{
"cells": [
{
"cell_type": "markdown",
"id": "763534d0-0530-4bcd-be43-5d50acfba37d",
"metadata": {},
"source": [
"## Calculation of isotropic superconductivity\n",
"\n",
"Author: S. Mishra (v1.1, 06/01/2024)
\n",
"Revision: S. Mishra (v1.2, 11/06/2024)
\n",
"\n",
"In this notebook, we calculate the superconductivity properties of FCC Pb by solving the\n",
"isotropic Migdal-Eliashberg equations. The theory related to this tutorial can be found in \n",
"[Phys. Rev. B **87**, 024505 (2013)](https://doi.org/10.1103/PhysRevB.87.024505). \n",
"\n",
"Electrons and phonons are computed with Quantum ESPRESSO (QE), maximally-localized Wannier functions are computed with Wannier90, and superconductivity is computed with EPW. Using EPW, we do the following:\n",
"\n",
"1. We interpolate the electron-phonon matrix elements to fine **k** and **q** grids;\n",
"2. Solve the Migdal-Eliashberg equations ignoring the momentum dependence in the imaginary and real frequencies at different temperatures;\n",
"3. We also showed the nesting function and momentum dependence of electron-phonon coupling. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d382c875-344d-4572-8003-6f97ce062237",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/shashi-phy/codes/q-e/bin\n"
]
}
],
"source": [
"import numpy as np\n",
"import time, sys, os\n",
"sys.path.insert(0,str(os.getcwd())+'/../')\n",
"from EPWpy import EPWpy\n",
"from EPWpy.plotting import plot_supercond\n",
"pathQE='/home/shashi-phy/codes/q-e/bin'\n",
"print(pathQE)"
]
},
{
"cell_type": "markdown",
"id": "064c7366-022e-4be2-86fa-a97508e8c91c",
"metadata": {},
"source": [
"Below we define constants that will remail unchanged throughout the Notebook. The object `pb` is created as an instance of the `EPWpy` class. This object will contain everything that we need to execute and analyze the calculations."
]
},
{
"cell_type": "markdown",
"id": "38c941db-0ea6-4850-ab12-d6f6ec563449",
"metadata": {},
"source": [
"#### Set paths to relevant directories:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "af48af95",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum number of cores to be used: 4\n"
]
}
],
"source": [
"prefix='pb'\n",
"pseudo='/home/shashi-phy/codes/epw_notebook/pseudos'\n",
"# Maximum number of cores to be used\n",
"cores = 4\n",
"print('Maximum number of cores to be used:', cores)"
]
},
{
"cell_type": "markdown",
"id": "c3811348",
"metadata": {},
"source": [
"#### Create Calculation Object"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "72a923e0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" \n",
" \n",
" -*#*- ...............- \n",
" .+*= .+%*-=%%: .=#*- -===============-:.\n",
" :*%=*%%- *%* #%* :+%+-%%+ .:. -=. :==-. \n",
" -%S -%%*: :#%. -%%-. -##: #%* -=. :==- \n",
" .. .%S: +%%%%*. :*%%%#= %%= -=. :==- \n",
" :=#%%*- .#S- .. .%%= :*#*: -=. :==- \n",
" -%S:.=#%%*==%# *%%=::=##-+%%. . .=-. :==- .= \n",
" :%%- .-+++: -+##*=. =%S :-::==: .==- --.\n",
" *%# #%+ -=--:. .----:. \n",
" :%%- -%S. \n",
" .-=*####SS%#########: -###########*+: +#######= =%SS####+::. \n",
" =+#**%SSSSSSSSSSSSSSSS= =SSSSSSSSSSSSSSS= #SSSSSSS*. +SSSSSSSS%%%%- \n",
" *%% =SSS.. .SSS= SSS=. .:+SSS+ -SSS:. .-::. .SSS+. #%* \n",
" #%#. =SSS. *S# *S%: SSS= %SS%. .SSS= #SSS%. :SSS: =%#. \n",
" *%%: =SSS#*#SSS- SSS= .+SSS+. %SS*.=SSSSS+ +SS%.:+%+ \n",
" +%%:=SSSSSSSSS- SSSSSSSSSSSSS=. +SSS:SSS%SSS:%SS*-#%= \n",
" ....#S==SSS:..SSS: =+- SSS%######+=. -SSS%SS%.#SS%SSS==%%=. \n",
" .:+##%%#*- =SSS. ::. :SSS. SSS=. SSSSSS:..SSSSSS: :*%%%*=- \n",
" #%+. -+#SSS*+++++++*SSS: .=+SSS#++++: %SSSS+. =SSSS%. :-+#%%+ \n",
" #%* .SSSSSSSSSSSSSSSSSS: *SSSSSSSSSSS. +SSS%. %SSS*. . .%%= \n",
" =%S :::::::::::::::::. .:::::::::. :::. :::. =+=-.#%+ \n",
" -%S: =+===#S: \n",
" ==*------------------------------=========+++++++++++++++++++++++========++-+## \n",
" =+++++++++++*******++++++++++++++++========------------------==========+++++++- \n",
"-- -- -- -- -- -- -- -- -- -- -- -- Structure Info -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
"0\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
"prefix: pb\n",
"pseudopotential: pb_s.UPF\n",
"pseudopotential directory: '/home/shashi-phy/codes/epw_notebook/pseudos'\n"
]
}
],
"source": [
"pb=EPWpy.EPWpy({ 'prefix':prefix,\n",
" 'calculation':'\\'scf\\'',\n",
" 'ibrav':2,\n",
" 'celldm(1)':'9.222558',\n",
" 'nat':1,\n",
" 'ntyp':1,\n",
" 'atomic_species':['Pb'],\n",
" 'atomic_pos':np.array([[0.0, 0.0, 0.0]]), \n",
" 'atomic_position_type':'crystal', \n",
" 'atoms':['Pb'],\n",
" #'pseudo_auto':True,\n",
" 'pseudo':['pb_s.UPF'],\n",
" 'ecutwfc':'90',\n",
" 'ecutrho':'360',\n",
" 'smearing':'\\'mp\\'',\n",
" 'occupations':'\\'smearing\\'',\n",
" 'degauss':'0.025',\n",
" 'pseudo_dir':'\\''+str(pseudo)+'\\''},\n",
