def plot_all_hartree_pot(self):
#plot planar averaged locpots, along with the difference for each axis
if not self._locpot_bulk:
print(
'did not feed path to locpot file! Need this for plotting hartree pot'
)
return
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(3, 1, 1)
ax.set_title('Locpot planar averaged potentials and difference')
bulkloc = Locpot.from_file(self._locpot_bulk)
defloc = Locpot.from_file(self._locpot_defect)
for axis in [0, 1, 2]:
ax = fig.add_subplot(3, 1, axis + 1)
defect_axis = defloc.get_axis_grid(axis)
defect_pot = defloc.get_average_along_axis(axis)
pure_pot = bulkloc.get_average_along_axis(axis)
ax.plot(defect_axis, pure_pot, 'r', label="Bulk potential")
ax.plot(defect_axis, defect_pot, 'b', label="Defect potential")
ax.plot(defect_axis,
defect_pot - pure_pot,
'k',
label='Defect-Bulk difference')
ax.plot([self._frac_coords[axis]*self._lengths[axis]],\
[0], 'or', markersize=4.0, label='Defect site')
ax.set_ylabel("axis " + str(axis + 1))
if not axis:
ax.legend()
ax.set_xlabel("distance (Angstrom)")
#plt.savefig('locpotavgdiffplot.png')
plt.show()
def plot_hartree_pot(self):
#plot planar averages of bulk and defect (good for seeing global changes)
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(3,1,1)
ax.set_title('Locpot planar averaged potentials')
bulkloc=Locpot.from_file(self._locpot_bulk)
defloc=Locpot.from_file(self._locpot_bulk)
get_agrid = bulkloc.get_axis_grid
get_baavg = bulkloc.get_average_along_axis
get_daavg = defloc.get_average_along_axis
for axis in [0,1,2]:
ax = fig.add_subplot(3, 1, axis+1)
latt_len = self._lengths[axis]
ax.plot(get_agrid(axis),get_baavg(axis),'r',
label="Bulk potential")
ax.plot(get_agrid(axis), get_daavg(axis),'b',
label="Defect potential")
ax.plot([self._frac_coords[axis]*latt_len], [0], 'or',
markersize=4.0, label="Defect site")
ax.set_ylabel("axis "+str(axis+1))
if not axis:
ax.legend()
ax.set_xlabel("distance (Angstrom)")
#plt.savefig('locpotavgplot.png')
plt.show()
def get_freysoldt_correction(defect_type, defect_specie, path_to_defect_locpot,path_to_pure_locpot,charge,
dielectric_constant,defect_site_coordinates,energy_cutoff=500,get_plot=False):
''' Function to perform charge corrections according to the method proposed py Freysoldt
If this correction is used, please reference Freysoldt's original paper.
doi: 10.1103/PhysRevLett.102.016402
Args:
defect_type: 'vacancy' or 'interstitial'
defect_specie: string with element occupying the defect site
path_to_defect_locpot: path to LOCPOT file of defect structure
path_to_pure_locpot: path to LOCPOT file of Pure structure
charge: Charge of the defected system
dielectric_constant: Dielectric constant
defect_site_coordinates: numpy array with fractional coordinates of defect site
energy_cutoff: Cut-off of plane wave expansion
get_plot: return also Matplotlib object with plot
Returns:
Freysoldt corrections values as a dictionary
'''
# acquiring data from LOCPOT files
locpot_pure = Locpot.from_file(path_to_pure_locpot)
vol_data_pure = VolumetricData(locpot_pure.structure,locpot_pure.data)
locpot_defect = Locpot.from_file(path_to_defect_locpot)
vol_data_defect = VolumetricData(locpot_defect.structure,locpot_defect.data)
parameters = {}
parameters['axis_grid'] = []
parameters['bulk_planar_averages'] = []
parameters['defect_planar_averages'] = []
for i in range(0,3):
parameters['axis_grid'].append(vol_data_pure.get_axis_grid(i))
parameters['bulk_planar_averages'].append(vol_data_pure.get_average_along_axis(i))
parameters['defect_planar_averages'].append(vol_data_defect.get_average_along_axis(i))
parameters['initial_defect_structure'] = locpot_defect.structure
parameters['defect_frac_sc_coords'] = defect_site_coordinates
structure_bulk = locpot_pure.structure
defect_site = PeriodicSite(defect_specie, coords=defect_site_coordinates, lattice = locpot_pure.structure.lattice)
module = importlib.import_module("pymatgen.analysis.defects.core")
defect_class = getattr(module,defect_type)
defect = defect_class(structure_bulk, defect_site, charge=charge, multiplicity=None)
defect_entry = DefectEntry(defect,None,corrections=None,parameters=parameters)
freysoldt_class = FreysoldtCorrection(dielectric_constant,energy_cutoff=energy_cutoff)
freysoldt_corrections = freysoldt_class.get_correction(defect_entry)
if get_plot:
plt = freysoldt_class.plot(1)
return freysoldt_corrections , plt
else:
return freysoldt_corrections
def correction(self, title=None, partflag='All'):
"""
Args:
title: set if you want to plot the planar averaged potential
partflag: four options
'pc' for just point charge correction, or
'potalign' for just potalign correction, or
'All' for both, or
'AllSplit' for individual parts split up (form [PC,potterm,full])
"""
logger = logging.getLogger(__name__)
logger.info('This is Freysoldt Correction.')
if not self._q:
if partflag == 'AllSplit':
return [0.0, 0.0, 0.0]
else:
return 0.0
if not type(self._purelocpot) is Locpot:
logger.debug('Load bulk locpot')
self._purelocpot = Locpot.from_file(self._purelocpot)
logger.debug('\nRun PC energy')
if partflag != 'potalign':
energy_pc = self.pc()
logger.debug('PC calc done, correction = %f', round(energy_pc, 4))
logger.debug('Now run potenttial alignment script')
if partflag != 'pc':
if not type(self._deflocpot) is Locpot:
logger.debug('Load defect locpot')
self._deflocpot = Locpot.from_file(self._deflocpot)
potalign = self.potalign(title=title)
logger.info('\n\nFreysoldt Correction details:')
if partflag != 'potalign':
logger.info('PCenergy (E_lat) = %f', round(energy_pc, 5))
if partflag != 'pc':
logger.info('potential alignment (-q*delta V) = %f',
round(potalign, 5))
if partflag in ['All', 'AllSplit']:
logger.info('TOTAL Freysoldt correction = %f',
round(energy_pc + potalign, 5))
if partflag == 'pc':
return round(energy_pc, 5)
elif partflag == 'potalign':
return round(potalign, 5)
elif partflag == 'All':
return round(energy_pc + potalign, 5)
else:
return map(lambda x: round(x, 5),
[energy_pc, potalign, energy_pc + potalign])
def freysoldt_loader(self, bulk_locpot=None):
"""Load metadata required for performing Freysoldt correction
requires "bulk_path" and "defect_path" to be loaded to DefectEntry parameters dict.
Can read gunzipped "LOCPOT.gz" files as well.
Args:
bulk_locpot (Locpot): Add bulk Locpot object for expedited parsing.
If None, will load from file path variable bulk_path
Return:
bulk_locpot object for reuse by another defect entry (for expedited parsing)
"""
if not self.defect_entry.charge:
#dont need to load locpots if charge is zero
return None
if not bulk_locpot:
bulk_locpot_path = os.path.join( self.defect_entry.parameters["bulk_path"],
"LOCPOT")
if os.path.exists(bulk_locpot_path):
bulk_locpot = Locpot.from_file(bulk_locpot_path)
elif os.path.exists(bulk_locpot_path+".gz"):
bulk_locpot = Locpot.from_file(bulk_locpot_path + ".gz")
else:
raise FileNotFoundError(f"""Well I can't f*****g find a LOCPOT(.gz) in {self.defect_entry.parameters['bulk_path']}.
