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# Copyright (c) [2024] [Daniel Pozsar]
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from itertools import permutations, product
from pprint import pprint
import numpy as np
from scipy.special import roots_legendre
from sisl import unit_convert
# Pauli matrices
tau_x = np.array([[0, 1], [1, 0]])
tau_y = np.array([[0, -1j], [1j, 0]])
tau_z = np.array([[1, 0], [0, -1]])
tau_0 = np.array([[1, 0], [0, 1]])
# define some useful functions
def hsk(H, ss, sc_off, k=(0, 0, 0)):
"""
One way to speed up Hk and Sk generation
"""
k = np.asarray(k, np.float64) # this two conversion lines
k.shape = (-1,) # are from the sisl source
# this generates the list of phases
phases = np.exp(-1j * 2 * np.pi * k @ sc_off.T)
HK = np.einsum("abc,a->bc", H, phases)
SK = np.einsum("abc,a->bc", ss, phases)
return HK, SK
def make_contour(emin=-20, emax=0.0, enum=42, p=150):
"""
A more sophisticated contour generator
"""
x, wl = roots_legendre(enum)
R = (emax - emin) / 2
z0 = (emax + emin) / 2
y1 = -np.log(1 + np.pi * p)
y2 = 0
y = (y2 - y1) / 2 * x + (y2 + y1) / 2
phi = (np.exp(-y) - 1) / p
ze = z0 + R * np.exp(1j * phi)
we = -(y2 - y1) / 2 * np.exp(-y) / p * 1j * (ze - z0) * wl
class ccont:
# just an empty container class
pass
cont = ccont()
cont.R = R
cont.z0 = z0
cont.ze = ze
cont.we = we
cont.enum = enum
return cont
def make_kset(dirs="xyz", NUMK=20):
"""
Simple k-grid generator. Depending on the value of the dirs
argument k sampling in 1,2 or 3 dimensions is generated.
If dirs argument does not contain either of x,y or z
a kset of a single k-pont at the origin is returend.
"""
if not (sum([d in dirs for d in "xyz"])):
return np.array([[0, 0, 0]])
kran = len(dirs) * [np.linspace(0, 1, NUMK, endpoint=False)]
mg = np.meshgrid(*kran)
dirsdict = dict()
for d in enumerate(dirs):
dirsdict[d[1]] = mg[d[0]].flatten()
for d in "xyz":
if not (d in dirs):
dirsdict[d] = 0 * dirsdict[dirs[0]]
kset = np.array([dirsdict[d] for d in "xyz"]).T
return kset
def commutator(a, b):
"Shorthand for commutator"
return a @ b - b @ a
def tau_u(u):
"""
Pauli matrix in direction u.
"""
u = u / np.linalg.norm(u) # u is force to be of unit length
return u[0] * tau_x + u[1] * tau_y + u[2] * tau_z
#
def crossM(u):
"""
Definition for the cross-product matrix.
Acting as a cross product with vector u.
"""
return np.array([[0, -u[2], u[1]], [u[2], 0, -u[0]], [-u[1], u[0], 0]])
def RotM(theta, u, eps=1e-10):
"""
Definition of rotation matrix with angle theta around direction u.
"""
u = u / np.linalg.norm(u)
M = (
np.cos(theta) * np.eye(3)
+ np.sin(theta) * crossM(u)
+ (1 - np.cos(theta)) * np.outer(u, u)
)
M[abs(M) < eps] = 0.0 # kill off small numbers
return M
def RotMa2b(a, b, eps=1e-10):
"""
Definition of rotation matrix rotating unit vector a to unit vector b.
Function returns array R such that R@a = b holds.
"""
v = np.cross(a, b)
c = a @ b
M = np.eye(3) + crossM(v) + crossM(v) @ crossM(v) / (1 + c)
M[abs(M) < eps] = 0.0 # kill off small numbers
return M
def spin_tracer(M):
"""
Spin tracer utility.
This akes an operator with the orbital-spin sequence:
orbital 1 up,
orbital 1 down,
orbital 2 up,
orbital 2 down,
that is in the SPIN-BOX representation,
and extracts orbital dependent Pauli traces.
