{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculation of polaron migration barriers using linear interpolation\n",
    "In this example we show how to calculate polaron migration barrier on the example of LiFePO4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "\n",
    "from siman.calc_manage import smart_structure_read,  add, res\n",
    "from siman.geo import interpolate\n",
    "from siman.set_functions import read_vasp_sets\n",
    "from siman.database import write_database, read_database\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = smart_structure_read('in/POSCAR_LiFePO4_1u_1_end') #read LiFePO4 structure\n",
    "# st.printme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- POSCAR was written to /hdd/home/aksenov/Simulation_wrapper/siman/tutorials/xyz/POSCAR__LiFePO4_1u_1_endpol27 \n",
      "\n",
      "-- POSCAR was written to /hdd/home/aksenov/Simulation_wrapper/siman/tutorials/xyz/POSCAR__LiFePO4_1u_1_endpol28 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/hdd/home/aksenov/Simulation_wrapper/siman/tutorials/xyz/POSCAR__LiFePO4_1u_1_endpol28'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st1 = st.localize_polaron(26,  -0.2) # displace surrounding oxygens by 0.2 A in the direction of iron, corresponding to oxidation\n",
    "st2 = st.localize_polaron(27, -0.2)\n",
    "st1.write_poscar() # write POSCAR to check in jmol that the correct displacement was performed\n",
    "st2.write_poscar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- POSCAR was written to xyz/1.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/2.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/3.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/4.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/5.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/6.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/7.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/8.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/9.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/10.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/11.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/12.POSCAR \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<siman.core.structure.Structure at 0x7facc7b7b400>,\n",
       " <siman.core.structure.Structure at 0x7facc7a71ba8>,\n",
       " <siman.core.structure.Structure at 0x7fac9b2146d8>,\n",
       " <siman.core.structure.Structure at 0x7fac9b214cc0>,\n",
       " <siman.core.structure.Structure at 0x7fac9b2146a0>,\n",
       " <siman.core.structure.Structure at 0x7fac9b2144e0>,\n",
       " <siman.core.structure.Structure at 0x7fac9b214278>,\n",
       " <siman.core.structure.Structure at 0x7fac9b214b00>,\n",
       " <siman.core.structure.Structure at 0x7fac9b214be0>,\n",
       " <siman.core.structure.Structure at 0x7fac9b214550>,\n",
       " <siman.core.structure.Structure at 0x7fac9b2145c0>,\n",
       " <siman.core.structure.Structure at 0x7fac9b214e10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# *images* is the number of intermediate structures\n",
    "# *write_poscar* write files to folder *poscar_folder*  \n",
    "interpolate(st1, st2, images = 10, write_poscar = 1, omit_edges = 0, poscar_folder = 'xyz/' ) # create intermediate structures by linerar interpolation between st1 and st2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Check all intermediate images by opening them simulteniously in Jmol and clicking next frame\n",
    "2. Make a shell script that launches VASP for:\n",
    "   a) for 1.POSCAR, 2.POSCAR ... 12.POSCAR in a loop (forward branch)\n",
    "   b) for 12.POSCAR 11.POSCAR ... 1.POSCAR in a loop (backward branch)\n",
    "3. Plot Total energy vs structure number for forward and backward branches on one figure\n",
    "4. Make sure that  ISTART\t= 1; LWAVE  =  .TRUE.; ICHARG = 0; in your INCAR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Attention! You have chosen to override set 1\n",
      " \n",
      "\n",
      "Warning! You did not change  ISIF  in 1 set\n",
      " \n",
      "\n",
      "\n",
      "Attention! You have chosen to override set 1m\n",
      " \n",
      "\n",
      "\n",
      "Attention! You have chosen to override set 1u\n",
      " \n",
      "\n",
      "-- s.params['ISTART']             = 1  \n",
      "-- s.params['NELM']               = 50  \n",
      "-- s.params['EDIFF']              = 1e-05  \n",
      "-- s.params['NSW']                = 25  \n",
