def evaluate_phases(data, bulk, x, y, nphases, x_energy, y_energy, mu_z, exp_x,
exp_z):
"""Calculates the surface energies of each phase as a function of chemical
potential of x and y. Then uses this data to evaluate which phase is most
stable at that x/y chemical potential cross section.
Parameters
----------
data : :py:attr:`list`
List containing the :py:class:`surfinpy.data.DataSet` objects for each phase
bulk : :py:class:`surfinpy.data.ReferenceDataSet`
Reference dataset
x : :py:attr:`dict`
X axis chemical potential values
y : :py:attr:`dict`
Y axis chemical potential values
nphases : :py:attr:`int`
Number of phases
x_energy : :py:attr:`float`
DFT 0K energy for species x
y_energy : :py:attr:`float`
DFT 0K energy for species y
mu_z : :py:attr:`float`
Set chemical potential for species y
exp_x : :py:attr:`float`
Experimental correction for species x
exp_z : :py:attr:`float`
Experimental correction for species y
Returns
-------
phase_data : :py:attr:`array_like`
array of ints, with each int corresponding to a phase.
"""
xnew = ut.build_xgrid(x, y)
ynew = ut.build_ygrid(x, y)
znew = (xnew * 0) + mu_z
exp_xnew = ut.build_zgrid(exp_x, x)
exp_znew = ut.build_zgrid(exp_z, x)
S = np.array([])
new_data_svib = 0
new_bulk_svib = 0
if bulk.entropy:
new_bulk_svib = ut.build_zgrid(bulk.avib, x)
for k in range(0, nphases):
if data[k].entropy:
new_data_svib = ut.build_zgrid(data[k].avib, x)
normalised_bulk = normalise_phase_energy(data[k], bulk)
SE = calculate_bulk_energy(xnew, ynew, x_energy, y_energy, znew,
data[k], bulk, normalised_bulk, exp_xnew,
exp_znew, new_bulk_svib, new_data_svib)
S = np.append(S, SE)
phase_data, SE = ut.get_phase_data(S, nphases)
return phase_data, SE
def calculate_surface_energy(AE, lnP, T, coverage, SE, nsurfaces):
r"""Calculates the surface energy as a function of pressure and
temperature for each surface system according to
.. math::
\gamma_{adsorbed, T, p} = \gamma_{bare} + (C(E_{ads, T} -
RTln(\frac{p}{p^o})
where :math:`\gamma_{adsorbed, T, p}` is the surface energy of the surface
with adsorbed species at a given temperature and pressure,
:math:`\gamma_{bare}` is the suface energy of the bare surface,
C is the coverage of adsorbed species, :math:`E_{ads, T}` is the
adsorption energy, R is the gas constant, T is the temperature, and
:math:`\frac{p}{p^o}` is the partial pressure.
Parameters
----------
AE : list
list of adsorption energies
lnP : array like
full pressure range
T : array like
full temperature range
coverage : array like
surface coverage of adsorbing species in each calculation
SE : float
surface energy of stoichiomteric surface
data : list
list of dictionaries containing info on each surface
nsurfaces : int
total number of surface
Returns
-------
SE_array : array like
array of integers corresponding to lowest surface energies
"""
R = codata.value('molar gas constant')
N_A = codata.value('Avogadro constant')
SEABS = np.array([])
xnew = ut.build_xgrid(T, lnP)
ynew = ut.build_ygrid(T, lnP)
for i in range(0, (nsurfaces - 1)):
SE_Abs_1 = (SE + (coverage[i] / N_A) * (AE[i] - (ynew * (xnew * R))))
SEABS = np.append(SEABS, SE_Abs_1)
surface_energy_array = np.zeros(lnP.size * T.size)
surface_energy_array = surface_energy_array + SE
SEABS = np.insert(SEABS, 0, surface_energy_array)
phase_data, SE = ut.get_phase_data(SEABS, nsurfaces)
return phase_data, SE
def evaluate_phases(data, bulk, x, y, nsurfaces, x_energy, y_energy):
"""Calculates the surface energies of each phase as a function of chemical
potential of x and y. Then uses this data to evaluate which phase is most
stable at that x/y chemical potential cross section.
