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grogu/test.ipynb

5885 lines
201 KiB

3 months ago
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
3 months ago
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Daniels-Air:40976] shmem: mmap: an error occurred while determining whether or not /var/folders/yh/dx7xl94n3g52ts3td8qcxjcc0000gn/T//ompi.Daniels-Air.501/jf.0/3308912640/sm_segment.Daniels-Air.501.c53a0000.0 could be created.\n"
]
3 months ago
}
],
"source": [
"import os\n",
"from sys import stdout\n",
"from tqdm import tqdm\n",
"from timeit import default_timer as timer\n",
"\n",
"os.environ[\"OMP_NUM_THREADS\"] = \"1\" # export OMP_NUM_THREADS=4\n",
"os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\" # export OPENBLAS_NUM_THREADS=4\n",
"os.environ[\"MKL_NUM_THREADS\"] = \"1\" # export MKL_NUM_THREADS=6\n",
"os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"1\" # export VECLIB_MAXIMUM_THREADS=4\n",
"os.environ[\"NUMEXPR_NUM_THREADS\"] = \"1\" # export NUMEXPR_NUM_THREADS=6\n",
"\n",
3 months ago
"import numpy as np\n",
"import sisl\n",
"from src.grogu_magn.useful import *\n",
3 months ago
"from mpi4py import MPI\n",
"from numpy.linalg import inv\n",
"import warnings\n",
"\n",
"start_time = timer()\n"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 2,
3 months ago
"metadata": {},
3 months ago
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of nodes in the parallel cluster: 1\n"
]
}
],
3 months ago
"source": [
"# this cell mimicks an input file\n",
"fdf = sisl.get_sile(\n",
" \"./lat3_791/Fe3GeTe2.fdf\"\n",
")\n",
3 months ago
"# this information needs to be given at the input!!\n",
"scf_xcf_orientation = np.array([0, 0, 1]) # z\n",
3 months ago
"# list of reference directions for around which we calculate the derivatives\n",
"# o is the quantization axis, v and w are two axes perpendicular to it\n",
"# at this moment the user has to supply o,v,w on the input.\n",
3 months ago
"# we can have some default for this\n",
"ref_xcf_orientations = [\n",
" dict(o=np.array([1, 0, 0]), vw=[np.array([0, 1, 0]), np.array([0, 0, 1])]),\n",
" dict(o=np.array([0, 1, 0]), vw=[np.array([1, 0, 0]), np.array([0, 0, 1])]),\n",
" dict(o=np.array([0, 0, 1]), vw=[np.array([1, 0, 0]), np.array([0, 1, 0])]),\n",
"]\n",
3 months ago
"\n",
"# human readable definition of magnetic entities\n",
"magnetic_entities = [\n",
" dict(atom=3, l=2),\n",
" dict(atom=4, l=2),\n",
" dict(atom=5, l=2),\n",
"# dict(atom=[3, 4]),\n",
"]\n",
3 months ago
"\n",
"# pair information\n",
"pairs = [\n",
" dict(ai=0, aj=1, Ruc=np.array([0, 0, 0])), # isotropic should be -82 meV\n",
" dict(ai=0, aj=2, Ruc=np.array([0, 0, 0])), # these should all be around -41.9 in the isotropic part\n",
"# dict(ai=1, aj=2, Ruc=np.array([0, 0, 0])),\n",
"# dict(ai=0, aj=2, Ruc=np.array([-1, 0, 0])),\n",
"# dict(ai=1, aj=2, Ruc=np.array([-1, 0, 0])),\n",
"]\n",
3 months ago
"\n",
3 months ago
"# Brilloun zone sampling and Green function contour integral\n",
"kset = 20\n",
"kdirs = \"xy\"\n",
"ebot = -30\n",
3 months ago
"eset = 50\n",
"esetp = 10000\n",
"\n",
"\n",
"# MPI parameters\n",
"comm = MPI.COMM_WORLD\n",
"size = comm.Get_size()\n",
"rank = comm.Get_rank()\n",
"root_node = 0\n",
"if rank == root_node:\n",
" print(\"Number of nodes in the parallel cluster: \", size)\n",
"\n",
"simulation_parameters = dict(path=\"Not yet specified.\",\n",
" scf_xcf_orientation=scf_xcf_orientation, \n",
" ref_xcf_orientations=ref_xcf_orientations,\n",
" kset=kset,\n",
" kdirs=kdirs, \n",