You sure there's one there pal? I need it to get the Freysoldt correction""")
def_locpot_path = os.path.join( self.defect_entry.parameters["defect_path"],
"LOCPOT")
if os.path.exists(def_locpot_path):
def_locpot = Locpot.from_file(def_locpot_path)
elif os.path.exists(def_locpot_path + ".gz"):
def_locpot = Locpot.from_file(def_locpot_path + ".gz")
else:
raise FileNotFoundError(f"""Well I can't f*****g find a LOCPOT(.gz) in {self.defect_entry.parameters['defect_path']}.
You sure there's one there pal? I need it to get the Freysoldt correction""")
axis_grid = [def_locpot.get_axis_grid(i) for i in range(3)]
bulk_planar_averages = [bulk_locpot.get_average_along_axis(i) for i in range(3)]
defect_planar_averages = [def_locpot.get_average_along_axis(i) for i in range(3)]
defect_frac_sc_coords = self.defect_entry.site.frac_coords
self.defect_entry.parameters.update({"axis_grid": axis_grid,
"bulk_planar_averages": bulk_planar_averages,
"defect_planar_averages": defect_planar_averages,
"initial_defect_structure": def_locpot.structure,
"defect_frac_sc_coords": defect_frac_sc_coords
})
return bulk_locpot
def vacuum(path=None):
'''
Gets the energy of the vacuum level. It either parses potential.csv file if
available or tries to calculate planar potential from LOCPOT. If neither
file is available, function returns np.nan.
Args:
path (`str`, optional): the path to potential.csv or LOCPOT files.
Can be the path to a directory in which either file is or you can
specify a path that must end in .csv or contain LOCPOT. Defaults to
looking for potential.csv or LOCPOT in cwd.
Returns:
Maximum value of planar potential
'''
if type(path) == str and path.endswith('.csv'):
df = pd.read_csv(path)
max_potential = df['planar'].max()
max_potential = round(max_potential, 3)
elif type(path) == str and 'LOCPOT' in path:
lpt = Locpot.from_file(path)
planar = lpt.get_average_along_axis(2)
max_potential = float(f"{np.max(planar): .3f}")
else:
if path is None:
cwd = os.getcwd()
else:
cwd = path
if os.path.isfile('{}/potential.csv'.format(cwd)):
df = pd.read_csv('{}/potential.csv'.format(cwd))
max_potential = df['planar'].max()
max_potential = round(max_potential, 3)
elif os.path.isfile('{}/LOCPOT'.format(cwd)):
lpt = Locpot.from_file('{}/LOCPOT'.format(cwd))
planar = lpt.get_average_along_axis(2)
max_potential = float(f"{np.max(planar): .3f}")
else:
max_potential = np.nan
warnings.formatwarning = _custom_formatwarning
warnings.warn(
'Vacuum electrostatic potential was not parsed from {} '
'no LOCPOT or potential.csv files were provided.'.format(path))
return max_potential
def freysoldt_loader(self, bulk_locpot=None):
"""Load metadata required for performing Freysoldt correction
requires "bulk_path" and "defect_path" to be loaded to DefectEntry parameters dict.
Args:
bulk_locpot (Locpot): Add bulk Locpot object for expedited parsing.
If None, will load from file path variable bulk_path
Return:
bulk_locpot object for reuse by another defect entry (for expedited parsing)
"""
if not self.defect_entry.charge:
#dont need to load locpots if charge is zero
return None
if not bulk_locpot:
bulk_locpot_path = os.path.join(
self.defect_entry.parameters["bulk_path"], "LOCPOT")
bulk_locpot = Locpot.from_file(bulk_locpot_path)
def_locpot_path = os.path.join(
self.defect_entry.parameters["defect_path"], "LOCPOT")
def_locpot = Locpot.from_file(def_locpot_path)
axis_grid = [def_locpot.get_axis_grid(i) for i in range(3)]
bulk_planar_averages = [
bulk_locpot.get_average_along_axis(i) for i in range(3)
]
defect_planar_averages = [
def_locpot.get_average_along_axis(i) for i in range(3)
]
defect_frac_sc_coords = self.defect_entry.site.frac_coords
self.defect_entry.parameters.update({
"axis_grid":
axis_grid,
"bulk_planar_averages":
bulk_planar_averages,
"defect_planar_averages":
defect_planar_averages,
"initial_defect_structure":
def_locpot.structure,
"defect_frac_sc_coords":
defect_frac_sc_coords
})
return bulk_locpot
def test_init(self):
filepath = os.path.join(test_dir, "LOCPOT")
locpot = Locpot.from_file(filepath)
self.assertAlmostEqual(-217.05226954, sum(locpot.get_average_along_axis(0)))
self.assertAlmostEqual(locpot.get_axis_grid(0)[-1], 2.87629, 2)
self.assertAlmostEqual(locpot.get_axis_grid(1)[-1], 2.87629, 2)
self.assertAlmostEqual(locpot.get_axis_grid(2)[-1], 2.87629, 2)
def __init__(self, locpot_bulk_path, locpot_defect_path, charge, epsilon,
site_frac_coords, encut, lengths=None, name=''):
"""
Args:
locpot_bulk:
Location of LOCPOT of bulk
locpot_defect:
Location of LOCPOT of defect
charge:
Charge relative to neutral defect (not relative to bulk)
epsilon:
Dielectric constant obtained from relaxation run
site_frac_coords:
Fractional coordinates of defect site as list
encut:
Planewave basis energy cutoff used in VASP run (in eV)
name:
Name of the defect to write files
lengths:
Length of lattice vectors.
"""
self._locpot_bulk = locpot_bulk_path
self._locpot_defect = locpot_defect_path
self._charge = charge
self._epsilon = epsilon
self._frac_coords = site_frac_coords
self._encut = encut
if not lengths:
struct=Locpot.from_file(locpot_bulk_path)
self._lengths=struct.structure.lattice.abc
print('had to import lengths, if want to speed up set lengths='+str(self._lengths))
else:
self._lengths = lengths
self._name = name
def from_files(poscar_filename, locpot_filename, outcar_filename, shift=0):
p = Poscar.from_file(poscar_filename)
l = Locpot.from_file(locpot_filename)
o = Outcar(outcar_filename)
return WorkFunctionAnalyzer(p.structure,
l.get_average_along_axis(int(sys.argv[1])),
o.efermi, shift=shift)
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor. If spin-polarized, returns all 4 band edges.
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file("LOCPOT")
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun("vasprun.xml")
efermi = vasprun.efermi - evac
if vasprun.get_band_structure().is_spin_polarized:
eigenvals = {Spin.up: [], Spin.down: []}
for band in vasprun.eigenvalues:
for eigenvalue in vasprun.eigenvalues[band]:
eigenvals[band[0]].append(eigenvalue)
up_cbm = min([e[0] for e in eigenvals[Spin.up] if not e[1]]) - evac
up_vbm = max([e[0] for e in eigenvals[Spin.up] if e[1]]) - evac
dn_cbm = min([e[0] for e in eigenvals[Spin.down] if not e[1]]) - evac
dn_vbm = max([e[0] for e in eigenvals[Spin.down] if e[1]]) - evac
edges = {"up_cbm": up_cbm, "up_vbm": up_vbm, "dn_cbm": dn_cbm, "dn_vbm": dn_vbm, "efermi": efermi}
else:
bs = vasprun.get_band_structure()
cbm = bs.get_cbm()["energy"] - evac
vbm = bs.get_vbm()["energy"] - evac
edges = {"cbm": cbm, "vbm": vbm, "efermi": efermi}
return edges
def plot_local_potential(axis=2, ylim=(-20, 0), fmt='pdf'):
"""
Plot data from the LOCPOT file along any of the 3 primary axes.
