#print "coordination number = 2, e^hull < 50meV/atom"
#for ic in cursor:
# if ic["c_num"] == 2 and ic["ehull"] <0.05:
# print mpr.get_entry_by_material_id(ic["task_id"]).composition.reduced_formula
# print "https://materialsproject.org/materials/"+ic["task_id"]+"/"
#cursor = cddb.find()
#print "coordination number = 3, e^hull < 50meV/atom"
#for ic in cursor:
# if ic["c_num"] == 3 and ic["ehull"] <0.05:
# print mpr.get_entry_by_material_id(ic["task_id"]).composition.reduced_formula
# print "https://materialsproject.org/materials/"+ic["task_id"]+"/"
cursor = cddb.find()
print "coordination number = 5, e^hull < 50meV/atom"
for ic in cursor:
if ic["c_num"] == 5 and ic["ehull"] < 0.05:
print mpr.get_entry_by_material_id(
ic["task_id"]).composition.reduced_formula
print "https://materialsproject.org/materials/" + ic["task_id"] + "/"
cursor = cddb.find()
print "coordination number = 8, e^hull < 50meV/atom"
for ic in cursor:
if ic["c_num"] == 8 and ic["ehull"] < 0.05:
print mpr.get_entry_by_material_id(
ic["task_id"]).composition.reduced_formula
print "https://materialsproject.org/materials/" + ic["task_id"] + "/"
'''
for c in cursor:
#if c["c_num"]== 3:
# plt.errorbar(c["c_dis"], c["ehull"], xerr=c["c_dis_"], yerr=0.0,color="blue",fmt="-o",alpha=0.5)
#if c["c_num"]== 4:
# plt.errorbar(c["c_dis"], c["ehull"], xerr=c["c_dis_"], yerr=0.0,linewidth=1,color="green",ms= 2,marker="o",alpha=0.1)
#if c["c_num"]== 5:
# This initializes the REST adaptor. Put your own API key in.
from pymatgen import MPRester
from pymatgen.analysis.phase_diagram import PhaseDiagram, PDPlotter
from pymatgen.io.vasp import Poscar
a = MPRester("")
# Entries are the basic unit for thermodynamic and other analyses in pymatgen.
# This gets all entries belonging to the mp-48 material.
entry = a.get_entry_by_material_id(
"mp-48",
inc_structure=True,
property_data=["material_id", "energy", "energy_per_atom", "volume"])
#print(entry)
structure = entry.structure
#print(structure)
p = Poscar(structure)
print(p)
INCAR['LDAUJ'] = [0.0, 0.0, 0.0]
elements = [str(element) for element in conv_struc.species][:-2]
for index, element in enumerate(elements):
if element in U_elements:
print(file_path)
INCAR['LDAU'] = '.TRUE.'
INCAR['LDAUL'][index] = 2
INCAR['LDAUU'][index] = U_dict[element]
INCAR.write_file('%03d_%s/INCAR' % (idx + 1.0, formula))
KPOINTS.write_file('%03d_%s/KPOINTS' % (idx + 1.0, formula))
# Potcar setup
mp_calc = mpr.get_entry_by_material_id({'material_id': mp_id})
mp_potcar_symbols = mp_calc.parameters['potcar_symbols']
for i in range(len(mp_potcar_symbols)):
mp_potcar_symbols[i] = mp_potcar_symbols[i].replace("PBE ", "")
if 'W_pv' in mp_potcar_symbols:
mp_potcar_symbols[1] = 'W_sv'
POTCAR = Potcar(mp_potcar_symbols)
POTCAR.write_file(file_path + 'POTCAR')
print('%02d' % (idx + 1.0), " ", formula, " ", conv_struc.formula, " ",
len(conv_struc), " %4.3f %4.3f %4.3f" % (conv_struc.lattice.abc),
" ", mp_potcar_symbols, " ", INCAR['LDAUU'],
" %s" % INCAR['MAGMOM'])
f.writelines([
class SurfaceEnergyAnalyzer(object):
"""
A class used for analyzing the surface energies of a material of a given
material_id. By default, this will use entries calculated from the
Materials Project to obtain chemical potential and bulk energy. As a
result, the difference in VASP parameters between the user's entry
(vasprun_dict) and the parameters used by Materials Project, may lead
to a rough estimate of the surface energy. For best results, it is
recommend that the user calculates all decomposition components first,
and insert the results into their own database as a pymatgen-db entry
and use those entries instead (custom_entries). In addition, this code
will only use one bulk entry to calculate surface energy. Ideally, to
get the most accurate surface energy, the user should compare their
slab energy to the energy of the oriented unit cell with both calculations
containing consistent k-points to avoid converegence problems as the
slab size is varied. See:
Sun, W.; Ceder, G. Efficient creation and convergence of surface slabs,
Surface Science, 2013, 617, 53–59, doi:10.1016/j.susc.2013.05.016.