      "-- s.params['EDIFFG']             = -0.025  \n",
      "-- s.params['IBRION']             = 1  \n",
      "-- s.params['ISIF']               = 2  \n",
      "-- s.params['PREC']               = Normal  \n",
      "-- s.params['ALGO']               = Normal  \n",
      "-- s.params['ENCUT']              = 400  \n",
      "-- s.params['ENAUG']              = 700.0  \n",
      "-- s.params['KSPACING']           = 0.2  \n",
      "-- s.params['KGAMMA']             = .TRUE.  \n",
      "-- s.params['LREAL']              = Auto  \n",
      "-- s.params['ISMEAR']             = 0  \n",
      "-- s.params['SIGMA']              = 0.1  \n",
      "-- s.params['LPLANE']             = .TRUE.  \n",
      "-- s.params['NPAR']               = 1  \n",
      "-- s.params['ICHARG']             = 1  \n",
      "-- s.params['LORBIT']             = 11  \n",
      "-- s.params['ISPIN']              = 2  \n",
      "-- s.params['GGA_COMPAT']         = .FALSE.  \n",
      "-- s.params['LDAU']               = .TRUE.  \n",
      "-- s.params['LDAUTYPE']           = 2  \n",
      "-- s.params['LDAUL']              = {'Cu': 2, 'Ti': 2, 'Nb': 2, 'Co': 2, 'Fe': 2, 'Ni': 2, 'Mn': 2, 'V': 2, 'Cr': 2, 'Mo': 2}  \n",
      "-- s.params['LDAUU']              = {'Cu': 4.0, 'Ti': 0, 'Nb': 1.5, 'Co': 3.4, 'Fe': 4.0, 'Ni': 6.2, 'Mn': 3.9, 'V': 3.1, 'Cr': 3.5, 'Fe/S': 1.9, 'Mo': 3}  \n",
      "-- s.params['LDAUJ']              = {'Cu': 0.0, 'Ti': 0.0, 'Nb': 0.0, 'Co': 0.0, 'Fe': 0.0, 'Ni': 0.0, 'Mn': 0.0, 'V': 0.0, 'Cr': 0.0, 'Fe/S': 0, 'Mo': 0}  \n",
      "-- s.params['LDAUPRINT']          = 2  \n",
      "-- s.params['LASPH']              = .TRUE.  \n",
      "-- s.params['LMAXMIX']            = 4  \n",
      "-- ngkpt: None \n",
      "\n",
      "-- POTDIR: {300: 'void', 200: 'octa', 0: 'n', 1: 'H', 2: 'He', 3: 'Li', 4: 'Be', 5: 'B', 6: 'C', 7: 'N', 8: 'O', 9: 'F', 10: 'Ne', 11: 'Na', 12: 'Mg', 13: 'Al', 14: 'Si', 15: 'P', 16: 'S', 17: 'Cl', 18: 'Ar', 19: 'K', 20: 'Ca', 21: 'Sc', 22: 'Ti', 23: 'V', 24: 'Cr', 25: 'Mn', 26: 'Fe', 27: 'Co', 28: 'Ni', 29: 'Cu', 30: 'Zn', 31: 'Ga', 32: 'Ge', 33: 'As', 34: 'Se', 35: 'Br', 36: 'Kr', 37: 'Rb', 38: 'Sr', 39: 'Y', 40: 'Zr', 41: 'Nb', 42: 'Mo', 43: 'Tc', 44: 'Ru', 45: 'Rh', 46: 'Pd', 47: 'Ag', 48: 'Cd', 49: 'In', 50: 'Sn', 51: 'Sb', 52: 'Te', 53: 'I', 54: 'Xe', 55: 'Cs', 56: 'Ba', 57: 'La', 58: 'Ce', 59: 'Pr', 60: 'Nd', 61: 'Pm', 62: 'Sm', 63: 'Eu', 64: 'Gd', 65: 'Tb', 66: 'Dy', 67: 'Ho', 68: 'Er', 69: 'Tm', 70: 'Yb', 71: 'Lu', 72: 'Hf', 73: 'Ta', 74: 'W', 75: 'Re', 76: 'Os', 77: 'Ir', 78: 'Pt', 79: 'Au', 80: 'Hg', 81: 'Tl', 82: 'Pb', 83: 'Bi', 84: 'Po', 85: 'At', 86: 'Rn', 87: 'Fr', 88: 'Ra', 89: 'Ac', 90: 'Th', 91: 'Pa', 92: 'U', 93: 'Np', 94: 'Pu', 95: 'Am', 96: 'Cm', 97: 'Bk', 98: 'Cf', 99: 'Es', 100: 'Fm', 101: 'Md', 102: 'No', 103: 'Lr', 104: 'Rf', 105: 'Db', 106: 'Sg', 107: 'Bh', 108: 'Hs', 109: 'Mt', 110: 'Ds', 111: 'Rg', 112: 'Cn', 114: 'Uuq', 116: 'Uuh'} \n"
     ]
    }
   ],
   "source": [
    "read_database() # read saved database if available\n",
    "#Creating new sets for DFT+U calculations from predifined set 'static'\n",
    "dftu_packet = {'ISTART'   :1,   'ICHARG':1,  'LDAUTYPE':2, 'LASPH':'.TRUE.', \n",
    "                'LDAUPRINT':2, 'LMAXMIX' :4, 'LDAU' :'.TRUE.',\n",
    "                'LDAUL':{'Cu':2, 'Ti':2,   'Nb':2,   'Co':2  , 'Fe':2  , 'Ni':2  , 'Mn':2  , 'V':2   , 'Cr':2, 'Mo':2 },\n",
    "                'LDAUU':{'Cu':4.0, 'Ti':0,   'Nb':1.5, 'Co':3.4, 'Fe':4.0, 'Ni':6.2, 'Mn':3.9, 'V':3.1 , 'Cr':3.5, 'Fe/S':1.9, 'Mo':3 },\n",
    "                'LDAUJ':{'Cu':0.0, 'Ti':0.0, 'Nb':0.0, 'Co':0.0, 'Fe':0.0, 'Ni':0.0, 'Mn':0.0, 'V':0.0 , 'Cr':0.0, 'Fe/S':0 , 'Mo':0  } } # universal set, Jain2011 azh values, Ni from genome\n",
    "mag_packet = {\n",
    "    'GGA_COMPAT': '.FALSE.',\n",
    "    'ISPIN':2,\n",
    "    'LORBIT':11, #more info\n",
    "    'magnetic_moments':{'Ti':0.6, 'Nb':0.6, 'V':5, 'Fe':5, 'Co':5, 'Mn':5, 'Ni':5, 'Cr':5, }\n",
    "}\n",
    "\n",
    "read_vasp_sets([('1', 'static', {'ISIF':2, 'NSW':25, 'EDIFFG':-0.025}, 'override')]) #new set '1' from 'static' with 'NSW' = 2\n",
    "read_vasp_sets([('1m', '1',mag_packet , 'override')]) #new set '1m' from '1'\n",
    "read_vasp_sets([('1u', '1m',dftu_packet , 'override')]) #new set '1u' from '1m'\n",
    "header.varset['1u'].printme()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating polaron migration barrier\n",