Useful for determining surface dipole moments and electric
potentials on the interior of the material.
Args:
axis (int): 0 = x, 1 = y, 2 = z
ylim (tuple): minimum and maximum potentials for the plot's y-axis.
fmt (str): matplotlib format style. Check the matplotlib docs
for options.
"""
ax = plt.figure(figsize=(16, 10)).gca()
locpot = Locpot.from_file('LOCPOT')
structure = Structure.from_file('CONTCAR')
vd = VolumetricData(structure, locpot.data)
abs_potentials = vd.get_average_along_axis(axis)
vacuum_level = max(abs_potentials)
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
if not bs.is_metal():
cbm = bs.get_cbm()['energy'] - vacuum_level
vbm = bs.get_vbm()['energy'] - vacuum_level
potentials = [potential - vacuum_level for potential in abs_potentials]
axis_length = structure.lattice.lengths[axis]
positions = np.arange(0, axis_length, axis_length / len(potentials))
ax.plot(positions, potentials, linewidth=2, color='k')
ax.set_xlim(0, axis_length)
ax.set_ylim(ylim[0], ylim[1])
ax.set_xticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_xticks()], size=20)
ax.set_yticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_yticks()], size=20)
ax.set_xlabel(r'$\mathrm{\AA}$', size=24)
ax.set_ylabel(r'$\mathrm{V\/(eV)}$', size=24)
if not bs.is_metal():
ax.text(ax.get_xlim()[1], cbm, r'$\mathrm{CBM}$',
horizontalalignment='right', verticalalignment='bottom',
size=20)
ax.text(ax.get_xlim()[1], vbm, r'$\mathrm{VBM}$',
horizontalalignment='right', verticalalignment='top', size=20)
ax.fill_between(ax.get_xlim(), cbm, ax.get_ylim()[1],
facecolor=plt.cm.jet(0.3), zorder=0, linewidth=0)
ax.fill_between(ax.get_xlim(), ax.get_ylim()[0], vbm,
facecolor=plt.cm.jet(0.7), zorder=0, linewidth=0)
if fmt == "None":
return ax
else:
plt.savefig('locpot.{}'.format(fmt))
plt.close()
def plot_local_potential(axis=2, ylim=(-20, 0), fmt='pdf'):
"""
Plot data from the LOCPOT file along any of the 3 primary axes.
Useful for determining surface dipole moments and electric
potentials on the interior of the material.
Args:
axis (int): 0 = x, 1 = y, 2 = z
ylim (tuple): minimum and maximum potentials for the plot's y-axis.
fmt (str): matplotlib format style. Check the matplotlib docs
for options.
"""
ax = plt.figure(figsize=(16, 10)).gca()
locpot = Locpot.from_file('LOCPOT')
structure = Structure.from_file('CONTCAR')
vd = VolumetricData(structure, locpot.data)
abs_potentials = vd.get_average_along_axis(axis)
vacuum_level = max(abs_potentials)
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
if not bs.is_metal():
cbm = bs.get_cbm()['energy'] - vacuum_level
vbm = bs.get_vbm()['energy'] - vacuum_level
potentials = [potential - vacuum_level for potential in abs_potentials]
axis_length = structure.lattice._lengths[axis]
positions = np.arange(0, axis_length, axis_length / len(potentials))
ax.plot(positions, potentials, linewidth=2, color='k')
ax.set_xlim(0, axis_length)
ax.set_ylim(ylim[0], ylim[1])
ax.set_xticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_xticks()], size=20)
ax.set_yticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_yticks()], size=20)
ax.set_xlabel(r'$\mathrm{\AA}$', size=24)
ax.set_ylabel(r'$\mathrm{V\/(eV)}$', size=24)
if not bs.is_metal():
ax.text(ax.get_xlim()[1], cbm, r'$\mathrm{CBM}$',
horizontalalignment='right', verticalalignment='bottom',
size=20)
ax.text(ax.get_xlim()[1], vbm, r'$\mathrm{VBM}$',
horizontalalignment='right', verticalalignment='top', size=20)
ax.fill_between(ax.get_xlim(), cbm, ax.get_ylim()[1],
facecolor=plt.cm.jet(0.3), zorder=0, linewidth=0)
ax.fill_between(ax.get_xlim(), ax.get_ylim()[0], vbm,
facecolor=plt.cm.jet(0.7), zorder=0, linewidth=0)
if fmt == "None":
return ax
else:
plt.savefig('locpot.{}'.format(fmt))
plt.close()
def test_init(self):
filepath = os.path.join(test_dir, 'LOCPOT')
locpot = Locpot.from_file(filepath)
self.assertAlmostEqual(-217.05226954,
sum(locpot.get_average_along_axis(0)))
self.assertAlmostEqual(locpot.get_axis_grid(0)[-1], 2.87629, 2)
self.assertAlmostEqual(locpot.get_axis_grid(1)[-1], 2.87629, 2)
self.assertAlmostEqual(locpot.get_axis_grid(2)[-1], 2.87629, 2)
def setUp(self):
self.bl = Locpot.from_file(bl_path)
self.dl = Locpot.from_file(dl_path)
self.bs = self.bl.structure
self.ds = self.dl.structure
self.kbi = KumagaiBulkInit(self.bs,
self.bl.dim,
15,
optgamma=3.49423226983)
self.kc = KumagaiCorrection(15,
-3,
3.49423226983,
self.kbi.g_sum,
self.bs,
self.ds,
bulk_locpot=self.bl,
defect_locpot=self.dl)
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor.
Returns:
edges (dict): {'up_cbm': , 'up_vbm': , 'dn_cbm': , 'dn_vbm': , 'efermi'}
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
eigenvals = vasprun.eigenvalues
efermi = vasprun.efermi - evac
if bs.is_spin_polarized:
print(eigenvals[Spin.up])
print([e[0] - evac for e in eigenvals[Spin.up][0]])
up_cbm = min([
min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])
for i in range(len(eigenvals[Spin.up]))
]) - evac
up_vbm = max([
max([e[0] for e in eigenvals[Spin.up][i] if e[1]])
for i in range(len(eigenvals[Spin.up]))
]) - evac
dn_cbm = min([
min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])
for i in range(len(eigenvals[Spin.down]))
]) - evac
dn_vbm = max([
max([e[0] for e in eigenvals[Spin.down][i] if e[1]])
for i in range(len(eigenvals[Spin.down]))
]) - evac
edges = {
'up_cbm': up_cbm,
'up_vbm': up_vbm,
'dn_cbm': dn_cbm,
'dn_vbm': dn_vbm,
'efermi': efermi
}
else:
cbm = bs.get_cbm()['energy'] - evac
vbm = bs.get_vbm()['energy'] - evac
edges = {
'up_cbm': cbm,
'up_vbm': vbm,
'dn_cbm': cbm,
'dn_vbm': vbm,
'efermi': efermi
}
return edges
def plot_hartree_pot_diff(self):
#only plot the difference in planar averaged potentials
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(3,1,1)
ax.set_title('Locpot planar averaged potential difference')
bulkloc=Locpot.from_file(self._locpot_bulk)
defloc=Locpot.from_file(self._locpot_defect)
for axis in [0,1,2]:
ax = fig.add_subplot(3, 1, axis+1)
defect_axis = defloc.get_axis_grid(axis)
defect_pot = defloc.get_average_along_axis(axis)
pure_pot = bulkloc.get_average_along_axis(axis)
latt_len = self._lengths[axis]
ax.plot(defect_axis,defect_pot-pure_pot,'b',
label='Defect-Bulk difference')
ax.plot([self._frac_coords[axis] * latt_len], [0], 'or',
markersize=4.0, label='Defect site')
ax.set_ylabel("axis "+str(axis+1))
if not axis:
ax.legend()
ax.set_xlabel("distance (Angstrom)")
#plt.savefig('locpotdiffplot.png')
plt.show()
def process_LOCPOT(self, locpot_path=None, dir_to_save=None):
"""
This function process LOCPOT file obtained by VASP
:param file_path: path to LOCPOT file
:param dir_to_save: path to directory to save vacuum_lvl
:return: nothing
"""
if locpot_path == None:
locpot_path = self.locpot_path
if dir_to_save == None:
dir_to_save = self.dir_to_save
locpot = Locpot.from_file(locpot_path)
avr = locpot.get_average_along_axis(2)
self.vacuum_lvl = np.max(avr)
self.save('vacuum_lvl', dir_to_save)
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor. For a metal, returns the fermi level.