and
Rogal, J., & Reuter, K. (2007). Ab Initio Atomistic Thermodynamics for
Surfaces : A Primer. Experiment, Modeling and Simulation of Gas-Surface
Interactions for Reactive Flows in Hypersonic Flights, 2–1 – 2–18.
.. attribute:: ref_element
All chemical potentials cna be written in terms of the range of chemical
potential of this element which will be used to calculate surface energy.
.. attribute:: mprester
Materials project rester for querying entries from the materials project.
Requires user MAPIKEY.
.. attribute:: ucell_entry
Materials Project entry of the material of the slab.
.. attribute:: x
Reduced amount composition of decomposed compound A in the bulk.
.. attribute:: y
Reduced amount composition of ref_element in the bulk.
.. attribute:: gbulk
Gibbs free energy of the bulk per formula unit
.. attribute:: chempot_range
List of the min and max chemical potential of ref_element.
.. attribute:: e_of_element
Energy per atom of ground state ref_element, eg. if ref_element=O,
than e_of_element=1/2*E_O2.
.. attribute:: vasprun_dict
Dictionary containing a list of Vaspruns for slab calculations as
items and the corresponding Miller index of the slab as the key
"""
def __init__(self, material_id, vasprun_dict, ref_element,
exclude_ids=[], custom_entries=[], mapi_key=None):
"""
Analyzes surface energies and Wulff shape of a particular
material using the chemical potential.
Args:
material_id (str): Materials Project material_id (a string,
e.g., mp-1234).
vasprun_dict (dict): Dictionary containing a list of Vaspruns
for slab calculations as items and the corresponding Miller
index of the slab as the key.
eg. vasprun_dict = {(1,1,1): [vasprun_111_1, vasprun_111_2,
vasprun_111_3], (1,1,0): [vasprun_111_1, vasprun_111_2], ...}
element: element to be considered as independent
variables. E.g., if you want to show the stability
ranges of all Li-Co-O phases wrt to uLi
exclude_ids (list of material_ids): List of material_ids
to exclude when obtaining the decomposition components
to calculate the chemical potential
custom_entries (list of pymatgen-db type entries): List of
user specified pymatgen-db type entries to use in finding
decomposition components for the chemical potential
mapi_key (str): Materials Project API key for accessing the
MP database via MPRester
"""
self.ref_element = ref_element
self.mprester = MPRester(mapi_key) if mapi_key else MPRester()
self.ucell_entry = \
self.mprester.get_entry_by_material_id(material_id,
inc_structure=True,
property_data=
["formation_energy_per_atom"])
ucell = self.ucell_entry.structure
# Get x and y, the number of species in a formula unit of the bulk
reduced_comp = ucell.composition.reduced_composition.as_dict()
if len(reduced_comp.keys()) == 1:
x = y = reduced_comp[ucell[0].species_string]
else:
for el in reduced_comp.keys():
if self.ref_element == el:
y = reduced_comp[el]
else:
x = reduced_comp[el]
# Calculate Gibbs free energy of the bulk per unit formula
gbulk = self.ucell_entry.energy /\
(len([site for site in ucell
if site.species_string == self.ref_element]) / y)
entries = [entry for entry in
self.mprester.get_entries_in_chemsys(list(reduced_comp.keys()),
property_data=["e_above_hull",
"material_id"])
if entry.data["e_above_hull"] == 0 and
entry.data["material_id"] not in exclude_ids] \
if not custom_entries else custom_entries
pd = PhaseDiagram(entries)
chempot_ranges = pd.get_chempot_range_map([Element(self.ref_element)])
# If no chemical potential is found, we return u=0, eg.
# for a elemental system, the relative u of Cu for Cu is 0
chempot_range = [chempot_ranges[entry] for entry in chempot_ranges.keys()
if entry.composition ==
self.ucell_entry.composition][0][0]._coords if \
chempot_ranges else [[0,0], [0,0]]
e_of_element = [entry.energy_per_atom for entry in
entries if str(entry.composition.reduced_composition)
== self.ref_element + "1"][0]
self.x = x
self.y = y
self.gbulk = gbulk
chempot_range = list(chempot_range)
self.chempot_range = sorted([chempot_range[0][0], chempot_range[1][0]])
self.e_of_element = e_of_element
self.vasprun_dict = vasprun_dict
def calculate_gamma(self, vasprun):
"""
Calculates the surface energy for a single slab.