Parameters
----------
data : list
List containing the dictionaries for each phase
bulk : dictionary
dictionary containing data for bulk
x : dictionary
X axis chemical potential values
y : dictionary
Y axis chemical potential values
nsurfaces : int
Number of phases
x_energy : float
DFT 0K energy for species x
y_energy : float
DFT 0K energy for species y
Returns
-------
phase_data : array like
array of ints, with each int corresponding to a phase.
"""
xnew = ut.build_xgrid(x, y)
ynew = ut.build_ygrid(x, y)
S = np.array([])
for k in range(0, nsurfaces):
xexcess = calculate_excess(data[k]['X'], data[k]['Cation'],
data[k]['Area'], bulk,
data[k]['nSpecies'], check=True)
yexcess = calculate_excess(data[k]['Y'], data[k]['Cation'],
data[k]['Area'], bulk)
normalised_bulk = calculate_normalisation(data[k]['Energy'],
data[k]['Cation'], bulk,
data[k]['Area'])
SE = calculate_surface_energy(xnew, ynew,
x_energy,
y_energy,
xexcess,
yexcess,
normalised_bulk)
S = np.append(S, SE)
phase_data = ut.get_phase_data(S, nsurfaces)
return phase_data
def evaluate_phases(data, bulk, x, y, nsurfaces, x_energy, y_energy):
"""Calculates the surface energies of each phase as a function of chemical
potential of x and y. Then uses this data to evaluate which phase is most
stable at that x/y chemical potential cross section.
Parameters
----------
data : :py:attr:`list`
List containing the :py:class:`surfinpy.data.DataSet` for each phase
bulk : :py:class:`surfinpy.data.DataSet`
Data for bulk
x : :py:attr:`dict`
X axis chemical potential values
y : :py:attr:`dict`
Y axis chemical potential values
nsurfaces : :py:attr:`int`
Number of phases
x_energy : :py:attr:`float`
DFT 0K energy for species x
y_energy : :py:attr:`float`
DFT 0K energy for species y
Returns
-------
phase_data : :py:attr:`array_like`
array of ints, with each int corresponding to a phase.
"""
xnew = ut.build_xgrid(x, y)
ynew = ut.build_ygrid(x, y)
S = np.array([])
for k in range(0, nsurfaces):
xexcess = calculate_excess(data[k].x, data[k].cation,
data[k].area, bulk,
data[k].nspecies, check=True)
yexcess = calculate_excess(data[k].y, data[k].cation,
data[k].area, bulk)
normalised_bulk = calculate_normalisation(data[k].energy,
data[k].cation, bulk,
data[k].area)
SE = calculate_surface_energy(xnew, ynew,
x_energy,
y_energy,
xexcess,
yexcess,
normalised_bulk)
S = np.append(S, SE)
phase_data, surface_energy = ut.get_phase_data(S, nsurfaces)
return phase_data, surface_energy
def evaluate_phases(data, bulk, x, y, nphases, x_energy, y_energy):
"""Calculates the free energies of each phase as a function of chemical
potential of x and y. Then uses this data to evaluate which phase is most
stable at that x/y chemical potential cross section.
Parameters
----------
data : :py:attr:`list`
List of :py:class:`surfinpy.data.DataSet` objects
bulk : :py:class:`surfinpy.data.ReferenceDataSet` object
Reference dataset
x : :py:attr:`dict`
X axis chemical potential values
y : :py:attr:`dict`
Y axis chemical potential values
nphases : :py:attr:`int`
Number of phases
x_energy : :py:attr:`float`
DFT 0 K energy for species x
y_energy : :py:attr:`float`
DFT 0 K energy for species y
Returns
-------
phase_data : :py:attr:`array_like`
array of ints, with each int corresponding to a phase.
"""
xnew = ut.build_xgrid(x, y)
ynew = ut.build_ygrid(x, y)
S = np.array([])
for k in range(0, nphases):
normalised_bulk = normalise_phase_energy(data[k], bulk)
SE = calculate_bulk_energy(xnew, ynew,
x_energy,
y_energy,
data[k],
normalised_bulk)
S = np.append(S, SE)
phase_data, SE = ut.get_phase_data(S, nphases)
return phase_data, SE
def test_get_phase_data(self):
X = np.arange(30)
a, b = ut.get_phase_data(X, 3)
expected = np.ones(10)
assert np.array_equal(a, expected)