" ebot=ebot,\n",
" eset=eset, \n",
" esetp=esetp,\n",
" parallel_size=size)\n",
"\n",
"# digestion of the input\n",
"# read in hamiltonian\n",
"dh = fdf.read_hamiltonian()\n",
"try:\n",
" simulation_parameters[\"geom\"] = fdf.read_geometry()\n",
"except:\n",
" print(\"Error reading geometry.\")\n",
"\n",
"# unit cell index\n",
"uc_in_sc_idx = dh.lattice.sc_index([0, 0, 0])\n",
"\n",
"setup_time = timer()"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 3,
3 months ago
"metadata": {},
"outputs": [],
3 months ago
"source": [
"NO = dh.no # shorthand for number of orbitals in the unit cell\n",
3 months ago
"\n",
3 months ago
"# preprocessing Hamiltonian and overlap matrix elements\n",
"h11 = dh.tocsr(dh.M11r)\n",
"h11 += dh.tocsr(dh.M11i) * 1.0j\n",
"h11 = h11.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype(\"complex128\")\n",
3 months ago
"\n",
"h22 = dh.tocsr(dh.M22r)\n",
"h22 += dh.tocsr(dh.M22i) * 1.0j\n",
"h22 = h22.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype(\"complex128\")\n",
3 months ago
"\n",
"h12 = dh.tocsr(dh.M12r)\n",
"h12 += dh.tocsr(dh.M12i) * 1.0j\n",
"h12 = h12.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype(\"complex128\")\n",
3 months ago
"\n",
"h21 = dh.tocsr(dh.M21r)\n",
"h21 += dh.tocsr(dh.M21i) * 1.0j\n",
"h21 = h21.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype(\"complex128\")\n",
3 months ago
"\n",
"sov = (\n",
" dh.tocsr(dh.S_idx)\n",
" .toarray()\n",
" .reshape(NO, dh.n_s, NO)\n",
" .transpose(0, 2, 1)\n",
" .astype(\"complex128\")\n",
")\n",
3 months ago
"\n",
"\n",
3 months ago
"# Reorganization of Hamiltonian and overlap matrix elements to SPIN BOX representation\n",
"U = np.vstack(\n",
" [np.kron(np.eye(NO, dtype=int), [1, 0]), np.kron(np.eye(NO, dtype=int), [0, 1])]\n",
")\n",
3 months ago
"# This is the permutation that transforms ud1ud2 to u12d12\n",
"# That is this transforms FROM SPIN BOX to ORBITAL BOX => U\n",
"# the inverse transformation is U.T u12d12 to ud1ud2\n",
"# That is FROM ORBITAL BOX to SPIN BOX => U.T\n",
"\n",
3 months ago
"# From now on everything is in SPIN BOX!!\n",
"hh, ss = np.array(\n",
" [\n",
" U.T @ np.block([[h11[:, :, i], h12[:, :, i]], [h21[:, :, i], h22[:, :, i]]]) @ U\n",
" for i in range(dh.lattice.nsc.prod())\n",
" ]\n",
"), np.array(\n",
" [\n",
" U.T\n",
" @ np.block([[sov[:, :, i], sov[:, :, i] * 0], [sov[:, :, i] * 0, sov[:, :, i]]])\n",
" @ U\n",
" for i in range(dh.lattice.nsc.prod())\n",
" ]\n",
")\n",
"\n",
"\n",
"# symmetrizing Hamiltonian and overlap matrix to make them hermitian\n",
3 months ago
"for i in range(dh.lattice.sc_off.shape[0]):\n",
" j = dh.lattice.sc_index(-dh.lattice.sc_off[i])\n",
" h1, h1d = hh[i], hh[j]\n",
" hh[i], hh[j] = (h1 + h1d.T.conj()) / 2, (h1d + h1.T.conj()) / 2\n",
" s1, s1d = ss[i], ss[j]\n",
" ss[i], ss[j] = (s1 + s1d.T.conj()) / 2, (s1d + s1.T.conj()) / 2\n",
3 months ago
"\n",
"# identifying TRS and TRB parts of the Hamiltonian\n",
"TAUY = np.kron(np.eye(NO), tau_y)\n",
"hTR = np.array([TAUY @ hh[i].conj() @ TAUY for i in range(dh.lattice.nsc.prod())])\n",
"hTRS = (hh + hTR) / 2\n",
"hTRB = (hh - hTR) / 2\n",
3 months ago
"\n",
"# extracting the exchange field\n",
"traced = [spin_tracer(hTRB[i]) for i in range(dh.lattice.nsc.prod())] # equation 77\n",
"XCF = np.array(\n",
" [\n",
" np.array([f[\"x\"] for f in traced]),\n",
" np.array([f[\"y\"] for f in traced]),\n",
" np.array([f[\"z\"] for f in traced]),\n",
" ]\n",
") # equation 77\n",
3 months ago
"\n",
3 months ago
"# Check if exchange field has scalar part\n",