Args:
vasprun (Vasprun): A Vasprun object
Returns (list): The surface energy for the minimum/maximun
chemical potential and the second list gives the range
of the chemical potential
"""
reduced_comp = self.ucell_entry.composition.reduced_composition.as_dict()
# Get the composition in the slab
slab = vasprun.final_structure
comp = slab.composition.as_dict()
if len(reduced_comp.keys()) == 1:
Ny = comp[self.ucell_entry.structure[0].species_string]
Nx = Ny
else:
for el in reduced_comp.keys():
if self.ref_element == el:
Ny = comp[el]
else:
Nx = comp[el]
# Calculate surface area
m = slab.lattice.matrix
A = np.linalg.norm(np.cross(m[0], m[1]))
# calculate the surface energy for the max and min chemical potential
return [(1 / (2 * A)) * (vasprun.final_energy - (Nx / self.x)
* self.gbulk - (Ny - (self.y / self.x) * Nx)
* (delu + self.e_of_element))
for delu in self.chempot_range]
def wulff_shape_from_chempot(self, chempot, symprec=1e-5):
"""
Method to get the Wulff shape at a specific chemical potential.
Args:
chempot (float): The chemical potential the Wulff Shape exist in.
"""
# Check if the user provided chemical potential is within the
# predetermine range of chemical potential. If not, raise a warning
if not max(self.chempot_range) >= chempot >= min(self.chempot_range):
warnings.warn("The provided chemical potential is outside the range "
"of chemical potential (%s to %s). The resulting Wulff "
"shape might not be reasonable." %(min(self.chempot_range),
max(self.chempot_range)))
latt = SpacegroupAnalyzer(self.ucell_entry.structure).\
get_conventional_standard_structure().lattice
miller_list = self.vasprun_dict.keys()
e_surf_list = []
for hkl in miller_list:
# At each possible configuration, we calculate surface energy as a
# function of u and take the lowest surface energy (corresponds to
# the most stable slab termination at that particular u)
surf_e_range_list = [self.calculate_gamma(vasprun)
for vasprun in self.vasprun_dict[hkl]]
e_list = []
for e_range in surf_e_range_list:
slope, intercept = self.get_slope_and_intercept(e_range)
e_list.append(slope * chempot + intercept)
e_surf_list.append(min(e_list))
return WulffShape(latt, miller_list, e_surf_list, symprec=symprec)
def wulff_shape_dict(self, symprec=1e-5, at_intersections=False):
"""
As the surface energy is a function of chemical potential, so too is the
Wulff shape. This methods generates a dictionary of Wulff shapes at
certain chemical potentials where a facet goes through a transition.
Returns a dict, eg. {chempot1: WulffShape1, chempot2: WulffShape2}
Args:
symprec (float): for recp_operation, default is 1e-5.
at_intersections (bool): Whether to generate a Wulff shape for each
intersection of surface energy for a specific facet (eg. at the
point where a (111) stoichiometric surface energy plot intersects
with the (111) nonstoichiometric plot) or to just generate two
Wulff shapes, one at the min and max chemical potential.
"""
# First lets get the Wulff shape at the
# minimum and maximum chemical potential
wulff_dict = {self.chempot_range[0]: \
self.wulff_shape_from_chempot(self.chempot_range[0],
symprec=symprec),
self.chempot_range[1]: \
self.wulff_shape_from_chempot(self.chempot_range[1],
symprec=symprec)}
# Now we get the Wulff shape each time a facet changes its configuration
# (ie, adsorption coverage, stoichiometric to nonstoichiometric, etc)
if at_intersections:
# Get all values of chemical potential where an intersection occurs
u_at_intersection = [self.get_intersections(hkl)[0] for hkl in
self.vasprun_dict.keys()
if self.get_intersections(hkl)]
# Get a Wulff shape for each intersection. The change in the Wulff shape
# will vary if the rate of change in surface energy for any facet changes
for u in u_at_intersection:
wulff_dict[u] = self.wulff_shape_from_chempot(u, symprec=symprec)
return wulff_dict
def get_slope_and_intercept(self, surf_e_pair):
"""
Returns the slope and intercept of the surface
energy vs chemical potential line
Args:
surf_e_pair ([e_at_min_u, e_at_max_u]): The surface energy at the
minimum chemical potential and maximum chemical potential
"""
slope, intercept, r_value, p_value, std_err = \
linregress(self.chempot_range, surf_e_pair)
slope = 0 if str(slope) == 'nan' else slope
intercept = surf_e_pair[0] if str(intercept) == 'nan' else intercept
return slope, intercept
def get_intersections(self, miller_index):
"""
Returns a all intersections for a specific facet. Useful for
finding when the configuration of a particular facet changes.