    "The function creates linear interpolation of lattice deformation between two positions *istart* and *ind* \n",
    "and calculates two branches.\n",
    "*images* - number of intermediate images\n",
    "*istart* - number of atom (counting from zero) with small polaron in its initial position  \n",
    "*iend* - number of atom (counting from zero) with small polaron in its final position   \n",
    "*polaron_type* - 'hole' or 'electron'\n",
    "*amp* - amplitude of polaron distortion in A (displacement of ligand around the TM center), the sign is determined automatically depending on the type of the polaron\n",
    "\n",
    "### Attention! *~/tools/siman/\\**.py should be present on cluster! Copy siman/siman folder to ~/tools/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- POSCAR was written to xyz/LiFePO4.PHtest/1.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/100.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/3.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/4.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/5.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/6.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/7.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/8.POSCAR \n",
      "\n",
      "-- POSCAR was written to xyz/LiFePO4.PHtest/9.POSCAR \n",
      "\n",
      "-- Warning! File ./LiFePO4/PH//LiFePO4.PHtest/LiFePO4.PHtest.auto_created_for_polaron.1.geo was replaced \n",
      "\n",
      "-- POSCAR was written to LiFePO4/PH///LiFePO4.PHtest.1u/2.POSCAR \n",
      "\n",
      "-- Number of elements is even! trying to find all antiferromagnetic orderings: \n",
      "\n",
      "-- Total number of orderings is  6 \n",
      "\n",
      "-- check_kpoints(): Kpoint   mesh is:  [4, 6, 7] \n",
      "\n",
      "-- check_kpoints(): The actual k-spacings are  [0. 0. 0.] \n",
      "\n",
      "-- POSCAR was written to LiFePO4/PH///LiFePO4.PHtest.1u/1.POSCAR \n",
      "\n",
      "{'images': 7, 'istart': 24, 'iend': 25, 'polaron_type': 'hole', 'amp': 0.12}\n",
      "-- Attention! ngkpt =  [4, 6, 7]  is adopted from struct_des which you provided for it  LiFePO4.PHtest  and kspacing =  0.2 \n",
      "\n",
      "\n",
      "Calculation db[('LiFePO4.PHtest', '1u', 1)] successfully created\n",
      "\n",
      " \n",
      "\n",
      "JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)\n",
      "            197406  AMG-long lvp.our_  a.burov PD       0:00      1 (QOSMaxJobsPerUserLimit)\n",
      "            197455 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197456 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197457 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197458 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197459 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197460 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197461 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197462 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197463 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197464 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197465 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197466 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197467 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197468 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197469 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197470 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197425 AMG-mediu NaFe2C6N o.kovaly  R 2-03:08:49      1 node-amg01\n",
      "            197426 AMG-mediu NaFe2C6N o.kovaly  R 2-03:08:49      1 node-amg07\n",
      "            197433 AMG-mediu Fe2C6N6_ o.kovaly  R 1-01:34:44      1 node-amg07\n",
      "            197454 AMG-mediu lvp.001.  a.burov  R       1:17      1 node-amg03\n",
      "            197535       AMG LTNO.2.2   a.boev  R      19:37      1 node-amg04\n",
      "            197451 AMG-mediu lvp.001.  a.burov  R    5:10:56      1 node-amg01\n",
      "            197405  AMG-long lvp.our_  a.burov  R 3-04:31:56      1 node-amg05\n",
      "            197404  AMG-long lvp.na.o  a.burov  R 3-23:48:21      1 node-amg10\n",
      "            197403  AMG-long lvp.na.o  a.burov  R 3-23:48:42      1 node-amg14\n",
      "Submitted batch job 197536\n",
      "             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)\n",
      "            197536 AMG-mediu LiFePO4. d.akseno PD       0:00      1 (None)\n",