"max_xcfs = abs(np.array(np.array([f[\"c\"] for f in traced]))).max()\n",
"if max_xcfs > 1e-12:\n",
" warnings.warn(\n",
" f\"Exchange field has non negligible scalar part. Largest value is {max_xcfs}\"\n",
" )\n",
"\n",
"H_and_XCF_time = timer()"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 4,
3 months ago
"metadata": {},
"outputs": [],
"source": [
3 months ago
"# for every site we have to store 3 Greens function (and the associated _tmp-s) in the 3 reference directions\n",
"for i, mag_ent in enumerate(magnetic_entities):\n",
" parsed = parse_magnetic_entity(dh, **mag_ent) # parse orbital indexes\n",
" magnetic_entities[i][\"orbital_indeces\"] = parsed\n",
" magnetic_entities[i][\"spin_box_indeces\"] = blow_up_orbindx(\n",
" parsed\n",
" ) # calculate spin box indexes\n",
" spin_box_shape = len(\n",
" mag_ent[\"spin_box_indeces\"]\n",
" ) # calculate size for Greens function generation\n",
"\n",
" mag_ent[\"energies\"] = [] # we will store the second order energy derivations here\n",
"\n",
" mag_ent[\"Gii\"] = [] # Greens function\n",
" mag_ent[\"Gii_tmp\"] = [] # Greens function for parallelization\n",
" mag_ent[\"Vu1\"] = [\n",
" list([]) for _ in range(len(ref_xcf_orientations))\n",
" ] # These will be the perturbed potentials from eq. 100\n",
" mag_ent[\"Vu2\"] = [list([]) for _ in range(len(ref_xcf_orientations))]\n",
3 months ago
" for i in ref_xcf_orientations:\n",
" mag_ent[\"Gii\"].append(\n",
" np.zeros((eset, spin_box_shape, spin_box_shape), dtype=\"complex128\")\n",
" ) # Greens functions for every quantization axis\n",
" mag_ent[\"Gii_tmp\"].append(\n",
" np.zeros((eset, spin_box_shape, spin_box_shape), dtype=\"complex128\")\n",
" )\n",
"\n",
"# for every site we have to store 2x3 Greens function (and the associated _tmp-s)\n",
"# in the 3 reference directions, because G_ij and G_ji are both needed\n",
3 months ago
"for pair in pairs:\n",
" spin_box_shape_i, spin_box_shape_j = len(\n",
" magnetic_entities[pair[\"ai\"]][\"spin_box_indeces\"]\n",
" ), len(\n",
" magnetic_entities[pair[\"aj\"]][\"spin_box_indeces\"]\n",
" ) # calculate size for Greens function generation\n",
"\n",
" pair[\"energies\"] = [] # we will store the second order energy derivations here\n",
"\n",
" pair[\"Gij\"] = [] # Greens function\n",
" pair[\"Gji\"] = []\n",
" pair[\"Gij_tmp\"] = [] # Greens function for parallelization\n",
" pair[\"Gji_tmp\"] = []\n",
"\n",
" pair[\"Vij\"] = [\n",
" list([]) for _ in range(len(ref_xcf_orientations))\n",
" ] # These will be the perturbed potentials from eq. 100\n",
3 months ago
" pair[\"Vji\"] = [list([]) for _ in range(len(ref_xcf_orientations))]\n",
3 months ago
"\n",
" for i in ref_xcf_orientations:\n",
" pair[\"Gij\"].append(\n",
" np.zeros((eset, spin_box_shape_i, spin_box_shape_j), dtype=\"complex128\")\n",
" )\n",
" pair[\"Gij_tmp\"].append(\n",
" np.zeros((eset, spin_box_shape_i, spin_box_shape_j), dtype=\"complex128\")\n",
" ) # Greens functions for every quantization axis\n",
" pair[\"Gji\"].append(\n",
" np.zeros((eset, spin_box_shape_j, spin_box_shape_i), dtype=\"complex128\")\n",
" )\n",
" pair[\"Gji_tmp\"].append(\n",
" np.zeros((eset, spin_box_shape_j, spin_box_shape_i), dtype=\"complex128\")\n",
" )\n",
"\n",
"site_and_pair_dictionaries_time = timer()\n"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 5,
3 months ago
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k loop: 0%| | 0/400 [00:00<?, ?it/s]"
]
}
],
3 months ago
"source": [
"kset = make_kset(dirs=kdirs, NUMK=kset) # generate k space sampling\n",