Returns:
edges (dict): {'up_cbm': , 'up_vbm': , 'dn_cbm': , 'dn_vbm': , 'efermi'}
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
eigenvals = vasprun.eigenvalues
efermi = vasprun.efermi - evac
if bs.is_metal():
edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,
'efermi': efermi}
elif bs.is_spin_polarized:
up_cbm = min(
[min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])
for i in range(len(eigenvals[Spin.up]))]) - evac
up_vbm = max(
[max([e[0] for e in eigenvals[Spin.up][i] if e[1]])
for i in range(len(eigenvals[Spin.up]))]) - evac
dn_cbm = min(
[min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])
for i in range(len(eigenvals[Spin.down]))]) - evac
dn_vbm = max(
[max([e[0] for e in eigenvals[Spin.down][i] if e[1]])
for i in range(len(eigenvals[Spin.down]))]) - evac
edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,
'dn_vbm': dn_vbm, 'efermi': efermi}
else:
cbm = bs.get_cbm()['energy'] - evac
vbm = bs.get_vbm()['energy'] - evac
edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,
'efermi': efermi}
return edges
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor. If spin-polarized, returns all 4 band edges.
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun('vasprun.xml')
efermi = vasprun.efermi - evac
if vasprun.get_band_structure().is_spin_polarized:
eigenvals = {Spin.up: [], Spin.down: []}
for band in vasprun.eigenvalues:
for eigenvalue in vasprun.eigenvalues[band]:
eigenvals[band[0]].append(eigenvalue)
up_cbm = min([e[0] for e in eigenvals[Spin.up] if not e[1]]) - evac
up_vbm = max([e[0] for e in eigenvals[Spin.up] if e[1]]) - evac
dn_cbm = min([e[0] for e in eigenvals[Spin.down] if not e[1]]) - evac
dn_vbm = max([e[0] for e in eigenvals[Spin.down] if e[1]]) - evac
edges = {
'up_cbm': up_cbm,
'up_vbm': up_vbm,
'dn_cbm': dn_cbm,
'dn_vbm': dn_vbm,
'efermi': efermi
}
else:
bs = vasprun.get_band_structure()
cbm = bs.get_cbm()['energy'] - evac
vbm = bs.get_vbm()['energy'] - evac
edges = {'cbm': cbm, 'vbm': vbm, 'efermi': efermi}
return edges
def load_data_for_ecstm(self):
"""
This inner function load necessary data for ECSTM calculations
:param outcar_path: str
path to OUTCAR vasp file
:param locpot_path: str
path to LOCPOT vasp file
:return:
"""
try:
self.E = np.load(self.path_to_data + '/E.npy')
self.DOS = np.load(self.path_to_data + '/DOS.npy')
except:
import preprocessing_v2 as preprocessing
p = preprocessing.Preprocessing(working_folder=self.working_folder)
p.process_OUTCAR()
self.E, self.DOS, dE_new = p.get_DOS(self.energy_range, self.dE)
if dE_new != self.dE:
print(
"WARNING! Something wrong with dE during DOS calculations")
np.save(self.path_to_data + '/DOS.npy', self.DOS)
np.save(self.path_to_data + '/E.npy', self.E)
try:
self.efermi = np.load(self.path_to_data + '/efermi.npy')
except:
print(
f"ERROR! {self.path_to_data}/efermi.npy does not exist. Try to preprocess data"
)
try:
self.vacuum_lvl = np.load(self.path_to_data + 'vacuum_lvl.npy')
except:
from pymatgen.io.vasp.outputs import Locpot
locpot = Locpot.from_file(self.working_folder + '/LOCPOT')
avr = locpot.get_average_along_axis(2)
self.vacuum_lvl = np.max(avr)
np.save(self.path_to_data + '/vacuum_lvl.npy', self.vacuum_lvl)
def setUp(self):
self.bl = Locpot.from_file(bl_path)
self.dl = Locpot.from_file(dl_path)
self.bs = self.bl.structure
self.ds = self.dl.structure
corrections = {}
include_freysoldt_corrections = True
###############################################################################
###############################################################################
# Paths have to end with backslash. I would update everything with os.path.join but I'm lazy
print('Path of pure calculation: "%s"\n' % pure_path)
print('Path of pure LOCPOT calculation: "%s"\n' % path_to_pure_locpot)
print('Path of pure DOS calculation: "%s"\n' % dos_path)
print('Path of defects calculation: "%s"\n' % input_path)
system_name = os.path.basename(os.path.dirname(input_path))
path_to_pure_locpot = os.path.join(path_to_pure_locpot, 'LOCPOT')
structure_pure = Locpot.from_file(path_to_pure_locpot).structure
natoms_sup = len(structure_pure.sites)
vasprun_pure = Vasprun(pure_path + 'vasprun.xml')
vasprun_dos = Vasprun(dos_path + 'vasprun.xml')
(band_gap, cbm, vbm,
is_band_gap_direct) = vasprun_dos.eigenvalue_band_properties
natoms_pure = len(Locpot.from_file(path_to_pure_locpot).structure.sites)
Epure = ((vasprun_pure.final_energy) / natoms_pure) * natoms_sup
list_dir_vacancies = glob(input_path + '*/')
# saving initial corrections dictionary
corrections_init = corrections.copy()
# list with defects
defect_list = []
def electrostatic_potential(lattice_vector,
filename='./LOCPOT',
axis=2,
make_csv=True,
csv_fname='potential.csv',
plt_fname='potential.png',
dpi=300,
**kwargs):
"""
Reads LOCPOT to get the planar and macroscopic potential in specified direction
Args:
lattice_vector (float): the periodicity of the slab
filename (str): path to your locpot file, default='./LOCPOT'
axis (int): direction in which the potential is investigated; a=0, b=1,
c=2; default=2
make_csv (bool): makes a csv file with planar and macroscopic potential,
default=True
csv_fname (str): filename of the csv file, default='potential.csv'
plt_fname (str): filename of the plot of potentials, controls the format,
default='potential.png'
dpi (int): dots per inch; default=300
Returns:
csv file and plot of planar and macroscopic potential
"""
# Read potential and structure data
lpt = Locpot.from_file(filename)
struc = Structure.from_file(filename)
# Planar potential
planar = lpt.get_average_along_axis(axis)
# Divide lattice parameter by no. of grid points in the direction
resolution = struc.lattice.abc[axis] / lpt.dim[axis]
# Get number of points over which the rolling average is evaluated
points = int(lattice_vector / resolution)
# Need extra points at the start and end of planar potential to evaluate the
# macroscopic potential this makes use of the PBC where the end of one unit
# cell coincides with start of the next one
add_to_start = planar[(len(planar) - points):]
add_to_end = planar[0:points]
pfm_data = np.concatenate((add_to_start, planar, add_to_end))
pfm = pd.DataFrame(data=pfm_data, columns=['y'])
# Macroscopic potential
m_data = pfm.y.rolling(window=points, center=True).mean()
macroscopic = m_data.iloc[points:(len(planar) + points)]
macroscopic.reset_index(drop=True, inplace=True)
# Make csv
if make_csv:
data = pd.DataFrame(data=planar, columns=['planar'])
data['macroscopic'] = macroscopic
data.to_csv(csv_fname, header=True, index=False)
# Plot both planar and macroscopic, save figure
fig, ax = plt.subplots()
ax.plot(planar, label='planar')
ax.plot(macroscopic, label='macroscopic')
ax.legend()
plt.ylabel('Potential / eV')
plt.savefig(plt_fname, dpi=dpi, **kwargs)
def __init__(self,
dielectric_tensor,
q,
gamma,
g_sum,
bulk_structure,
defect_structure,
energy_cutoff=520,
madetol=0.0001,
lengths=None,
**kw):
"""
Args:
dielectric_tensor:
Macroscopic dielectric tensor
Include ionic also if defect is relaxed, othewise ion clamped.
