def dimer(pot,
atoms,
elements,
folder=None,
base64=False,
index=None,
nsamples=50,
zoom=False):
"""Plots the potential behavior as the distance between two atoms in a
dimer.
.. note:: This produces a **dict** of all possible 2-body interaction between
distinct element types.
Args:
elements (list): list of chemical symbols in the system.
pot (matdb.calculators.basic.AsyncCalculator): IP to calculate properties with.
atoms (matdb.atoms.AtomsList): list of atoms from which to calculate correlations.
folder (str): path to the folder where the saved image should be stored.
base64 (bool): when True, use a base-64 encoded `src` for the output of
this image; otherwise, the image is saved to `folder` and only its
name is returned.
index (int): integer index of this plot in the parent
collection.
nsamples (int): number of samples to take along the trajectory.
zoom (bool): when True, show a zoomed in view of the potential.
Returns:
dict: keys are concatenated element names (e.g. `AgPd`) and values are
:class:`PointDetailImage` instances.
"""
#First, determine the set of unique element pairs in the list.
from itertools import product
possible = list(product(elements, repeat=2))
target = list(set(map(lambda l: tuple(sorted(l)), possible)))
subplot_kw = {"xlabel": "Distance (Ang)", "ylabel": "IP Energy (eV)"}
result = {}
for elements in target:
#Look up the lattice parameters for each of the elements. Use Vegard's
#law to get a decent domain to plot the energy over.
rmin, rvegard, rmax = _dimer_range(elements)
dimer = Atoms(positions=np.zeros((2, 3)), cell=np.eye(3) * 100)
dimer.positions = 0.
dimer.set_calculator(pot)
dimer.set_chemical_symbols(elements)
if zoom:
rs = np.linspace(0.7 * rmin, pot.cutoff(), nsamples)
else:
rs = np.linspace(0.4 * rmin, pot.cutoff(), nsamples)
energy = []
for r in rs:
dimer.positions[1, 2] = r
energy.append(dimer.get_potential_energy())
elemstr = ''.join(elements) + ('z' if zoom else "")
img = PDI(rs,
np.array(energy),
"plot",
subplot_kw=subplot_kw,
index=index,
base64=base64,
name=elemstr,
imgtype=elemstr,
folder=folder)
result[elemstr] = img
return result
def trimer(pot,
atoms,
elements,
folder=None,
base64=False,
index=None,
nsamples=50):
"""Plots the potential behavior as the angle between three atoms changes in a
trimer.
.. note:: This produces a *dict* of all possible 3-body interactions between
distinct element types.
Args:
elements (list): list of chemical symbols in the system.
pot (matdb.calculators.basic.AsyncCalculator): IP to calculate properties with.
atoms (matdb.atoms.AtomsList): list of atoms from which to calculate correlations.
folder (str): path to the folder where the saved image should be stored.
base64 (bool): when True, use a base-64 encoded `src` for the output of
this image; otherwise, the image is saved to `folder` and only its
name is returned.
index (int): integer index of this plot in the parent
collection.
nsamples (int): number of samples to take along the trajectory.
Returns:
dict: keys are concatenated element names (e.g. `AgPd`) and values are
:class:`PointDetailImage` instances.
"""
#First, determine the set of unique element pairs in the list.
from itertools import product
possible = list(product(elements, repeat=3))
target = list(set(map(lambda l: tuple(sorted(l)), possible)))
subplot_kw = {"xlabel": "Angle (Rad)", "ylabel": "IP Energy (eV)"}
result = {}
for elements in target:
#Look up the lattice parameters for each of the elements. Use Vegard's
#law to get a decent domain to plot the energy over.
from matdb.data import vegard
uelements = list(set(elements))
concdict = [elements.count(e) / 3. for e in uelements]
rvegard = vegard(uelements, concdict)
trimer = Atoms(positions=np.zeros((3, 3)), cell=np.eye(3) * 100)
trimer.positions = 0.
trimer.set_chemical_symbols(elements)
trimer.set_calculator(pot)
#Set the position of the second atom to be at equilibrium with respect
#to the Vegard's law calculation.
trimer.positions[1, 2] = rvegard
#Now, vary the angle of the third atom with respect to the original two
#and see how the angle changes.
theta = np.linspace(np.pi / 6, np.pi, nsamples)
energy = []
for t in theta:
x = rvegard * np.cos(t)
y = rvegard * np.sin(t)
trimer.positions[1, 2] = x
trimer.positions[1, 2] = y
energy.append(trimer.get_potential_energy())
elemstr = trimer.get_chemical_formula()
img = PDI(theta,
np.array(energy),
"plot",
subplot_kw=subplot_kw,
index=index,
base64=base64,
name=elemstr,
imgtype=elemstr,
folder=folder)
result[elemstr] = img
return result