"""
M11 = M[0::2, 0::2]
M12 = M[0::2, 1::2]
M21 = M[1::2, 0::2]
M22 = M[1::2, 1::2]
M_o = dict()
M_o["x"] = M12 + M21
M_o["y"] = 1j * (M12 - M21)
M_o["z"] = M11 - M22
M_o["c"] = M11 + M22
return M_o
def parse_magnetic_entity(dh, atom=None, l=None, **kwargs):
"""
Function to define orbital indeces of a given magnetic entity.
dh: a sisl Hamiltonian object
atom: an integer or list of integers, defining atom (or atoms) in the unicell forming the magnetic entity
l: integer, defining the angular momentum channel
"""
# case where we deal with more than one atom defining the magnetic entity
if type(atom) == list:
dat = []
for a in atom:
a_orb_idx = dh.geometry.a2o(a, all=True)
if (
type(l) == int
): # if specified we restrict to given l angular momentum channel inside each atom
a_orb_idx = a_orb_idx[[o.l == l for o in dh.geometry.atoms[a].orbitals]]
dat.append(a_orb_idx)
orbital_indeces = np.hstack(dat)
# case where we deal with a singel atom magnetic entity
elif type(atom) == int:
orbital_indeces = dh.geometry.a2o(atom, all=True)
if (
type(l) == int
): # if specified we restrict to given l angular momentum channel
orbital_indeces = orbital_indeces[
[o.l == l for o in dh.geometry.atoms[atom].orbitals]
]
return orbital_indeces # numpy array containing integers labeling orbitals associated to a magnetic entity.
def blow_up_orbindx(orb_indices):
"""
Function to blow up orbital indeces to make SPIN BOX indices.
"""
return np.array([[2 * o, 2 * o + 1] for o in orb_indices]).flatten()
def calculate_exchange_tensor(pair):
o1, o2, o3 = pair["energies"] # o1=x, o2=y, o3=z
# dict(o=np.array([1, 0, 0]), vw=[np.array([0, 1, 0]), np.array([0, 0, 1])]),
# dict(o=np.array([0, 1, 0]), vw=[np.array([1, 0, 0]), np.array([0, 0, 1])]),
# dict(o=np.array([0, 0, 1]), vw=[np.array([1, 0, 0]), np.array([0, 1, 0])]),
J_ii = np.array([o2[-1], o1[0], o1[-1]]) # xx, yy, zz
J_S = -0.5 * np.array([o3[1] + o3[2], o2[1] + o2[1], o1[1] + o1[2]]) # yz, zx, xy
D = 0.5 * np.array([o1[1] - o1[2], o2[2] - o2[1], o3[1] - o3[2]]) # x, y, z
return J_ii.sum() / 3, np.concatenate([J_ii[:2] - J_ii.sum() / 3, J_S]).flatten(), D
def print_pair_atomic_indices(pair, magnetic_entities, dh):
atomic_indices = ""
# iterate over the two magnetic entities in a pair
for mag_ent in [magnetic_entities[pair["ai"]], magnetic_entities[pair["aj"]]]:
# get atoms of magnetic entity
atoms_idx = mag_ent["atom"]
# if orbital is not set use all
if "l" not in mag_ent.keys():
mag_ent["l"] = "all"
orbitals = mag_ent["l"]
# if magnetic entity contains one atoms
if isinstance(atoms_idx, int):
atomic_indices += f"[{atoms_idx}]{dh.atoms[atoms_idx].tag}({orbitals})"
# if magnetic entity contains more than one atoms
if isinstance(atoms_idx, list):
# iterate over atoms
atom_group = "{"
for atom_idx in atoms_idx:
atom_group += f"[{atom_idx}]{dh.atoms[atom_idx].tag}({orbitals})--"
# end {} of the atoms in the magnetic entity
atomic_indices += atom_group[:-2] + "}"
# separate magnetic entities
atomic_indices += " "
return atomic_indices
def print_output(simulation_parameters, magnetic_entities, pairs, dh, times):
print(
"##################################################################### GROGU OUTPUT #############################################################################"
)
print(
"================================================================================================================================================================"
)
print("Input file: ")
print(simulation_parameters["path"])
print(
"Number of nodes in the parallel cluster: ",
simulation_parameters["parallel_size"],
)
print(
"================================================================================================================================================================"
)
try:
print("Cell [Ang]: ")
print(simulation_parameters["geom"].cell)
except:
print("Geometry could not be read.")