"wkset = np.ones(len(kset)) / len(kset) # generate weights for k points\n",
"kpcs = np.array_split(kset, size) # split the k points based on MPI size\n",
"kpcs[root_node] = tqdm(kpcs[root_node], desc='k loop', file=stdout)\n",
"\n",
"k_set_time = timer()"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 6,
3 months ago
"metadata": {},
"outputs": [],
"source": [
3 months ago
"# this will contain all the data needed to calculate the energy variations upon rotation\n",
"hamiltonians = []\n",
3 months ago
"\n",
3 months ago
"# iterate over the reference directions (quantization axes)\n",
"for i, orient in enumerate(ref_xcf_orientations):\n",
" # obtain rotated exchange field\n",
" R = RotMa2b(scf_xcf_orientation, orient[\"o\"])\n",
" rot_XCF = np.einsum(\"ij,jklm->iklm\", R, XCF)\n",
" rot_H_XCF = sum(\n",
" [np.kron(rot_XCF[i], tau) for i, tau in enumerate([tau_x, tau_y, tau_z])]\n",
" )\n",
3 months ago
" rot_H_XCF_uc = rot_H_XCF[uc_in_sc_idx]\n",
"\n",
3 months ago
" # obtain total Hamiltonian with the rotated exchange field\n",
" rot_H = hTRS + rot_H_XCF # equation 76\n",
3 months ago
"\n",
" hamiltonians.append(\n",
" dict(orient=orient[\"o\"], H=rot_H, rotations=[])\n",
" ) # store orientation and rotated Hamiltonian\n",
"\n",
" for u in orient[\n",
" \"vw\"\n",
" ]: # these are the infinitezimal rotations (for now) perpendicular to the quantization axis\n",
" Tu = np.kron(np.eye(NO, dtype=int), tau_u(u)) # section 2.H\n",
3 months ago
"\n",
" Vu1 = 1j / 2 * commutator(rot_H_XCF_uc, Tu) # equation 100\n",
" Vu2 = 1 / 8 * commutator(commutator(Tu, rot_H_XCF_uc), Tu) # equation 100\n",
3 months ago
"\n",
3 months ago
" for mag_ent in magnetic_entities:\n",
" mag_ent[\"Vu1\"][i].append(\n",
" Vu1[:, mag_ent[\"spin_box_indeces\"]][mag_ent[\"spin_box_indeces\"], :]\n",
" ) # fill up the perturbed potentials (for now) based on the on-site projections\n",
" mag_ent[\"Vu2\"][i].append(\n",
" Vu2[:, mag_ent[\"spin_box_indeces\"]][mag_ent[\"spin_box_indeces\"], :]\n",
" )\n",
3 months ago
"\n",
3 months ago
" for pair in pairs:\n",
" ai = magnetic_entities[pair[\"ai\"]][\n",
" \"spin_box_indeces\"\n",
" ] # get the pair orbital sizes from the magnetic entities\n",
3 months ago
" aj = magnetic_entities[pair[\"aj\"]][\"spin_box_indeces\"]\n",
" pair[\"Vij\"][i].append(\n",
" Vu1[:, ai][aj, :]\n",
" ) # fill up the perturbed potentials (for now) based on the on-site projections\n",
" pair[\"Vji\"][i].append(Vu1[:, aj][ai, :])\n",
"\n",
"reference_rotations_time = timer()"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 7,
3 months ago
"metadata": {},
3 months ago
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of magnetic entities being calculated: 3\n",
3 months ago
"We have to calculate the Greens function for three reference direction and we are going to calculate 15 energy integrals per site.\n",
"The shape of the Hamiltonian and the Greens function is 84x84.\n",
"k loop: 100%|██████████| 400/400 [01:48<00:00, 3.69it/s]\n"
3 months ago
]
}
],
3 months ago
"source": [
3 months ago
"if rank == root_node:\n",
" print(\"Number of magnetic entities being calculated: \", len(magnetic_entities))\n",
" print(\n",
" \"We have to calculate the Greens function for three reference direction and we are going to calculate 15 energy integrals per site.\"\n",
" )\n",
" print(f\"The shape of the Hamiltonian and the Greens function is {NO}x{NO}.\")\n",
3 months ago
"comm.Barrier()\n",
"# ----------------------------------------------------------------------\n",