" code=pathQE,\n",
" env='mpirun',system = prefix)\n",
"#######Printing relevant info ######\n",
"pseudopot=pb.__dict__['pw_atomic_species']['pseudo'][0]\n",
"print('prefix:', pb.__dict__['pw_control']['prefix'])\n",
"print('pseudopotential:', pb.__dict__['pw_atomic_species']['pseudo'][0])\n",
"print('pseudopotential directory:', pb.__dict__['pw_control']['pseudo_dir'])\n",
"pb.run_serial= True\n",
"#app = pb.display_lattice(supercell=[2,2,1])\n",
"#app.run()"
]
},
{
"cell_type": "markdown",
"id": "b0acd779-98b9-44de-8ca1-39a3ea62ac4f",
"metadata": {},
"source": [
"#### Self-consistent field (SCF) calculation\n",
"\n",
"Here we perform the self-consistent field calculation to obtain the electron charge density of pb in the ground state. The calculation consists of three separate steps:\n",
"1. Apply the method `scf` to the object `pb`. This step specifies runtime parameters for an SCF calculation on pb\n",
"2. Based on the properties defined at step 1 as well as other properties that are set by default within EPWpy, the method `prepare` creates the input file needed by QE\n",
"3. The method `run` applied to the object `pb` instructs QE to perform the SCF calculation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bd97d945",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-- -- -- -- -- -- -- -- -- -- -- Calculation: scf -- -- -- -- -- -- -- -- -- -- -- \n",
"Running scf |████████████████████████████████████████| in 38.1s (0.03/s) \n",
"\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
]
}
],
"source": [
"pb.scf(name='scf', kpoints={'kpoints':[[14,14,14]]})\n",
"#####################################\n",
"pb.prepare(1,type_run='scf')\n",
"pb.run(cores)"
]
},
{
"cell_type": "markdown",
"id": "1d0c3fdf-f9e1-4cae-8dba-fe3a255f1f5d",
"metadata": {},
"source": [
"#### Band Structure Calculation\n",
"\n",
"We now calculate the band structure of the material.\n",
"\n",
"The band structure is the eigenenergies of KS orbitals at various points in the Brillouin zone.\n",
"We choose a path that passes through all of the high symmetry $k$-points and plot along that.\n",
"\n",
"$\\Big(-\\frac{\\hbar^2}{2m}|k+G|^2+V_{KS}(G-G')\\Big)\\phi_v(k)=\\epsilon_v(k)\\phi_v(k)$ \n",
"\n",
"This calculation is not strictly necessary to compute the superconductivity, but it is useful to understand the electronic structure of the system under consideration."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8e5832d2",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-- -- -- -- -- -- -- -- -- -- -- Calculation: bs -- -- -- -- -- -- -- -- -- -- -- \n",
"on 1: running: mpirun -np 4 /home/shashi-phy/codes/q-e/bin/pw.x -nk 2 -nt 2 -in bs.in > bs.out\n",
"Running bs |████████████████████████████████████████| in 31.3s (0.04/s) \n",
"\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
]
}
],
"source": [
"pb.verbosity = 2\n",
"pb.scf(control={'calculation':'\\'bands\\''},\n",
" kpoints=\n",
" {'kpoints':[['0.000', '0.000', '0.000', '20'],\n",
" ['0.000', '0.500', '0.500', '20'],\n",
" ['0.250', '0.500', '0.750', '20'],\n",
" ['0.500', '0.500', '0.500', '20'],\n",
" ['0.375', '0.375', '0.750', '20'],\n",
" ['0.000', '0.000', '0.000', '20'],\n",
" ['0.500', '0.500', '0.500', '1'],\n",
" ],'kpoints_type':'{crystal_b}'},\n",
" name='bs')\n",
"########################################\n",
"pb.prepare(1,type_run='bs')\n",
"pb.run(cores,type_run='bs')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e6896267-0fc0-4096-80d8-442fa49df6e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-- -- -- -- -- -- -- -- -- -- -- Calculation: bands -- -- -- -- -- -- -- -- -- -- -- \n",
"on 1: running: mpirun -np 4 /home/shashi-phy/codes/q-e/bin/bands.x -in bands.in > bands.out\n",
"Running bands |████████████████████████████████████████| in 0.7s (25.09/s) \n",
"\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
]
}
],
"source": [
"pb.bands(name='bands')\n",
"pb.prepare(1,type_run='bands')\n",
"# Running on too many cores will cause the job to fail\n",
"pb.run(4,type_run='bands')"
]
},
{
"cell_type": "markdown",
"id": "96df6fa9-782e-451a-817f-a9bc28203d79",
"metadata": {},
"source": [
"#### Plotting band structure"
]
},
{
"cell_type": "markdown",
"id": "f944420d",
"metadata": {},
"source": [
"The band structure is given in file `bands.dat` and plottable bands are written to `bands.dat.gnu` file, which\n",
"contains two columns. The first column is the distance along the $k$-path, and the second column is the energies (eV). \n",
"We will use this file for plotting as below."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "407b9f35-8b4e-4d41-ba3d-9b3f5e9cc6ec",
"metadata": {},
"outputs": [
{
"data": {
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",
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