{
"cells": [
{
"cell_type": "markdown",
"id": "9479fd4b-d8cb-4771-808d-92a4703f0769",
"metadata": {},
"source": [
"## Calculation of anisotropic superconductivity\n",
"\n",
"Author: S. Mishra (v1.1, 06/01/2024)
\n",
"Revision: S. Mishra (v1.2, 11/25/2024)
\n",
"\n",
"In this notebook, we calculate the superconductivity properties of MgB$_2$ by solving the\n",
"anisotropic 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) which are also briefly discussed in this notebook. \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 in the imaginary and real frequencies at different temperatures;\n",
"3. We compared the solution of Migdal-Eliashberg equations with and without assuming the constant density of states. \n",
"\n",
"NOTE: For this example, we use very coarse $k$- and $q$-grids. For actual calculations, one should use much denser grids."
]
},
{
"cell_type": "markdown",
"id": "24be6618",
"metadata": {},
"source": [
"#### Set up working environment"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "053a3a87",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/shashi-phy/codes/q-e/bin\n"
]
}
],
"source": [
"import numpy as np\n",
"import 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": "bc944448-af91-4b93-8999-2d5da8cb0281",
"metadata": {},
"source": [
"Below we define constants that will remail unchanged throughout the Notebook. The object `mgb2` 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": "b939a045-6aca-4edf-9c27-7c8757ce8188",
"metadata": {},
"source": [
"#### Set paths to relevant directories:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ed722f47",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum number of cores to be used: 8\n"
]
}
],
"source": [
"prefix='mgb2'\n",
"pseudo='/home/shashi-phy/codes/epw_notebook/pseudos'\n",
"# Maximum number of cores to be used\n",
"cores = 8\n",
"print('Maximum number of cores to be used:', cores)"
]
},
{
"cell_type": "markdown",
"id": "c0a32746",
"metadata": {},
"source": [
"#### Create Calculation Object\n",
"Define general calculation parameters to be used throughout the workflow:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "53c2b435",
"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",
"2\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
"prefix: mgb2\n",
"pseudopotential: Mg.pz-n-vbc.UPF\n",
"pseudopotential directory: '/home/shashi-phy/codes/epw_notebook/pseudos'\n"
]
}
],
"source": [
"mgb2=EPWpy.EPWpy({'prefix':prefix,\n",
" 'restart_mode':'\\'from_scratch\\'',\n",
" 'calculation':'\\'scf\\'',\n",
" 'ibrav':4,\n",
" 'nat':3,\n",
" 'ntyp':2,\n",
" 'atomic_species':['Mg', 'B'],\n",
" 'celldm(1)':'5.8260252227888',\n",
" 'celldm(3)':'1.1420694129095',\n",
" 'atoms':['Mg', 'B', 'B'],\n",
" 'atomic_pos':np.array([[0.0, 0.0, 0.0], [0.3333,0.66667,0.5],[0.66667,0.3333,0.5]]), \n",
" 'atomic_position_type':'crystal', \n",
" 'mass':[24.305, 10.811],\n",
" 'pseudo':['Mg.pz-n-vbc.UPF', 'B.pz-vbc.UPF'],\n",
" 'ecutwfc':'40',\n",
" 'ecutrho':'160',\n",
" 'smearing':'\\'mp\\'',\n",
" 'occupations':'\\'smearing\\'',\n",
" 'degauss':'0.05',\n",
" 'pseudo_dir':'\\''+str(pseudo)+'\\''},\n",
" code=pathQE,\n",
" env='mpirun',system = prefix)\n",
"#######Printing any attribute######\n",
"pseudopot=mgb2.__dict__['pw_atomic_species']['pseudo']\n",
"print('prefix:', mgb2.__dict__['pw_control']['prefix'])\n",
"print('pseudopotential:', mgb2.__dict__['pw_atomic_species']['pseudo'][0])\n",
"print('pseudopotential directory:', mgb2.__dict__['pw_control']['pseudo_dir'])"
]
},
{
"cell_type": "markdown",
"id": "22af6968-f42e-4409-9321-2b08b63d7dc4",
"metadata": {},
"source": [
"### Self-Consistent Field (SCF) Calculations\n",