Args:
miller_index ((h, k, l)): Miller index of the facet we
are interested in
"""
# First lets calculate the range of surface
# energies for all terminations of a specific facet
all_se_ranges = [self.calculate_gamma(vasprun) for vasprun
in self.vasprun_dict[miller_index]]
if len(all_se_ranges) == 1:
return []
# Now get all possible intersection coordinates for each pair of lines
intersections = []
for pair_ranges in itertools.combinations(all_se_ranges, 2):
slope1, intercept1 = self.get_slope_and_intercept(pair_ranges[0])
slope2, intercept2 = self.get_slope_and_intercept(pair_ranges[1])
# Calculate the intersection coordinates
u = (intercept1-intercept2)/(slope2-slope1)
# if the intersection is beyond the chemical potential
# range or if the lines are parallel, we ignore it
if slope1-slope2 == 0 or u < min(self.chempot_range) \
or u > max(self.chempot_range):
continue
intersections.append([u, slope1 * u + intercept1])
return sorted(intersections, key=lambda ints: ints[0])
def area_frac_vs_chempot_plot(self, cmap=cm.jet, at_intersections=False,
increments=10):
"""
Plots the change in the area contribution of
each facet as a function of chemical potential.
Args:
cmap (cm): A matplotlib colormap object, defaults to jet.
at_intersections (bool): Whether to generate a Wulff shape for each
intersection of surface energy for a specific facet (eg. at the
point where a (111) stoichiometric surface energy plot intersects
with the (111) nonstoichiometric plot) or to just generate two
Wulff shapes, one at the min and max chemical potential.
increments (bool): Number of data points between min/max or point
of intersection. Defaults to 5 points.
"""
# Choose unique colors for each facet
f = [int(i) for i in np.linspace(0, 255, len(self.vasprun_dict.keys()))]
# Get all points of min/max chempot and intersections
chempot_intersections = []
chempot_intersections.extend(self.chempot_range)
for hkl in self.vasprun_dict.keys():
chempot_intersections.extend([ints[0] for ints in
self.get_intersections(hkl)])
chempot_intersections = sorted(chempot_intersections)
# Get all chempots
if at_intersections:
all_chempots = []
for i, intersection in enumerate(chempot_intersections):
if i < len(chempot_intersections)-1:
all_chempots.extend(np.linspace(intersection,
chempot_intersections[i+1],
increments))
else:
all_chempots = np.linspace(min(self.chempot_range),
max(self.chempot_range), increments)
# initialize a dictionary of lists of fractional areas for each hkl
hkl_area_dict = {}
for hkl in self.vasprun_dict.keys():
hkl_area_dict[hkl] = []
# Get plot points for each Miller index
for u in all_chempots:
wulffshape = self.wulff_shape_from_chempot(u)
for hkl in wulffshape.area_fraction_dict.keys():
hkl_area_dict[hkl].append(wulffshape.area_fraction_dict[hkl])
# Plot the area fraction vs chemical potential for each facet
plt = pretty_plot()
for i, hkl in enumerate(self.vasprun_dict.keys()):
# Ignore any facets that never show up on the
# Wulff shape regardless of chemical potential
if all([a == 0 for a in hkl_area_dict[hkl]]):
continue
else:
plt.plot(all_chempots, hkl_area_dict[hkl],
'--', color=cmap(f[i]), label=str(hkl))
# Make the figure look nice
plt.ylim([0,1])
plt.xlim(self.chempot_range)
plt.ylabel(r"Fractional area $A^{Wulff}_{hkl}/A^{Wulff}$")
plt.xlabel(r"Chemical potential $\Delta\mu_{%s}$ (eV)" %(self.ref_element))
plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
return plt
def chempot_vs_gamma_plot(self, cmap=cm.jet, show_unstable_points=False):
"""
Plots the surface energy of all facets as a function of chemical potential.