print(
"================================================================================================================================================================"
)
print("DFT axis: ")
print(simulation_parameters["scf_xcf_orientation"])
print("Quantization axis and perpendicular rotation directions:")
for ref in simulation_parameters["ref_xcf_orientations"]:
print(ref["o"], " --» ", ref["vw"])
print(
"================================================================================================================================================================"
)
print("number of k points: ", simulation_parameters["kset"])
print("k point directions: ", simulation_parameters["kdirs"])
print(
"================================================================================================================================================================"
)
print("Parameters for the contour integral:")
print("Ebot: ", simulation_parameters["ebot"])
print("Eset: ", simulation_parameters["eset"])
print("Esetp: ", simulation_parameters["esetp"])
print(
"================================================================================================================================================================"
)
print("Atomic informations: ")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print(
"[atom index]Element(orbitals) x [Ang] y [Ang] z [Ang] Sx Sy Sz Q Lx Ly Lz Jx Jy Jz"
)
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over magnetic entities
for mag_ent in magnetic_entities:
# get atoms of magnetic entity
atoms_idx = mag_ent["atom"]
# if orbital is not set use all
if "l" not in mag_ent.keys():
mag_ent["l"] = "all"
orbitals = mag_ent["l"]
# if magnetic entity contains one atom
if isinstance(atoms_idx, int):
# coordinates and tag
x, y, z = dh.xyz[atoms_idx]
print(
f"[{atoms_idx}]{dh.atoms[atoms_idx].tag}({orbitals}) {x} {y} {z}"
)
# if magnetic entity contains more than one atoms
if isinstance(atoms_idx, list):
# iterate over atoms
for atom_idx in atoms_idx:
# coordinates and tag
x, y, z = dh.xyz[atom_idx]
print(
f"[{atom_idx}]{dh.atoms[atom_idx].tag}({orbitals}) {x} {y} {z}"
)
print("")
print(
"================================================================================================================================================================"
)
print("Exchange [meV]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print("Magnetic entity1 Magnetic entity2 [i j k] d [Ang]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over pairs
for pair in pairs:
# calculate magnetic parameters
J_iso, J_S, D = calculate_exchange_tensor(pair)
J_iso = J_iso * unit_convert("eV", "meV")
J_S = J_S * unit_convert("eV", "meV")
D = D * unit_convert("eV", "meV")
# print pair parameters
print(
print_pair_atomic_indices(pair, magnetic_entities, dh),
f" {pair['Ruc']} d [Ang] Not yet.",
)
# print magnetic parameters
print("Isotropic: ", J_iso)
print("DMI: ", D)
print("Symmetric-anisotropy: ", J_S)
print("Energies for debugging: ")
pprint(np.array(pair["energies"]))
print(
"J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)"
)
o1, o2, o3 = pair["energies"]
pprint(np.array([o2[-1], o3[0], o1[0]]))
print("Test J_xx = E(y,z) = E(z,y)")
print(o2[-1], o3[-1])
print("")
print(
"================================================================================================================================================================"
)
print("Runtime information: ")
print(f"Total runtime: {times['end_time'] - times['start_time']} s")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print(f"Initial setup: {times['setup_time'] - times['start_time']} s")
print(
f"Hamiltonian conversion and XC field extraction: {times['H_and_XCF_time'] - times['setup_time']:.3f} s"
)
print(
f"Pair and site datastructure creatrions: {times['site_and_pair_dictionaries_time'] - times['H_and_XCF_time']:.3f} s"
)
print(
f"k set cration and distribution: {times['k_set_time'] - times['site_and_pair_dictionaries_time']:.3f} s"
)
print(
f"Rotating XC potential: {times['reference_rotations_time'] - times['k_set_time']:.3f} s"
)
print(
f"Greens function inversion: {times['green_function_inversion_time'] - times['reference_rotations_time']:.3f} s"
)
print(
f"Calculate energies and magnetic components: {times['end_time'] - times['green_function_inversion_time']:.3f} s"
)