def compute_effective_field(self, y):
y.shape = (self.total_image_num, -1)
for i in range(self.image_num):
self.sim.spin[:] = y[i + 1][:]
#
self.sim.compute_effective_field(t=0)
# Compute effective field, which is the gradient of
# the energy in the NEB method (derivative with respect to
# the generalised coordinates)
h = self.sim.field
#
self.Heff[i + 1, :] = h[:]
# Compute the total energy
self.energy[i + 1] = self.sim.compute_energy()
# Compute the 'distance' or difference between neighbouring states
# around y[i+1]. This is used to compute the spring force
#
dm1 = compute_dm(y[i], y[i + 1])
dm2 = compute_dm(y[i + 1], y[i + 2])
self.springs[i] = self.spring * (dm2 - dm1)
# Use the native NEB (C code) to compute the tangents according
# to the improved NEB method, developed by Henkelman and Jonsson
# at: Henkelman et al., Journal of Chemical Physics 113, 22 (2000)
neb_clib.compute_tangents(y, self.energy, self.tangents, self.total_image_num, 3 * self.n)
# native_neb.compute_springs(y,self.springs,self.spring)
y.shape = (-1,)
def compute_effective_field(self, y):
y.shape = (self.total_image_num, -1)
for i in range(self.image_num):
self.sim.spin[:] = y[i + 1][:]
#
self.sim.compute_effective_field(t=0)
# Compute effective field, which is the gradient of
# the energy in the NEB method (derivative with respect to
# the generalised coordinates)
h = self.sim.field
#
self.Heff[i + 1, :] = h[:]
# Compute the total energy
self.energy[i + 1] = self.sim.compute_energy()
# Compute the 'distance' or difference between neighbouring states
# around y[i+1]. This is used to compute the spring force
#
dm1 = compute_dm(y[i], y[i + 1])
dm2 = compute_dm(y[i + 1], y[i + 2])
self.springs[i] = self.spring * (dm2 - dm1)
# Use the native NEB (C code) to compute the tangents according
# to the improved NEB method, developed by Henkelman and Jonsson
# at: Henkelman et al., Journal of Chemical Physics 113, 22 (2000)
neb_clib.compute_tangents(y, self.energy, self.tangents,
self.total_image_num, 3 * self.n)
# native_neb.compute_springs(y,self.springs,self.spring)
y.shape = (-1, )
gc.collect()
def compute_effective_field(self, y):
"""
Compute the effective field using Fidimag and the tangents using
the NEB C code, according
to the improved NEB method, developed by Henkelman and Jonsson
# at: Henkelman et al., Journal of Chemical Physics 113, 22 (2000)
y :: The array with all the spin components for every image, i.e.
for a N images energy band and P spins system:
[theta0_0, phi0_0, theta0_1, phi0_1, ... , theta0_P, phi0_P,
theta1_0, phi1_0, ..., theta1_P, phi1_P,
...
thetaN_0, phiN_0, ..., thetaN_P, phiN_P]
"""
# Every row has all the spin components of an image
y.shape = (self.total_image_num, -1)
for i in range(self.image_num):
# Redefine the angles of the (i + 1)-th image
# (see the corresponding function)
check_boundary(y[i + 1])
# Set the simulation magnetisation to the (i+1)-th image
# spin components
self.sim.spin = spherical2cartesian(y[i + 1])
# Compute the effective field using Fidimag's methods.
# (we use the time=0 since we are only using the simulation
# object to get the field)
# Remember that The effective field is the gradient of
# the energy with respect to the generalised coordinates
# i.e. the magnetisation
self.sim.compute_effective_field(t=0)
h = self.sim.field
# Save the field components of the (i + 1)-th image
# to the effective field array which is in spherical coords
self.Heff[i + 1, :] = cartesian2spherical_field(h, y[i + 1])
# Compute and save the total energy for this image
self.energy[i + 1] = self.sim.compute_energy()
# Compute the 'distance' or difference between neighbouring states
# around the y[i+1] image. This is used to compute the spring force
dm1 = compute_dm(y[i], y[i + 1])
dm2 = compute_dm(y[i + 1], y[i + 2])
self.springs[i] = self.spring * (dm2 - dm1)
# Compute tangents using the C library (this is also used for
# cartesian coordinates, we set here the total number of spin
# components for spherical coords)
neb_clib.compute_tangents(
y, self.energy, self.tangents, self.total_image_num, 2 * self.n)
# Flatten y
y.shape = (-1, )
def compute_effective_field(self, y):
"""
Compute the effective field using Fidimag and the tangents using
the NEB C code, according
to the improved NEB method, developed by Henkelman and Jonsson
# at: Henkelman et al., Journal of Chemical Physics 113, 22 (2000)
y :: The array with all the spin components for every image, i.e.
for a N images energy band and P spins system:
[theta0_0, phi0_0, theta0_1, phi0_1, ... , theta0_P, phi0_P,
theta1_0, phi1_0, ..., theta1_P, phi1_P,
...
thetaN_0, phiN_0, ..., thetaN_P, phiN_P]
"""
# Every row has all the spin components of an image
y.shape = (self.total_image_num, -1)
for i in range(self.image_num):
# Redefine the angles of the (i + 1)-th image
# (see the corresponding function)
check_boundary(y[i + 1])
# Set the simulation magnetisation to the (i+1)-th image
# spin components
self.sim.spin = spherical2cartesian(y[i + 1])
# Compute the effective field using Fidimag's methods.
# (we use the time=0 since we are only using the simulation
# object to get the field)
# Remember that The effective field is the gradient of
# the energy with respect to the generalised coordinates
# i.e. the magnetisation
self.sim.compute_effective_field(t=0)
h = self.sim.field
# Save the field components of the (i + 1)-th image
# to the effective field array which is in spherical coords
self.Heff[i + 1, :] = cartesian2spherical_field(h, y[i + 1])
# Compute and save the total energy for this image
self.energy[i + 1] = self.sim.compute_energy()
# Compute the 'distance' or difference between neighbouring states
# around the y[i+1] image. This is used to compute the spring force
dm1 = compute_dm(y[i], y[i + 1])
dm2 = compute_dm(y[i + 1], y[i + 2])
self.springs[i] = self.spring * (dm2 - dm1)
# Compute tangents using the C library (this is also used for
# cartesian coordinates, we set here the total number of spin
# components for spherical coords)
neb_clib.compute_tangents(y, self.energy, self.tangents,
self.total_image_num, 2 * self.n)
# Flatten y
y.shape = (-1, )