Can be a matrix array or scalar.
q:
Charge associated with the defect. Typically integer
gamma:
Convergence parameter. Obtained from KumagaiBulkPart
g_sum:
value that is dependent on the Bulk only.
Obtained from KumagaiBulkPart
bulk_structure:
bulk Pymatgen structure object. Need to specify this if
using Outcar method for atomic site avg.
(If you specify outcar files for bulk_file_path but dont
specify structure then code will break)
(TO DO: resolve this dumb dependency by being smarter
about where structure comes from?)
defect_structure:
defect structure. Needed if using Outcar method
energy_cutoff:
Energy for plane wave cutoff (in eV).
If not given, Materials Project default 520 eV is used.
madetol:
Tolerance for convergence of energy terms in eV
lengths:
Lengths of axes, for speeding up plotting slightly
keywords:
1) bulk_locpot: Bulk Locpot file path OR Bulk Locpot
defect_locpot: Defect Locpot file path or defect Locpot
2) (Or) bulk_outcar: Bulk Outcar file path
defect_outcar: Defect outcar file path
3) defect_position: Defect position as a pymatgen Site object in the bulk supercell structure
NOTE: this is optional but recommended, if not provided then analysis is done to find
the defect position; this analysis has been rigorously tested, but has broken in an example with
severe long range relaxation
(at which point you probably should not be including the defect in your analysis...)
"""
if isinstance(dielectric_tensor, int) or \
isinstance(dielectric_tensor, float):
self.dieltens = np.identity(3) * dielectric_tensor
else:
self.dieltens = np.array(dielectric_tensor)
if 'bulk_locpot' in kw:
if isinstance(kw['bulk_locpot'], Locpot):
self.locpot_blk = kw['bulk_locpot']
else:
self.locpot_blk = Locpot.from_file(kw['bulk_locpot'])
if isinstance(kw['defect_locpot'], Locpot):
self.locpot_def = kw['defect_locpot']
else:
self.locpot_def = Locpot.from_file(kw['defect_locpot'])
self.dim = self.locpot_blk.dim
self.outcar_blk = None
self.outcar_def = None
self.do_outcar_method = False
if 'bulk_outcar' in kw:
self.outcar_blk = Outcar(str(kw['bulk_outcar']))
self.outcar_def = Outcar(str(kw['defect_outcar']))
self.do_outcar_method = True
self.locpot_blk = None
self.locpot_def = None
self.dim = self.outcar_blk.ngf
if 'defect_position' in kw:
self._defpos = kw['defect_position']
else:
self._defpos = None
self.madetol = madetol
self.q = q
self.encut = energy_cutoff
self.structure = bulk_structure
self.defstructure = defect_structure
self.gamma = gamma
self.g_sum = g_sum
self.lengths = lengths
def electrostatic_potential(locpot='./LOCPOT',
lattice_vector=None,
save_csv=True,
csv_fname='potential.csv',
save_plt=True,
plt_fname='potential.png',
**kwargs):
"""
Reads LOCPOT to get the planar and optionally macroscopic potential in
c direction.
Args:
locpot (`str`, optional): The path to the LOCPOT file. Defaults to
``'./LOCPOT'``
lattice_vector (`float`, optional): The periodicity of the slab,
calculates macroscopic potential with that periodicity
save_csv (`bool`, optional): Saves to csv. Defaults to ``True``.
csv_fname (`str`, optional): Filename of the csv file. Defaults
to ``'potential.csv'``.
save_plt (`bool`, optional): Make and save the plot of electrostatic
potential. Defaults to ``True``.
plt_fname (`str`, optional): Filename of the plot. Defaults to
``'potential.png'``.
Returns:
DataFrame
"""
# Read potential and structure data
lpt = Locpot.from_file(locpot)
struc = Structure.from_file(locpot)
# Planar potential
planar = lpt.get_average_along_axis(2)
df = pd.DataFrame(data=planar, columns=['planar'])
# Calculate macroscopic potential
if lattice_vector is not None:
# Divide lattice parameter by no. of grid points in the direction
resolution = struc.lattice.abc[2] / lpt.dim[2]
# Get number of points over which the rolling average is evaluated
points = int(lattice_vector / resolution)
# Need extra points at the start and end of planar potential to evaluate the
# macroscopic potential this makes use of the PBC where the end of one unit
# cell coincides with start of the next one
add_to_start = planar[(len(planar) - points):]
add_to_end = planar[0:points]
pfm_data = np.concatenate((add_to_start, planar, add_to_end))
pfm = pd.DataFrame(data=pfm_data, columns=['y'])
# Macroscopic potential
m_data = pfm.y.rolling(window=points, center=True).mean()
macroscopic = m_data.iloc[points:(len(planar) + points)]
macroscopic.reset_index(drop=True, inplace=True)
df['macroscopic'] = macroscopic
# Get gradient of the plot - this is used for convergence testing, to make
# sure the potential is actually flat
df['gradient'] = np.gradient(df['planar'])
# Plot and save the graph, save the csv or return the dataframe
if save_plt:
plot_electrostatic_potential(df=df, plt_fname=plt_fname, **kwargs)
if save_csv:
if not csv_fname.endswith('.csv'):
csv_fname += '.csv'
df.to_csv(csv_fname, header=True, index=False)
else:
return df
def pc(self, struct=None):
"""
Peform Electrostatic Correction
note this ony needs structural info
so struct input object speeds this calculation up
equivalently fast if input Locpot is a locpot object
"""
logger = logging.getLogger(__name__)
if type(struct) is Structure:
s1 = struct
else:
if not type(self._purelocpot) is Locpot:
logging.info('load Pure locpot')
self._purelocpot = Locpot.from_file(self._purelocpot)
s1 = self._purelocpot.structure
ap = s1.lattice.get_cartesian_coords(1)
logger.info('Running Freysoldt 2011 PC calculation (should be '\
'equivalent to sxdefectalign)')
logger.debug('defect lattice constants are (in angstroms)' \
+ str(cleanlat(ap)))
[a1, a2, a3] = ang_to_bohr * ap
logging.debug( 'In atomic units, lat consts are (in bohr):' \
+ str(cleanlat([a1, a2, a3])))
vol = np.dot(a1, np.cross(a2, a3)) #vol in bohr^3
#compute isolated energy
step = 1e-4
encut1 = 20 #converge to some smaller encut first [eV]
flag = 0
converge = []
while (flag != 1):
eiso = 1.