3 months ago
"\n",
"# make energy contour\n",
3 months ago
"# we are working in eV now !\n",
"# and sisil shifts E_F to 0 !\n",
"cont = make_contour(emin=ebot, enum=eset, p=esetp)\n",
3 months ago
"eran = cont.ze\n",
"\n",
"# ----------------------------------------------------------------------\n",
3 months ago
"# sampling the integrand on the contour and the BZ\n",
"for k in kpcs[rank]:\n",
" wk = wkset[rank] # weight of k point in BZ integral\n",
" for i, hamiltonian_orientation in enumerate(\n",
" hamiltonians\n",
" ): # iterate over reference directions\n",
" # calculate Greens function\n",
3 months ago
" H = hamiltonian_orientation[\"H\"]\n",
" HK, SK = hsk(H, ss, dh.sc_off, k)\n",
3 months ago
" Gk = inv(SK * eran.reshape(eset, 1, 1) - HK)\n",
"\n",
3 months ago
" # store the Greens function slice of the magnetic entities (for now) based on the on-site projections\n",
" for mag_ent in magnetic_entities:\n",
" mag_ent[\"Gii_tmp\"][i] += (\n",
" Gk[:, mag_ent[\"spin_box_indeces\"]][..., mag_ent[\"spin_box_indeces\"]]\n",
" * wk\n",
" )\n",
3 months ago
"\n",
" for pair in pairs:\n",
" # add phase shift based on the cell difference\n",
" phase = np.exp(1j * 2 * np.pi * k @ pair[\"Ruc\"].T)\n",
"\n",
3 months ago
" # get the pair orbital sizes from the magnetic entities\n",
" ai = magnetic_entities[pair[\"ai\"]][\"spin_box_indeces\"]\n",
" aj = magnetic_entities[pair[\"aj\"]][\"spin_box_indeces\"]\n",
"\n",
" # store the Greens function slice of the magnetic entities (for now) based on the on-site projections\n",
" pair[\"Gij_tmp\"][i] += Gk[:, ai][..., aj] * phase * wk\n",
" pair[\"Gji_tmp\"][i] += Gk[:, aj][..., ai] * phase * wk\n",
3 months ago
"\n",
"# summ reduce partial results of mpi nodes\n",
"for i in range(len(hamiltonians)):\n",
" for mag_ent in magnetic_entities:\n",
" comm.Reduce(mag_ent[\"Gii_tmp\"][i], mag_ent[\"Gii\"][i], root=root_node)\n",
3 months ago
"\n",
3 months ago
" for pair in pairs:\n",
" comm.Reduce(pair[\"Gij_tmp\"][i], pair[\"Gij\"][i], root=root_node)\n",
" comm.Reduce(pair[\"Gji_tmp\"][i], pair[\"Gji\"][i], root=root_node)\n",
"\n",
"green_function_inversion_time = timer()\n"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 8,
3 months ago
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"############################### GROGU OUTPUT ###################################\n",
"================================================================================\n",
"Input file: \n",
"Not yet specified.\n",
"Number of nodes in the parallel cluster: 1\n",
"================================================================================\n",
"Cell [Ang]: \n",
"[[ 3.79100000e+00 0.00000000e+00 0.00000000e+00]\n",
" [-1.89550000e+00 3.28310231e+00 0.00000000e+00]\n",
" [ 1.25954923e-15 2.18160327e-15 2.05700000e+01]]\n",
"================================================================================\n",
"DFT axis: \n",
"[0 0 1]\n",
"Quantization axis and perpendicular rotation directions:\n",
"[1 0 0] --» [array([0, 1, 0]), array([0, 0, 1])]\n",
"[0 1 0] --» [array([1, 0, 0]), array([0, 0, 1])]\n",
"[0 0 1] --» [array([1, 0, 0]), array([0, 1, 0])]\n",
"================================================================================\n",
"number of k points: 20\n",
"k point directions: xy\n",
"================================================================================\n",
"Parameters for the contour integral:\n",
"Ebot: -30\n",
"Eset: 50\n",
"Esetp: 10000\n",
"================================================================================\n",
