def calculate(stoich,
data,
SE,
adsorbant,
thermochem,
max_t=1000,
min_p=-13,
max_p=5.5,
coverage=None,
transform=True):
'''Collects input variables and intitialises the calculation.
Parameters
----------
stoich : :py:class:`surfinpy.data.DataSet`
information about the stoichiometric surface
data : :py:attr:`list`
list of :py:class:`surfinpy.data.DataSet` objects on the "adsorbed" surfaces
SE : :py:attr:`float`
surface energy of the stoichiomteric surface
adsorbant : :py:attr:`float`
dft energy of adsorbing species
coverage : :py:attr:`array_like` (default None)
Numpy array containing the different coverages of adsorbant.
thermochem : :py:attr:`array_like`
Numpy array containing thermochemcial data downloaded from NIST_JANAF
for the adsorbing species.
max_t : :py:attr:`int`
Maximum temperature in the phase diagram
min_p : :py:attr:`int`
Minimum pressure of phase diagram
max_p : :py:attr:`int`
Maximum pressure of phase diagram
Returns
-------
system : :py:class:`surfinpy.plotting.PTPlot`
plotting object
'''
if coverage is None:
coverage = ut.calculate_coverage(data)
lnP, logP, T, adsorbant_t = inititalise(thermochem, adsorbant, max_t,
min_p, max_p)
nsurfaces = len(data) + 1
AE = adsorption_energy(data, stoich, adsorbant_t)
SE_array, SEABS = calculate_surface_energy(AE, lnP, T, coverage, SE,
nsurfaces)
ticks = np.unique([SE_array])
if transform is True:
SE_array = ut.transform_numbers(SE_array, ticks)
phase_grid = np.reshape(SE_array, (lnP.size, T.size))
SEABS = np.reshape(SEABS, (lnP.size, T.size))
data.insert(0, stoich)
y = logP
x = T
z = phase_grid
system = plotting.PTPlot(x, y, z)
return system
def calculate(stoich,
data,
SE,
adsorbant,
thermochem,
max_t=1000,
min_p=-13,
max_p=5.5,
coverage=None):
'''Collects input variables and intitialises the calculation.
Parameters
----------
stoich : dictionary
information about the stoichiometric surface
data : list
list of dictionaries containing information on the "adsorbed" surfaces
SE : float
surface energy of the stoichiomteric surface
adsorbant : float
dft energy of adsorbing species
coverage : array like (default None)
Numpy array containing the different coverages of adsorbant.
thermochem : array like
Numpy array containing thermochemcial data downloaded from NIST_JANAF
for the adsorbing species.
max_t : int
Maximum temperature in the phase diagram
min_p : int
Minimum pressure of phase diagram
max_p : int
Maximum pressure of phase diagram
Returns
-------
system : class object
plotting object
'''
if coverage is None:
coverage = ut.calculate_coverage(data)
lnP, logP, T, adsorbant_t = inititalise(thermochem, adsorbant, max_t,
min_p, max_p)
nsurfaces = len(data) + 1
AE = adsorption_energy(data, stoich, adsorbant_t)
SE_array, SE = calculate_surface_energy(AE, lnP, T, coverage, SE,
nsurfaces)
ticks = np.unique([SE_array])
SE_array = ut.transform_numbers(SE_array, ticks)
phase_grid = np.reshape(SE_array, (lnP.size, T.size))
data.insert(0, stoich)
y = logP
x = T
z = phase_grid
system = pvt_plot.PVTPlot(x, y, z)
return system, SE
def calculate_surface_energy(stoich,
data,
SE,
adsorbant,
thermochem,
T,
P,
coverage=None):
"""Calculate the surface energy at a specific temperature
and pressure.
Parameters
----------
stoich : :py:class:`surfinpy.data.ReferenceDataSet`
information about the stoichiometric surface
data : :py:attr:`list`
list of dictionaries containing information on the "adsorbed" surfaces
SE : :py:attr:`float`
surface energy of the stoichiomteric surface
adsorbant : :py:attr:`float`
dft energy of adsorbing species
coverage : :py:attr:`array_like`
Numpy array containing the different coverages of adsorbant.
thermochem : :py:attr:`array_like`
Numpy array containing thermochemcial data downloaded from NIST_JANAF
for the adsorbing species.
T : :py:attr:`float`
Temperature to calculate surface energy
P : :py:attr:`float`
Pressure to calculate the surface energy
coverage : :py:attr:`array_like`
Coverage of adsorbed specied on the surface.
Returns
-------
SEs : :py:attr:`array_like`
surface energies for each surface at T/P
"""
if coverage is None:
coverage = ut.calculate_coverage(data)
R = value('molar gas constant')
N_A = value('Avogadro constant')
lnP = np.log(10**P)
adsorbant = temperature_correction(T, thermochem, adsorbant)
AE = pt.adsorption_energy(data, stoich, adsorbant)
SEs = np.array([SE])
for i in range(0, len(data)):
S = SE + (coverage[i] / N_A) * (AE[i] - (lnP * (T * R)))
SEs = np.append(SEs, S)
return SEs
def test_calculate_coverage(self):
H20 = data.DataSet(cation=24,
x=48,
y=2,
area=60.22,
energy=-570.00,
label="One",
nspecies=1)
H2O_2 = data.DataSet(cation=24,
x=48,
y=4,
area=60.22,
energy=-570.00,
label="Two",
nspecies=1)
dataset = [H20, H2O_2]
x = ut.calculate_coverage(dataset)
expected = np.array([1.66057788 * 10**18, 3.32115576 * 10**18])
assert_approx_equal(expected[0], x[0], significant=8)
assert_approx_equal(expected[1], x[1], significant=8)
def test_calculate_coverage(self):
H2O = {
'M': 24,
'X': 48,
'Y': 2,
'Area': 60.22,
'Energy': -621.877140,
'Label': '1.66 - $Ce^{4+}$'
}
H2O_2 = {
'M': 24,
'X': 48,
'Y': 4,
'Area': 60.22,
'Energy': -670.229520,
'Label': '3.32 - $Ce^{4+}$'
}
data = [H2O, H2O_2]
x = ut.calculate_coverage(data)
expected = np.array([1.66057788 * 10**18, 3.32115576 * 10**18])
assert_approx_equal(expected[0], x[0], significant=8)
assert_approx_equal(expected[1], x[1], significant=8)
from surfinpy import utils as ut
from surfinpy import p_vs_t
adsorbant = -14.00
SE = 1.40
stoich = {'Cation': 24, 'X': 48, 'Y': 0, 'Area': 60.22, 'Energy': -575.00, 'Label': 'Bare'}
H2O = {'Cation': 24, 'X': 48, 'Y': 2, 'Area': 60.22, 'Energy': -605.00, 'Label': '1 Water'}
H2O_2 = {'Cation': 24, 'X': 48, 'Y': 8, 'Area': 60.22, 'Energy': -695.00, 'Label': '2 Water'}
data = [H2O, H2O_2]
coverage = ut.calculate_coverage(data)
thermochem = ut.read_nist("H2O.txt")
system = p_vs_t.calculate(stoich, data, SE, adsorbant, thermochem, coverage)
system.plot(output="Example_4.png",)