      "            197406  AMG-long lvp.our_  a.burov PD       0:00      1 (QOSMaxJobsPerUserLimit)\n",
      "            197455 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197456 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197457 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197458 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197459 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197460 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197461 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197462 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197463 AMG-mediu lvp.001.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197464 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197465 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197466 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197467 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197468 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197469 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197470 AMG-mediu lvp.010.  a.burov PD       0:00      1 (AssocGrpCpuLimit)\n",
      "            197425 AMG-mediu NaFe2C6N o.kovaly  R 2-03:08:49      1 node-amg01\n",
      "            197426 AMG-mediu NaFe2C6N o.kovaly  R 2-03:08:49      1 node-amg07\n",
      "            197433 AMG-mediu Fe2C6N6_ o.kovaly  R 1-01:34:44      1 node-amg07\n",
      "            197454 AMG-mediu lvp.001.  a.burov  R       1:17      1 node-amg03\n",
      "            197535       AMG LTNO.2.2   a.boev  R      19:37      1 node-amg04\n",
      "            197451 AMG-mediu lvp.001.  a.burov  R    5:10:56      1 node-amg01\n",
      "            197405  AMG-long lvp.our_  a.burov  R 3-04:31:56      1 node-amg05\n",
      "            197404  AMG-long lvp.na.o  a.burov  R 3-23:48:21      1 node-amg10\n",
      "            197403  AMG-long lvp.na.o  a.burov  R 3-23:48:42      1 node-amg14 \n",
      "\n",
      "-- To read results use  res_loop('LiFePO4.PHtest', ['1u'], [1], show = 'fo'  )     # , on 2023-02-08   ; possible options for show: fit, fo, fop, en, mag, magp, smag, maga, occ, occ1, mep, mepp \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'LiFePO4.PHtest'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from siman import header\n",
    "header.warnings = 'yY'\n",
    "st = smart_structure_read('in/POSCAR_LiFePO4_1u_1_end') #read LiFePO4 structure\n",
    "# here run=1 means that the calculation will be immidiately started on cluster\n",
    "add('LiFePO4.PHtest', '1u', 1, run = 0, up = 'up2', input_st = st, it_folder = 'LiFePO4/PH/', calc_method = 'polaron', \n",
    "    params = {'polaron':{'images':7, 'istart':24, 'iend':25, 'polaron_type':'hole', 'amp':0.12}}, cluster = 'cee', show ='fo')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.30   3.80   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [2353 3401 2902  564  683  736  212  418  166  173  139  168  176   93\n",
      "  117  144  140   86   59   53   56   29   67   24] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.1']     |LiFePO4.PHtest.1u.1| -196.1801  |10.45, 6.09, 4.75|-11443,-11884,-10286 |  24, 7,189    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.80   4.30   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [2373 3010 2613  874  787  569  216  290  215  265  182  137  100  183\n",
      "   76   81   52   49   48   50   57   42   40   39   36] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.2']     |LiFePO4.PHtest.1u.2| -196.1779  |10.45, 6.09, 4.75|-11557,-11940,-10339 |  25,14,354    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.30   3.80   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [25] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.21']    |LiFePO4.PHtest.1u.21| -196.1801  |10.45, 6.09, 4.75|-11442,-11883,-10288 |   1, 2,  2    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.29   3.80   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [220] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.22']    |LiFePO4.PHtest.1u.22| -196.1683  |10.45, 6.09, 4.75|-11521,-11976,-10369 |   1, 7,  7    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.28   3.81   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [417] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.23']    |LiFePO4.PHtest.1u.23| -196.1350  |10.45, 6.09, 4.75|-11586,-12064,-10430 |   1, 7,  7    \n",
      "-- Attention!, SCF was not converged to desirable prec -0.028 > 0.01 meV \n",