"Atomic informations: \n",
"\n",
"\n",
"Not yet specified.\n",
"\n",
"\n",
"================================================================================\n",
"Exchange [meV]\n",
"--------------------------------------------------------------------------------\n",
"Atom1 Atom2 [i j k] d [Ang]\n",
"--------------------------------------------------------------------------------\n",
"[3]Fe(2) [4]Fe(2) [0 0 0] d [Ang] Not yet.\n",
"Isotropic: -60.89330922309355\n",
"DMI: [-5.95741551e+00 7.27737654e+00 6.90431275e-04 -8.11057566e-04\n",
" -5.49031203e-06]\n",
"Symmetric-anisotropy: [-9.32966923e-01 -8.92579299e-04 -2.04258659e-06]\n",
"\n",
"[3]Fe(2) [5]Fe(2) [0 0 0] d [Ang] Not yet.\n",
"Isotropic: -60.55651225519789\n",
"DMI: [-0.34565796 0.65919757 0.07106945 -6.23149871 -0.0424978 ]\n",
"Symmetric-anisotropy: [ 3.78506176e+00 -6.13838308e+00 3.59037036e-03]\n",
"\n",
"================================================================================\n",
"Runtime information: \n",
"Total runtime: 109.200008125\n",
"--------------------------------------------------------------------------------\n",
"Initial setup: 0.15450520899999987\n",
"Hamiltonian conversion and XC field extraction: 0.579 s\n",
"Pair and site datastructure creatrions: 0.009 s\n",
"k set cration and distribution: 0.049 s\n",
"Rotating XC potential: 0.221 s\n",
"Greens function inversion: 108.089 s\n",
"Calculate energies and magnetic components: 0.099 s\n"
]
}
],
3 months ago
"source": [
"if rank == root_node:\n",
" # iterate over the magnetic entities\n",
" for tracker, mag_ent in enumerate(magnetic_entities):\n",
" # iterate over the quantization axes\n",
" for i, Gii in enumerate(mag_ent[\"Gii\"]):\n",
" storage = []\n",
" # iterate over the first and second order local perturbations\n",
" for Vu1, Vu2 in zip(mag_ent[\"Vu1\"][i], mag_ent[\"Vu2\"][i]):\n",
3 months ago
" # The Szunyogh-Lichtenstein formula\n",
" traced = np.trace((Vu2 @ Gii + 0.5 * Gii @ Vu1 @ Gii), axis1=1, axis2=2)\n",
3 months ago
" # evaluation of the contour integral\n",
" storage.append(np.trapz(-1 / np.pi * np.imag(traced * cont.we)))\n",
"\n",
" # fill up the magnetic entities dictionary with the energies\n",
" mag_ent[\"energies\"].append(storage)\n",
"\n",
" # iterate over the pairs\n",
" for tracker, pair in enumerate(pairs):\n",
" # iterate over the quantization axes\n",
" for i, (Gij, Gji) in enumerate(zip(pair[\"Gij\"], pair[\"Gji\"])):\n",
" site_i = magnetic_entities[pair[\"ai\"]]\n",
" site_j = magnetic_entities[pair[\"aj\"]]\n",
"\n",
" storage = []\n",
" # iterate over the first order local perturbations in all possible orientations for the two sites\n",
" for Vui in site_i[\"Vu1\"][i]:\n",
" for Vuj in site_j[\"Vu1\"][i]:\n",
" # The Szunyogh-Lichtenstein formula\n",
" traced = np.trace((Vui @ Gij @ Vuj @ Gji), axis1=1, axis2=2)\n",
" # evaluation of the contour integral\n",
" storage.append(np.trapz(-1 / np.pi * np.imag(traced * cont.we)))\n",
"\n",
" # fill up the pairs dictionary with the energies\n",
" pairs[tracker][\"energies\"].append(storage)\n",
"\n",
" end_time = timer()\n",
"\n",
" print(\"############################### GROGU OUTPUT ###################################\")\n",
" print(\"================================================================================\")\n",
" print(\"Input file: \")\n",
" print(simulation_parameters[\"path\"])\n",
" print(\"Number of nodes in the parallel cluster: \", simulation_parameters[\"parallel_size\"])\n",
