Beispiel #1
0
    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,)
Beispiel #2
0
    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()
Beispiel #3
0
    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, )
Beispiel #4
0
    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, )