gcut = eV_to_k(encut1) #gcut is in units of 1/A
g = step #initalize
while g < (gcut + step):
#simpson integration
eiso += 4 * (self._q_model.rho_rec(g * g)**2)
eiso += 2 * (self._q_model.rho_rec((g + step)**2)**2)
g += 2 * step
eiso -= self._q_model.rho_rec(gcut**2)**2
eiso *= (self._q**2) * step / (3 * round(np.pi, 6))
converge.append(eiso)
if len(converge) > 2:
if abs(converge[-1] - converge[-2]) < self._madetol:
flag = 1
elif encut1 > self._encut:
logger.error('Eiso did not converge before ' \
+ str(self._encut) + ' eV')
raise
encut1 += 20
eiso = converge[-1]
logger.debug('Eisolated : %f, converged at encut: %d', round(eiso, 5),
encut1 - 20)
#compute periodic energy;
encut1 = 20 #converge to some smaller encut
flag = 0
converge = []
while flag != 1:
eper = 0.0
for g2 in generate_reciprocal_vectors_squared(a1, a2, a3, encut1):
eper += (self._q_model.rho_rec(g2)**2) / g2
eper *= (self._q**2) * 2 * round(np.pi, 6) / vol
eper += (self._q**2) *4* round(np.pi, 6) \
* self._q_model.rho_rec_limit0() / vol
converge.append(eper)
if len(converge) > 2:
if abs(converge[-1] - converge[-2]) < self._madetol:
flag = 1
elif encut1 > self._encut:
logger.error('Eper did not converge before %d eV',
self._encut)
return
encut1 += 20
eper = converge[-1]
logger.info('Eperiodic : %f hartree, converged at encut %d eV',
round(eper, 5), encut1 - 20)
logger.info('difference (periodic-iso) is %f hartree',
round(eper - eiso, 6))
logger.info('difference in (eV) is %f',
round((eper - eiso) * hart_to_ev, 4))
PCfreycorr = round((eiso - eper) / self._dielectricconst * hart_to_ev,
6)
logger.info('Defect Correction without alignment %f (eV): ',
PCfreycorr)
return PCfreycorr
CONTACR and LOCPOT files.")
(options, args) = parser.parse_args()
v = Vasprun('vasprun.xml')
cdos = v.complete_dos
element_dos = cdos.get_element_dos()
plotter = DosPlotter()
efermi = v.efermi
if options.verbose:
from pymatgen.core import Element
from pymatgen.io.vasp.outputs import Locpot, Poscar
from pymatgen.analysis.surface_analysis import WorkFunctionAnalyzer
l = Locpot.from_file('LOCPOT')
s = Poscar.from_file('CONTCAR')
wf = WorkFunctionAnalyzer(s.structure,
l.get_average_along_axis(1),
efermi,
shift=0)
loc_vac = wf.vacuum_locpot
for i in element_dos:
element_dos[i].efermi = loc_vac
plotter.add_dos_dict(element_dos)
plt = plotter.get_plot(xlim=[-9, 1])
plt.plot([efermi - loc_vac, efermi - loc_vac],
plt.ylim(),
def potalign(self,
title=None,
widthsample=1.0,
axis=None,
output_sr=False):
"""
For performing planar averaging potential alignment
Accounts for defects in arbitrary positions
title is for name of plot, if you dont want a plot then leave it as None
widthsample is the width of the region in between defects where the potential alignment correction is averaged
axis allows you to override the axis setting of class
(good for quickly plotting multiple axes without having to reload Locpot)
output_sr allows for output of the short range potential in the middle (sampled) region.
(Good for delocalization analysis)
"""
logger = logging.getLogger(__name__)
if axis is None:
axis = self._axis
else:
axis = axis
if not type(self._purelocpot) is Locpot:
logger.debug('load pure locpot object')
self._purelocpot = Locpot.from_file(self._purelocpot)
if not type(self._deflocpot) is Locpot:
logger.debug('load defect locpot object')
self._deflocpot = Locpot.from_file(self._deflocpot)
#determine location of defects
blksite, defsite = find_defect_pos(self._purelocpot.structure,
self._deflocpot.structure,
defpos=self._defpos)
if blksite is None and defsite is None:
logger.error('Not able to determine defect site')
return
if blksite is None:
logger.debug('Found defect to be Interstitial type at %s',
repr(defsite))
elif defsite is None:
logger.debug('Found defect to be Vacancy type at %s',
repr(blksite))
else:
logger.debug(
'Found defect to be antisite/substitution type at '
'%s in bulk, and %s in defect cell', repr(blksite),
repr(defsite))
#It is important to do planar averaging at same position, otherwise
#you can get rigid shifts due to atomic changes at far away from defect
#note these are cartesian co-ordinate sites...
if defsite is None: #vacancies
self._pos = blksite
else: #all else, do w.r.t defect site
self._pos = defsite
x = np.array(self._purelocpot.get_axis_grid(axis)) #angstrom
nx = len(x)
logging.debug('run Freysoldt potential alignment method')
#perform potential alignment part
pureavg = self._purelocpot.get_average_along_axis(axis) #eV
defavg = self._deflocpot.get_average_along_axis(axis) #eV
#now shift these planar averages to have defect at origin
blklat = self._purelocpot.structure.lattice
axfracval = blklat.get_fractional_coords(self._pos)[axis]
axbulkval = axfracval * blklat.abc[axis]
if axbulkval < 0:
axbulkval += blklat.abc[axis]
elif axbulkval > blklat.abc[axis]:
axbulkval -= blklat.abc[axis]
if axbulkval:
for i in range(len(x)):
if axbulkval < x[i]:
break
rollind = len(x) - i
pureavg = np.roll(pureavg, rollind)
defavg = np.roll(defavg, rollind)
#if not self._silence:
logger.debug('calculating lr part along planar avg axis')
latt = self._purelocpot.structure.lattice
reci_latt = latt.reciprocal_lattice
dg = reci_latt.abc[axis]
dg /= ang_to_bohr #convert to bohr to do calculation in atomic units
v_G = np.empty(len(x), np.dtype('c16'))
epsilon = self._dielectricconst
# q needs to be that of the back ground
v_G[0] = 4 * np.pi * -self._q / epsilon * self._q_model.rho_rec_limit0(
)
for i in range(1, nx):
if (2 * i < nx):
g = i * dg
else:
g = (i - nx) * dg
g2 = g * g
v_G[i] = 4 * np.pi / (epsilon *
g2) * -self._q * self._q_model.rho_rec(g2)
if not (nx % 2):
v_G[nx // 2] = 0
v_R = np.fft.fft(v_G)
v_R_imag = np.imag(v_R)
v_R /= (latt.volume * ang_to_bohr**3)
v_R = np.real(v_R) * hart_to_ev
max_imag_vr = v_R_imag.max()
if abs(max_imag_vr) > self._madetol:
logging.error('imaginary part found to be %s', repr(max_imag_vr))
sys.exit()
#now get correction and do plots
short = (defavg - pureavg - v_R)
checkdis = int((widthsample / 2) / (x[1] - x[0]))
mid = int(len(short) / 2)
tmppot = [short[i] for i in range(mid - checkdis, mid + checkdis)]
logger.debug('shifted defect position on axis (%s) to origin',
repr(axbulkval))
logger.debug('means sampling region is (%f,%f)', x[mid - checkdis],
x[mid + checkdis])
C = -np.mean(tmppot)
logger.debug('C = %f', C)
final_shift = [short[j] + C for j in range(len(v_R))]
v_R = [elmnt - C for elmnt in v_R]
logger.info('C value is averaged to be %f eV ', C)
logger.info('Potentital alignment (-q*delta V): %f (eV)',
-self._q * C)
if title: # TODO: Make title optional and use a flag for plotting
plotter = FreysoldtCorrPlotter(
x, v_R, defavg - pureavg, final_shift,
np.array([mid - checkdis, mid + checkdis]))
if title != 'written':
plotter.plot(title=title)
else:
# TODO: Make this default fname more defect specific so it doesnt
# over write previous defect data written
fname = 'FreyAxisData' # Extension is npz
plotter.to_datafile(fname)
if output_sr:
return ((-self._q * C), tmppot) #pot align energy correction (eV)
else:
return (-self._q * C) #pot align energy correction (eV)
def plot_band_alignments(directories, run_type="PBE", fmt="pdf"):
"""
Plot CBM's and VBM's of all compounds together, relative to the band
edges of H2O.