" print(\"================================================================================\")\n",
" try:\n",
" print(\"Cell [Ang]: \")\n",
" print(simulation_parameters[\"geom\"].cell)\n",
" except:\n",
" print(\"Geometry could not be read.\")\n",
" print(\"================================================================================\")\n",
" print(\"DFT axis: \")\n",
" print(simulation_parameters[\"scf_xcf_orientation\"])\n",
" print(\"Quantization axis and perpendicular rotation directions:\")\n",
" for ref in ref_xcf_orientations:\n",
" print(ref[\"o\"], \" --» \", ref[\"vw\"])\n",
" print(\"================================================================================\")\n",
" print(\"number of k points: \", simulation_parameters[\"kset\"])\n",
" print(\"k point directions: \", simulation_parameters[\"kdirs\"])\n",
" print(\"================================================================================\")\n",
" print(\"Parameters for the contour integral:\")\n",
" print(\"Ebot: \", simulation_parameters[\"ebot\"])\n",
" print(\"Eset: \", simulation_parameters[\"eset\"])\n",
" print(\"Esetp: \", simulation_parameters[\"esetp\"])\n",
" print(\"================================================================================\")\n",
" print(\"Atomic informations: \")\n",
" print(\"\")\n",
" print(\"\")\n",
" print(\"Not yet specified.\")\n",
" print(\"\")\n",
" print(\"\")\n",
" print(\"================================================================================\")\n",
" print(\"Exchange [meV]\")\n",
" print(\"--------------------------------------------------------------------------------\")\n",
" print(\"Atom1 Atom2 [i j k] d [Ang]\")\n",
" print(\"--------------------------------------------------------------------------------\")\n",
" for pair in pairs:\n",
" J_iso, J_S, D = calculate_exchange_tensor(pair)\n",
" J_iso = J_iso * sisl.unit_convert(\"eV\", \"meV\")\n",
" J_S = J_S * sisl.unit_convert(\"eV\", \"meV\")\n",
" D = D * sisl.unit_convert(\"eV\", \"meV\")\n",
" \n",
" print(print_atomic_indices(pair, magnetic_entities, dh))\n",
" print(\"Isotropic: \", J_iso)\n",
" print(\"DMI: \", D)\n",
" print(\"Symmetric-anisotropy: \", J_S)\n",
" print(\"\")\n",
" \n",
" print(\"================================================================================\")\n",
" print(\"Runtime information: \")\n",
" print(\"Total runtime: \", end_time - start_time)\n",
" print(\"--------------------------------------------------------------------------------\")\n",
" print(\"Initial setup: \", setup_time - start_time)\n",
" print(f\"Hamiltonian conversion and XC field extraction: {H_and_XCF_time - setup_time:.3f} s\")\n",
" print(f\"Pair and site datastructure creatrions: {site_and_pair_dictionaries_time - H_and_XCF_time:.3f} s\")\n",
" print(f\"k set cration and distribution: {k_set_time - site_and_pair_dictionaries_time:.3f} s\")\n",
" print(f\"Rotating XC potential: {reference_rotations_time - k_set_time:.3f} s\")\n",
" print(f\"Greens function inversion: {green_function_inversion_time - reference_rotations_time:.3f} s\")\n",
" print(f\"Calculate energies and magnetic components: {end_time - green_function_inversion_time:.3f} s\")\n"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 9,
3 months ago
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (3105939143.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[9], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m ========================================\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"========================================\n",