"\n",
"We first solve the Kohn-Sham equations to obtain the Kohn-Sham orbitals $\\phi_v(r)$, where $r$ is the electronic position (generally a mesh grid), and $R$ is the position of ions.\n",
"\n",
"$E[\\phi_v,R]=-\\frac{\\hbar^2}{2m}\\sum_v{\\int{\\phi_v^\\star(r)\\nabla^2\\phi_v(r)dr}+\\int{V_R(r)n(r)dr}+\\frac{e^2}{2}\\int{\\frac{n(r)n(r')}{|r-r'|}drdr'}+E_{xc}[n(r')]+\\sum_{I\\neq J}{\\frac{e^2}{2}\\frac{Z_IZ_J}{|R_I-R_J|}}}$\n",
"\n",
"We minimize $E(R)=min(E[\\phi_v,R])$\n",
"\n",
"Where, $\\Big(-\\frac{\\hbar^2}{2m}\\nabla^2+V_{KS}(r)\\Big)\\phi_v(r)=\\epsilon_v\\phi_v(r)$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a9b4699f",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-- -- -- -- -- -- -- -- -- -- -- Calculation: scf -- -- -- -- -- -- -- -- -- -- -- \n",
"Running scf |████████████████████████████████████████| in 0.0s (4366.51/s) \n",
"\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
]
}
],
"source": [
"mgb2.scf(kpoints={'kpoints':[[8,8,8]],'type':'automatic'},name='scf')\n",
"mgb2.prepare(1,type_run='scf')\n",
"mgb2.run(cores, type_run='scf')"
]
},
{
"cell_type": "markdown",
"id": "f07f6473-661e-461f-a16c-a13ef62d8f4c",
"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 the high symmetry $k$-points.\n",
"\n",
"$\\Big(-\\frac{\\hbar^2}{2m}|k+G|^2+V_{KS}(G-G')\\Big)\\phi_v(k)=\\epsilon_v(k)\\phi_v(k)$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "98e37d0d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-- -- -- -- -- -- -- -- -- -- -- Calculation: bs -- -- -- -- -- -- -- -- -- -- -- \n",
"Running bs |████████████████████████████████████████| in 0.0s (5391.53/s) \n",
"\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
]
}
],
"source": [
"mgb2.scf(control={'calculation':'\\'bands\\''},\n",
" kpoints={'kpoints':[['0.0', '0.0', '0.0', '20'],\n",
" ['0.333', '0.333', '0.0', '20'],\n",
" ['0.5', '0.0', '0.0', '20'],\n",
" ['0.0', '0.0', '0.0', '20'],\n",
" ['0.0', '0.0', '0.5', '20'],\n",
" ['0.333', '0.333', '0.5', '20'],\n",
" ['0.5','0.0','0.5','20'],\n",
" ['0.0', '0.0', '0.5', '20']],\n",
" 'kpoints_type':'{crystal_b}'},\n",
" name='bs')\n",
"\n",
"########################################\n",
"mgb2.prepare(1,type_run='bs')\n",
"mgb2.run(cores,type_run='bs')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "639a98c1-16e0-4f61-ad87-d7273baa0789",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-- -- -- -- -- -- -- -- -- -- -- Calculation: bands -- -- -- -- -- -- -- -- -- -- -- \n",
"Running bands |████████████████████████████████████████| in 0.0s (4166.66/s) \n",
"\n",
"-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
]
}
],
"source": [
"mgb2.bands(name='bands')\n",
"mgb2.prepare(1,type_run='bands')\n",
"# Running on too many cores will cause the job to fail\n",
"mgb2.run(cores,type_run='bands')"
]
},
{
"cell_type": "markdown",
"id": "0d73472e-4624-4cd4-b188-f84b2339470f",
"metadata": {},
"source": [
"### Plotting band structure"
]
},
{
"cell_type": "markdown",
"id": "c46f8087-f153-418f-ba85-e9fd72987267",
"metadata": {},
"source": [
"The band structure is given in the file `bands.dat` and plottable bands are written to `bands.dat.gnu`, which\n",
"contains two columns The first column is the distance along the $k$‐path, and the second column is the energies (eV). We will use this file for plotting as below."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d6390518-6816-41fc-94f3-03ba566b42c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plot saved as: mgb2_band.pdf\n"
]
},
{
"data": {
"image/png": 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",
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