Args:
directories (list): list of the directory paths for materials
to include in the plot.
run_type (str): 'PBE' or 'HSE', so that the function knows which
subdirectory to go into (pbe_bands or hse_bands).
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
if run_type == "HSE":
subdirectory = "hse_bands"
else:
subdirectory = "pbe_bands"
band_gaps = {}
for directory in directories:
if is_converged("{}/{}".format(directory, subdirectory)):
os.chdir("{}/{}".format(directory, subdirectory))
band_structure = Vasprun("vasprun.xml").get_band_structure()
band_gap = band_structure.get_band_gap()
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file("LOCPOT")
evac = max(locpot.get_average_along_axis(2))
try:
is_metal = False
is_direct = band_gap["direct"]
cbm = band_structure.get_cbm()
vbm = band_structure.get_vbm()
except AttributeError:
cbm = None
vbm = None
is_metal = True
is_direct = False
band_gaps[directory] = {"CBM": cbm, "VBM": vbm, "Direct": is_direct, "Metal": is_metal, "E_vac": evac}
os.chdir("../../")
ax = plt.figure(figsize=(16, 10)).gca()
x_max = len(band_gaps) * 1.315
ax.set_xlim(0, x_max)
# Rectangle representing band edges of water.
ax.add_patch(plt.Rectangle((0, -5.67), height=1.23, width=len(band_gaps), facecolor="#00cc99", linewidth=0))
ax.text(len(band_gaps) * 1.01, -4.44, r"$\mathrm{H+/H_2}$", size=20, verticalalignment="center")
ax.text(len(band_gaps) * 1.01, -5.67, r"$\mathrm{O_2/H_2O}$", size=20, verticalalignment="center")
x_ticklabels = []
y_min = -8
i = 0
# Nothing but lies.
are_directs, are_indirects, are_metals = False, False, False
for compound in [cpd for cpd in directories if cpd in band_gaps]:
x_ticklabels.append(compound)
# Plot all energies relative to their vacuum level.
evac = band_gaps[compound]["E_vac"]
if band_gaps[compound]["Metal"]:
cbm = -8
vbm = -2
else:
cbm = band_gaps[compound]["CBM"]["energy"] - evac
vbm = band_gaps[compound]["VBM"]["energy"] - evac
# Add a box around direct gap compounds to distinguish them.
if band_gaps[compound]["Direct"]:
are_directs = True
linewidth = 5
elif not band_gaps[compound]["Metal"]:
are_indirects = True
linewidth = 0
# Metals are grey.
if band_gaps[compound]["Metal"]:
are_metals = True
linewidth = 0
color_code = "#404040"
else:
color_code = "#002b80"
# CBM
ax.add_patch(
plt.Rectangle(
(i, cbm), height=-cbm, width=0.8, facecolor=color_code, linewidth=linewidth, edgecolor="#e68a00"
)
)
# VBM
ax.add_patch(
plt.Rectangle(
(i, y_min),
height=(vbm - y_min),
width=0.8,
facecolor=color_code,
linewidth=linewidth,
edgecolor="#e68a00",
)
)
i += 1
ax.set_ylim(y_min, -2)
# Set tick labels
ax.set_xticks([n + 0.4 for n in range(i)])
ax.set_xticklabels(x_ticklabels, family="serif", size=20, rotation=60)
ax.set_yticklabels(ax.get_yticks(), family="serif", size=20)
# Add a legend
height = y_min
if are_directs:
ax.add_patch(
plt.Rectangle(
(i * 1.165, height),
width=i * 0.15,
height=(-y_min * 0.1),
facecolor="#002b80",
edgecolor="#e68a00",
linewidth=5,
)
)
ax.text(
i * 1.24,
height - y_min * 0.05,
"Direct",
family="serif",
color="w",
size=20,
horizontalalignment="center",
verticalalignment="center",
)
height -= y_min * 0.15
if are_indirects:
ax.add_patch(
plt.Rectangle((i * 1.165, height), width=i * 0.15, height=(-y_min * 0.1), facecolor="#002b80", linewidth=0)
)
ax.text(
i * 1.24,
height - y_min * 0.05,
"Indirect",
family="serif",
size=20,
color="w",
horizontalalignment="center",
verticalalignment="center",
)
height -= y_min * 0.15
if are_metals:
ax.add_patch(
plt.Rectangle((i * 1.165, height), width=i * 0.15, height=(-y_min * 0.1), facecolor="#404040", linewidth=0)
)
ax.text(
i * 1.24,
height - y_min * 0.05,
"Metal",
family="serif",
size=20,
color="w",
horizontalalignment="center",
verticalalignment="center",
)
# Who needs axes?
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
ax.set_ylabel("eV", family="serif", size=24)
plt.savefig("band_alignments.{}".format(fmt), transparent=True)
plt.close()
def plot_band_alignments(directories, run_type='PBE', fmt='pdf'):
"""
Plot CBM's and VBM's of all compounds together, relative to the band
edges of H2O.
Args:
directories (list): list of the directory paths for materials
to include in the plot.
run_type (str): 'PBE' or 'HSE', so that the function knows which
subdirectory to go into (pbe_bands or hse_bands).
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
if run_type == 'HSE':
subdirectory = 'hse_bands'
else:
subdirectory = 'pbe_bands'
band_gaps = {}
for directory in directories:
sub_dir = os.path.join(directory, subdirectory)
if is_converged(sub_dir):
os.chdir(sub_dir)
band_structure = Vasprun('vasprun.xml').get_band_structure()
band_gap = band_structure.get_band_gap()
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
if not band_structure.is_metal():
is_direct = band_gap['direct']
cbm = band_structure.get_cbm()
vbm = band_structure.get_vbm()
else:
cbm = None
vbm = None
is_direct = False
band_gaps[directory] = {'CBM': cbm, 'VBM': vbm,
'Direct': is_direct,
'Metal': band_structure.is_metal(),
'E_vac': evac}
os.chdir('../../')
ax = plt.figure(figsize=(16, 10)).gca()
x_max = len(band_gaps) * 1.315
ax.set_xlim(0, x_max)
# Rectangle representing band edges of water.
ax.add_patch(plt.Rectangle((0, -5.67), height=1.23, width=len(band_gaps),
facecolor='#00cc99', linewidth=0))
ax.text(len(band_gaps) * 1.01, -4.44, r'$\mathrm{H+/H_2}$', size=20,
verticalalignment='center')
ax.text(len(band_gaps) * 1.01, -5.67, r'$\mathrm{O_2/H_2O}$', size=20,
verticalalignment='center')
x_ticklabels = []