" \n",
"Atom Angstrom\n",
"# Label, x y z Sx Sy Sz #Q Lx Ly Lz Jx Jy Jz\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Te1 1.8955 1.0943 13.1698 -0.0000 0.0000 -0.1543 # 5.9345 -0.0000 0.0000 -0.0537 -0.0000 0.0000 -0.2080 \n",
"Te2 1.8955 1.0943 7.4002 0.0000 -0.0000 -0.1543 # 5.9345 0.0000 -0.0000 -0.0537 0.0000 -0.0000 -0.2080 \n",
"Ge3 -0.0000 2.1887 10.2850 0.0000 0.0000 -0.1605 # 3.1927 -0.0000 0.0000 0.0012 0.0000 0.0000 -0.1593 \n",
"Fe4 -0.0000 0.0000 11.6576 0.0001 -0.0001 2.0466 # 8.3044 0.0000 -0.0000 0.1606 0.0001 -0.0001 2.2072 \n",
"Fe5 -0.0000 0.0000 8.9124 -0.0001 0.0001 2.0466 # 8.3044 -0.0000 0.0000 0.1606 -0.0001 0.0001 2.2072 \n",
"Fe6 1.8955 1.0944 10.2850 0.0000 0.0000 1.5824 # 8.3296 -0.0000 -0.0000 0.0520 -0.0000 0.0000 1.6344 \n",
"==================================================================================================================================\n",
" \n",
"Exchange meV\n",
"--------------------------------------------------------------------------------\n",
"# at1 at2 i j k # d (Ang)\n",
"--------------------------------------------------------------------------------\n",
"Fe4 Fe5 0 0 0 # 2.7452\n",
"Isotropic -82.0854\n",
"DMI 0.12557 -0.00082199 6.9668e-08\n",
"Symmetric-anisotropy -0.60237 -0.83842 -0.00032278 -1.2166e-05 -3.3923e-05\n",
"--------------------------------------------------------------------------------\n",
"Fe4 Fe6 0 0 0 # 2.5835\n",
"Isotropic -41.9627\n",
"DMI 1.1205 -1.9532 0.0018386\n",
"Symmetric-anisotropy 0.26007 -0.00013243 0.12977 -0.069979 -0.042066\n",
"--------------------------------------------------------------------------------\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
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"zeroline": false,
"zerolinecolor": "#ccc",
"zerolinewidth": 1
}
}
},
"xaxis": {
"scaleanchor": "y",
"scaleratio": 1,
"title": {
"text": "X axis [Ang]"
}
},
"yaxis": {
"title": {
"text": "Y axis [Ang]"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
3 months ago
"source": [
"import sisl.viz\n",
"dh.geometry.tile(2,1).plot(axes=\"xy\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
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"template": {
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},
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}
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}
},
"metadata": {},
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}
],
"source": [
"dh.geometry.plot(axes=\"xz\")"
3 months ago
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xyz[-3:]: red, green, blue\n"
]
},
{
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"text/plain": [
"<Figure size 1500x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"coords = dh.xyz[-3:]\n",
"\n",
"\n",
"plt.figure(figsize=(15,5))\n",
"plt.subplot(131)\n",
"plt.scatter(coords[:,0], coords[:,2], color=[\"r\", \"g\", \"b\"])\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"z\")\n",
"plt.subplot(132)\n",
"plt.scatter(coords[:,1], coords[:,2], color=[\"r\", \"g\", \"b\"])\n",
"plt.xlabel(\"y\")\n",
"plt.ylabel(\"z\")\n",
"plt.subplot(133)\n",
"plt.scatter(coords[:,0], coords[:,1], color=[\"r\", \"g\", \"b\"])\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"print(\"xyz[-3:]: red, green, blue\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
3 months ago
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
3 months ago
}
},
"nbformat": 4,
"nbformat_minor": 2
}