      "\n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.25   3.86   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [508] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.24']    |LiFePO4.PHtest.1u.24| -196.0835  |10.45, 6.09, 4.75|-11654,-12098,-10462 |   1,15, 15    \n",
      "-- Attention!, SCF was not converged to desirable prec 0.017 > 0.01 meV \n",
      "\n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.12   4.04   3.79   3.80 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [358] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.25']    |LiFePO4.PHtest.1u.25| -196.0362  |10.45, 6.09, 4.75|-11809,-12032,-10488 |   1,31, 31    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.95   4.20   3.79   3.79 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [345] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.26']    |LiFePO4.PHtest.1u.26| -196.0384  |10.45, 6.09, 4.75|-11893,-11938,-10458 |   1,34, 34    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.83   4.28   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [501] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.27']    |LiFePO4.PHtest.1u.27| -196.1025  |10.45, 6.09, 4.75|-11833,-11873,-10450 |   1,37, 37    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.80   4.30   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [36] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.42']    |LiFePO4.PHtest.1u.42| -196.1779  |10.45, 6.09, 4.75|-11532,-11908,-10309 |   1, 9,  9    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.80   4.29   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [218] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.43']    |LiFePO4.PHtest.1u.43| -196.1649  |10.45, 6.09, 4.75|-11610,-12025,-10390 |   1, 7,  7    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.82   4.28   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [412] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.44']    |LiFePO4.PHtest.1u.44| -196.1304  |10.45, 6.09, 4.75|-11631,-12091,-10457 |   1, 7,  7    \n",
      "-- Attention!, SCF was not converged to desirable prec -0.023 > 0.01 meV \n",
      "\n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  3.87   4.25   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [481] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.45']    |LiFePO4.PHtest.1u.45| -196.0780  |10.45, 6.09, 4.75|-11695,-12115,-10482 |   1,15, 15    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.04   4.12   3.79   3.79 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [348] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.46']    |LiFePO4.PHtest.1u.46| -196.0341  |10.45, 6.09, 4.75|-11820,-12039,-10490 |   1,21, 21    \n",
      "-- Attention!, SCF was not converged to desirable prec -0.06 > 0.01 meV \n",
      "\n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.18   3.98   3.78   3.79 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [300] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.47']    |LiFePO4.PHtest.1u.47| -196.0308  |10.45, 6.09, 4.75|-12009,-11926,-10439 |   1,50, 50    \n",
      "The following TM are found: [26]\n",
      "\n",
      " Znucl:   26\n",
      "  4.28   3.84   3.78   3.78 \n",
      "\n",
      "\n",
      "--                           |  energy(eV)|    Vector lenghts (A)   | Stresses (MPa)     | N MD, N SCF    \n",
      "\n",
      "\n",
      "Max. F. tot  (meV/A) =  [561] \n",
      "\n",
      "-- db['LiFePO4.PHtest.1u.48']    |LiFePO4.PHtest.1u.48| -196.0890  |10.45, 6.09, 4.75|-12024,-11657,-10283 |   1,44, 44    \n",
      "-- Image saved to  figs/polmep.LiFePO4.PHtest.1u.U4.0.pdf \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([\"db['LiFePO4.PHtest.1u.1']     |LiFePO4.PHtest.1u.1| -196.1801  |10.45, 6.09, 4.75|-11443,-11884,-10286 |  24, 7,189   \"],\n",
       " [])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To read results, run:\n",
    "\n",
    "res('LiFePO4.PHtest', '1u', 1, up = 'up2', analys_type = 'polaron', readfiles = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=('./figs/png/polmep.LiFePO4.PHtest.1u.U4.0.png'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Opening  only_calc.gdbm3 for writing \n",
      "\n",
      "\n",
      "End of work at 2023-02-08 12:24:06.016428\n",
      " \n",
      "\n",
      "\n",
      "Database has been successfully updated\n",
      "\n"
     ]
    }
   ],
   "source": [
    "write_database()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}