y_min = -8
i = 0
# Nothing but lies.
are_directs, are_indirects, are_metals = False, False, False
for compound in [cpd for cpd in directories if cpd in band_gaps]:
x_ticklabels.append(compound)
# Plot all energies relative to their vacuum level.
evac = band_gaps[compound]['E_vac']
if band_gaps[compound]['Metal']:
cbm = -8
vbm = -2
else:
cbm = band_gaps[compound]['CBM']['energy'] - evac
vbm = band_gaps[compound]['VBM']['energy'] - evac
# Add a box around direct gap compounds to distinguish them.
if band_gaps[compound]['Direct']:
are_directs = True
linewidth = 5
elif not band_gaps[compound]['Metal']:
are_indirects = True
linewidth = 0
# Metals are grey.
if band_gaps[compound]['Metal']:
are_metals = True
linewidth = 0
color_code = '#404040'
else:
color_code = '#002b80'
# CBM
ax.add_patch(plt.Rectangle((i, cbm), height=-cbm, width=0.8,
facecolor=color_code, linewidth=linewidth,
edgecolor="#e68a00"))
# VBM
ax.add_patch(plt.Rectangle((i, y_min),
height=(vbm - y_min), width=0.8,
facecolor=color_code, linewidth=linewidth,
edgecolor="#e68a00"))
i += 1
ax.set_ylim(y_min, 0)
# Set tick labels
ax.set_xticks([n + 0.4 for n in range(i)])
ax.set_xticklabels(x_ticklabels, family='serif', size=20, rotation=60)
ax.set_yticklabels(ax.get_yticks(), family='serif', size=20)
# Add a legend
height = y_min
if are_directs:
ax.add_patch(plt.Rectangle((i*1.165, height), width=i*0.15,
height=(-y_min*0.1), facecolor='#002b80',
edgecolor='#e68a00', linewidth=5))
ax.text(i*1.24, height - y_min * 0.05, 'Direct', family='serif',
color='w', size=20, horizontalalignment='center',
verticalalignment='center')
height -= y_min * 0.15
if are_indirects:
ax.add_patch(plt.Rectangle((i*1.165, height), width=i*0.15,
height=(-y_min*0.1), facecolor='#002b80',
linewidth=0))
ax.text(i*1.24, height - y_min * 0.05, 'Indirect', family='serif',
size=20, color='w', horizontalalignment='center',
verticalalignment='center')
height -= y_min * 0.15
if are_metals:
ax.add_patch(plt.Rectangle((i*1.165, height), width=i*0.15,
height=(-y_min*0.1), facecolor='#404040',
linewidth=0))
ax.text(i*1.24, height - y_min * 0.05, 'Metal', family='serif',
size=20, color='w', horizontalalignment='center',
verticalalignment='center')
# Who needs axes?
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_ylabel('eV', family='serif', size=24)
if fmt == "None":
return ax
else:
plt.savefig('band_alignments.{}'.format(fmt), transparent=True)
plt.close()
def plot_band_alignments(directories, run_type='PBE', fmt='pdf'):
"""
Plot CBM's and VBM's of all compounds together, relative to the band
edges of H2O.
Args:
directories (list): list of the directory paths for materials
to include in the plot.
run_type (str): 'PBE' or 'HSE', so that the function knows which
subdirectory to go into (pbe_bands or hse_bands).
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
if run_type == 'HSE':
subdirectory = 'hse_bands'
else:
subdirectory = 'pbe_bands'
band_gaps = {}
for directory in directories:
sub_dir = os.path.join(directory, subdirectory)
if is_converged(sub_dir):
os.chdir(sub_dir)
band_structure = Vasprun('vasprun.xml').get_band_structure()
band_gap = band_structure.get_band_gap()
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
if not band_structure.is_metal():
is_direct = band_gap['direct']
cbm = band_structure.get_cbm()
vbm = band_structure.get_vbm()
else:
cbm = None
vbm = None
is_direct = False
band_gaps[directory] = {
'CBM': cbm,
'VBM': vbm,
'Direct': is_direct,
'Metal': band_structure.is_metal(),
'E_vac': evac
}
os.chdir('../../')
ax = plt.figure(figsize=(16, 10)).gca()
x_max = len(band_gaps) * 1.315
ax.set_xlim(0, x_max)
# Rectangle representing band edges of water.
ax.add_patch(
plt.Rectangle((0, -5.67),
height=1.23,
width=len(band_gaps),
facecolor='#00cc99',
linewidth=0))
ax.text(len(band_gaps) * 1.01,
-4.44,
r'$\mathrm{H+/H_2}$',
size=20,
verticalalignment='center')
ax.text(len(band_gaps) * 1.01,
-5.67,
r'$\mathrm{O_2/H_2O}$',
size=20,
verticalalignment='center')
x_ticklabels = []
y_min = -8
i = 0
# Nothing but lies.
are_directs, are_indirects, are_metals = False, False, False
for compound in [cpd for cpd in directories if cpd in band_gaps]:
x_ticklabels.append(compound)
# Plot all energies relative to their vacuum level.
evac = band_gaps[compound]['E_vac']
if band_gaps[compound]['Metal']:
cbm = -8
vbm = -2
else:
cbm = band_gaps[compound]['CBM']['energy'] - evac
vbm = band_gaps[compound]['VBM']['energy'] - evac
# Add a box around direct gap compounds to distinguish them.
if band_gaps[compound]['Direct']:
are_directs = True
linewidth = 5
elif not band_gaps[compound]['Metal']:
are_indirects = True
linewidth = 0
# Metals are grey.
if band_gaps[compound]['Metal']:
are_metals = True
linewidth = 0
color_code = '#404040'
else:
color_code = '#002b80'
# CBM
ax.add_patch(
plt.Rectangle((i, cbm),
height=-cbm,
width=0.8,
facecolor=color_code,
linewidth=linewidth,
edgecolor="#e68a00"))
# VBM
ax.add_patch(
plt.Rectangle((i, y_min),
height=(vbm - y_min),
width=0.8,
facecolor=color_code,
linewidth=linewidth,
edgecolor="#e68a00"))
i += 1
ax.set_ylim(y_min, 0)
# Set tick labels
ax.set_xticks([n + 0.4 for n in range(i)])
ax.set_xticklabels(x_ticklabels, family='serif', size=20, rotation=60)
ax.set_yticklabels(ax.get_yticks(), family='serif', size=20)
# Add a legend
height = y_min
if are_directs:
ax.add_patch(
plt.Rectangle((i * 1.165, height),
width=i * 0.15,
height=(-y_min * 0.1),
facecolor='#002b80',
edgecolor='#e68a00',
linewidth=5))
ax.text(i * 1.24,
height - y_min * 0.05,
'Direct',
family='serif',
color='w',
size=20,
horizontalalignment='center',
verticalalignment='center')
height -= y_min * 0.15
if are_indirects:
ax.add_patch(
plt.Rectangle((i * 1.165, height),
width=i * 0.15,
height=(-y_min * 0.1),
facecolor='#002b80',
linewidth=0))
ax.text(i * 1.24,
height - y_min * 0.05,
'Indirect',
family='serif',
size=20,
color='w',
horizontalalignment='center',
verticalalignment='center')
height -= y_min * 0.15
if are_metals:
ax.add_patch(
plt.Rectangle((i * 1.165, height),
width=i * 0.15,
height=(-y_min * 0.1),
facecolor='#404040',
linewidth=0))
ax.text(i * 1.24,
height - y_min * 0.05,
'Metal',
family='serif',
size=20,
color='w',
horizontalalignment='center',
verticalalignment='center')
# Who needs axes?
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_ylabel('eV', family='serif', size=24)
if fmt == "None":
return ax
else:
plt.savefig('band_alignments.{}'.format(fmt), transparent=True)
plt.close()