Each facet will be associated with its own distinct colors. Dashed lines
will represent stoichiometries different from that of the mpid's compound.
Args:
cmap (cm): A matplotlib colormap object, defaults to jet.
show_unstable_points (bool): For each facet, there may be various
terminations or stoichiometries and the relative stability of
these different slabs may change with chemical potential. This
option will only plot the most stable surface energy for a
given chemical potential.
"""
plt = pretty_plot()
# Choose unique colors for each facet
f = [int(i) for i in np.linspace(0, 255, sum([len(vaspruns) for vaspruns in
self.vasprun_dict.values()]))]
i, already_labelled, colors = 0, [], []
for hkl in self.vasprun_dict.keys():
for vasprun in self.vasprun_dict[hkl]:
slab = vasprun.final_structure
# Generate a label for the type of slab
label = str(hkl)
# use dashed lines for slabs that are not stoichiometric
# wrt bulk. Label with formula if nonstoichiometric
if slab.composition.reduced_composition != \
self.ucell_entry.composition.reduced_composition:
mark = '--'
label += " %s" % (slab.composition.reduced_composition)
else:
mark = '-'
# label the chemical environment at the surface if different from the bulk.
# First get the surface sites, then get the reduced composition at the surface
# s = vasprun.final_structure
# ucell = SpacegroupAnalyzer(self.ucell_entry.structure).\
# get_conventional_standard_structure()
# slab = Slab(s.lattice, s.species, s.frac_coords, hkl, ucell, 0, None)
# surf_comp = slab.surface_composition()
#
# if surf_comp.reduced_composition != ucell.composition.reduced_composition:
# label += " %s" %(surf_comp.reduced_composition)
if label in already_labelled:
c = colors[already_labelled.index(label)]
label = None
else:
already_labelled.append(label)
c = cmap(f[i])
colors.append(c)
se_range = self.calculate_gamma(vasprun)
plt.plot(self.chempot_range, se_range, mark, color=c, label=label)
i += 1
# Make the figure look nice
axes = plt.gca()
ylim = axes.get_ylim()
plt.ylim(ylim)
plt.xlim(self.chempot_range)
plt.ylabel(r"Surface energy (eV/$\AA$)")
plt.xlabel(r"Chemical potential $\Delta\mu_{%s}$ (eV)" %(self.ref_element))
plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
return plt
def broken_bond_vs_gamma(self):
return
#print "coordination number = 2, e^hull < 50meV/atom"
#for ic in cursor:
# if ic["c_num"] == 2 and ic["ehull"] <0.05:
# print mpr.get_entry_by_material_id(ic["task_id"]).composition.reduced_formula
# print "https://materialsproject.org/materials/"+ic["task_id"]+"/"
#cursor = cddb.find()
#print "coordination number = 3, e^hull < 50meV/atom"
#for ic in cursor:
# if ic["c_num"] == 3 and ic["ehull"] <0.05:
# print mpr.get_entry_by_material_id(ic["task_id"]).composition.reduced_formula
# print "https://materialsproject.org/materials/"+ic["task_id"]+"/"
cursor = cddb.find()
print "coordination number = 5, e^hull < 50meV/atom"
for ic in cursor:
if ic["c_num"] == 5 and ic["ehull"] <0.05:
print mpr.get_entry_by_material_id(ic["task_id"]).composition.reduced_formula
print "https://materialsproject.org/materials/"+ic["task_id"]+"/"
cursor = cddb.find()
print "coordination number = 8, e^hull < 50meV/atom"
for ic in cursor:
if ic["c_num"] == 8 and ic["ehull"] <0.05:
print mpr.get_entry_by_material_id(ic["task_id"]).composition.reduced_formula
print "https://materialsproject.org/materials/"+ic["task_id"]+"/"
'''
for c in cursor:
#if c["c_num"]== 3:
# plt.errorbar(c["c_dis"], c["ehull"], xerr=c["c_dis_"], yerr=0.0,color="blue",fmt="-o",alpha=0.5)
#if c["c_num"]== 4:
# plt.errorbar(c["c_dis"], c["ehull"], xerr=c["c_dis_"], yerr=0.0,linewidth=1,color="green",ms= 2,marker="o",alpha=0.1)
#if c["c_num"]== 5:
