False)
This is based on the ... paper.
TBA
One can save the generated data within the function call for unfolding and band ceneter determination routines as shown above. [recommened]
However, you may want save the generated data (from the above function calls) after some post processing, for e.g., you want to save part of the data only. In such cases, you can use functions from SaveBandStructuredata class to save those data. Note that the data format should be compatible with the required data format for each functions.
# Fermi energy
Efermi = 5.9740
# Minima in Energy axis to plot
Emin = -5
# Maxima in Energy axis to plot
Emax = 5
# Filename to save the figure. If None, figure will not be saved
save_file_name = 'unfolded_bandstructure.png'
Option 1: Continue with previous instance.
fig, ax, CountFig \
= band_unfold.plot_ebs(save_figure_dir=save_to_dir, save_file_name=save_file_name, CountFig=None,
Ef=Efermi, Emin=Emin, Emax=Emax, pad_energy_scale=0.5,
mode="density", special_kpoints=special_kpoints_pos_labels,
plotSC=True, fatfactor=20, nE=100,smear=0.2, marker='o',
threshold_weight=0.01, show_legend=True,
color='gray', color_map='viridis')
Option 2: Using BandUPpy Plotting module.
# --------------------- Initiate Plotting method ----------------------------
plot_unfold = banduppy.Plotting(save_figure_dir=save_to_dir)
# -------- Read the saved unfolded bandstructure saved data file ------------
unfolded_bandstructure_ = np.loadtxt(f'{save_to_dir}/bandstructure_unfolded.dat')
kpline = np.loadtxt(f'{save_to_dir}/kpoints_unfolded.dat')[:,1]
with open(f'{save_to_dir}/KPOINTS_SpecialKpoints.pkl', 'rb') as handle:
special_kpoints_pos_labels = pickle.load(handle)
fig, ax, CountFig \
= plot_unfold.plot_ebs(kpath_in_angs=kpline, unfolded_bandstructure=unfolded_bandstructure_,
save_file_name=save_file_name, CountFig=None,
Ef=Efermi, Emin=Emin, Emax=Emax, pad_energy_scale=0.5,
mode="density", special_kpoints=special_kpoints_pos_labels,
plotSC=True, fatfactor=20, nE=100,smear=0.2, marker='o',
threshold_weight=0.01, show_legend=True,
color='gray', color_map='viridis')
fig, ax, CountFig \
= plot_unfold.plot_ebs(kpath_in_angs=kpline1,
unfolded_bandstructure=unfolded_bandstructure_1,
save_file_name=None, CountFig=None, threshold_weight=0.1,
Ef=Efermi, Emin=Emin, Emax=Emax, pad_energy_scale=0.5,
mode="fatband", special_kpoints=special_kpoints_pos_labels1,
plotSC=True, fatfactor=20, nE=100, smear=0.2,
color='red', color_map='viridis', show_plot=False)
fig, ax, CountFig \
= plot_unfold.plot_ebs(ax=ax, kpath_in_angs=kpline1,
unfolded_bandstructure=unfolded_bandstructure_2,
save_file_name=save_file_name, CountFig=None,
Ef=Efermi, Emin=Emin, Emax=Emax, pad_energy_scale=0.5,
mode="fatband", special_kpoints=None, marker='x',
smear=0.2, color='black', color_map='viridis')
fig, ax, CountFig \
= plot_unfold.plot_ebs(kpath_in_angs=kpline,
unfolded_bandstructure=unfolded_bandstructure_properties,
save_file_name=save_file_name, CountFig=None, threshold_weight=min_dN,
Ef=Efermi, Emin=Emin, Emax=Emax, pad_energy_scale=0.5,
mode="band_centers", special_kpoints=special_kpoints_pos_labels,
marker='x', smear=0.2, plot_colormap_bandcenter=True,
color='black', color_map='viridis')
plot_unfold.plot_scf(kpath_in_angs=kpline, unfolded_bandstructure=unfolded_bandstructure_,
al_scf_data=all_scf_data, plot_max_scf_steps=3, save_file_name=save_file_name,
Ef=Efermi, Emin=Emin, Emax=Emax, pad_energy_scale=0.5, threshold_weight=min_dN,
special_kpoints=special_kpoints_pos_labels, plot_sc_unfold=True, marker='o',
fatfactor=20, smear=0.05, color=None, color_map='viridis', show_legend=False,
plot_colormap_bandcenter=True, show_colorbar=True, colorbar_label=None,
vmin=None, vmax=None, dpi=72)
Note: One can save the generated figure within the function call plot_ebs() and plot_scf(). save_plot_figure() function can also be used to save a figure instance independently.
If you have new suggestions, please feel free to reach out to us. We are committed to providing the best experience for our users and greatly value your feedback.
Have fun with BandUPpy!
Best wishes,
The BandUPpy Team