dftb_calcs.ipynb

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  {
   "cell_type": "markdown",
   "id": "f74f15d4",
   "metadata": {},
   "source": [
    "# Compute DFTB energies and forces for the ~24.6k structures in CSD-10k_combined_w_kpts_11_pc dataset (_initial_ training set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "623c646c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.io import read, write\n",
    "from ase.calculators.dftb import Dftb\n",
    "import pickle\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540b011d",
   "metadata": {},
   "source": [
    "## Compute for training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "29c6b71d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load database\n",
    "with open('delta_data/CSD-10k_combined_w_kpts_11_pc.pickle', 'rb') as f:\n",
    "    db = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "966cc4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get array of all crystal names\n",
    "names = []\n",
    "for key in db.keys():\n",
    "    names.append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff108dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set preliminary DFTB params\n",
    "#These should be the exact same as I have found in DFTB input scripts from Edgar/Aditi (I thoroughly checked)\n",
    "calc_preliminary = Dftb(Hamiltonian_='DFTB',\n",
    "           Hamiltonian_Charge=0,\n",
    "           Hamiltonian_DampXH='Yes',\n",
    "           Hamiltonian_DampXHExponent = 4.0,\n",
    "           Hamiltonian_Dispersion_ = 'DftD3',\n",
    "           Hamiltonian_Dispersion_Damping='BeckeJohnson{}',\n",
    "           Hamiltonian_Eigensolver='RelativelyRobust{}',\n",
    "           Hamiltonian_HubbardDerivs_='',\n",
    "           Hamiltonian_MaxAngularMomentum_='',\n",
    "           Hamiltonian_SCC='Yes',\n",
    "           Hamiltonian_SCCTolerance=1e-4,\n",
    "           Hamiltonian_MaxSCCIterations=1000,\n",
    "           Hamiltonian_SlaterKosterFiles_='Type2FileNames',\n",
    "           Hamiltonian_SlaterKosterFiles_Prefix='\"./dftb-param/\"',\n",
    "           Hamiltonian_SlaterKosterFiles_Separator='\"-\"',\n",
    "           Hamiltonian_SlaterKosterFiles_Suffix='\".skf\"',\n",
    "           Hamiltonian_SlaterKosterFiles_LowerCaseTypeName='No',\n",
    "           Hamiltonian_ThirdOrderFull='Yes',\n",
    "           Hamiltonian_Filling_='Fermi',\n",
    "           Hamiltonian_Filling_Temperature=300*0.316681534524639E-05, #convert from K to Hartree!\n",
    "           #Options_WriteResultsTag='No',\n",
    "           #Options_WriteDetailedOut='No',\n",
    "           #Options_WriteBandOut='No',\n",
    "           ParserOptions_='',\n",
    "           ParserOptions_ParserVersion=4,\n",
    "           ParserOptions_IgnoreUnprocessedNodes = 'No',\n",
    "           do_mulliken=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e56e6b32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data that will need to be set in the loop, depending on which species are present\n",
    "\n",
    "#Hamiltonian_HubbardDerivs_H=-0.1857, \n",
    "#Hamiltonian_HubbardDerivs_C=-0.1492, \n",
    "#Hamiltonian_HubbardDerivs_N=-0.1535, \n",
    "#Hamiltonian_HubbardDerivs_O=-0.1575,\n",
    "#Hamiltonian_MaxAngularMomentum_H='\"s\"', \n",
    "#Hamiltonian_MaxAngularMomentum_C='\"p\"', \n",
    "#Hamiltonian_MaxAngularMomentum_N='\"p\"', \n",
    "#Hamiltonian_MaxAngularMomentum_O='\"p\"',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a031c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename \"forces\" array to \"PBE-D2_forces\" to avoid confusion, and rename \"energy\" to \"PBE-D2_energy\"\n",
    "for name in names:\n",
    "    for frm in db[name]:\n",
    "        frm.arrays['PBE-D2_forces'] = frm.arrays['forces'].copy() #add new array with good name\n",
    "        frm.set_array('forces', None) #remove old array\n",
    "        frm.info['PBE-D2_energy'] = frm.info['energy'].copy() #add new info with energy\n",
    "        frm.info.pop('energy') #remove old energy info"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "29b9f6a1",
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    "scrolled": true
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      "IOPub message rate exceeded.\n",
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      "IOPub message rate exceeded.\n",
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      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Compute DFTB energies and forces, and add them to the frames\n",
    "for name in tqdm(names, desc=\"Crystal Progress\", leave=False):\n",
    "    for frm in tqdm(db[name], desc=\"Config Progress\", leave=False):\n",
    "        \n",
    "        kpts = np.array2string(frm.info['kpts'], separator=' ')[1:-1] #Get K-points\n",
    "        if len(kpts) != 5: #Print k points if they are longer than useful (for security) \n",
    "            print(name, frm) \n",
    "        kpts += ' 1.0' #add the k points \"weight\" for DFTB+\n",
    "        \n",
    "        calc=Dftb(**calc_preliminary.todict()) #Get preliminary calculator\n",
    "        calc.set(Hamiltonian_KPointsAndWeights=\"{\"+kpts+\"}\") #Set k points\n",
    "        \n",
    "        #Set Hubbard derivs and max angular momenta for the relevant species present\n",
    "        if \"H\" in frm.get_chemical_symbols():\n",
    "            calc.set(Hamiltonian_HubbardDerivs_H=-0.1857)\n",
    "            calc.set(Hamiltonian_MaxAngularMomentum_H='\"s\"')\n",
    "        if \"C\" in frm.get_chemical_symbols():\n",
    "            calc.set(Hamiltonian_HubbardDerivs_C=-0.1492)\n",
    "            calc.set(Hamiltonian_MaxAngularMomentum_C='\"p\"')\n",
    "        if \"N\" in frm.get_chemical_symbols():\n",
    "            calc.set(Hamiltonian_HubbardDerivs_N=-0.1535)\n",
    "            calc.set(Hamiltonian_MaxAngularMomentum_N='\"p\"')\n",
    "        if \"O\" in frm.get_chemical_symbols():\n",
    "            calc.set(Hamiltonian_HubbardDerivs_O=-0.1575)\n",
    "            calc.set(Hamiltonian_MaxAngularMomentum_O='\"p\"')\n",
    "        \n",
    "        frm.set_calculator(calc) #set calculator for the frame\n",
    "        \n",
    "        frm.info['DFTB_energy'] = frm.get_potential_energy() #calculate potential energy, and add it as info\n",
    "        frm.arrays['DFTB_forces'] = frm.get_forces() #calculate forces, and add them as info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6ce5e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save dataset\n",
    "#with open('delta_data/CSD-10k_combined_w_kpts_11_pc_w_DFTB.pickle', 'wb') as f:\n",
    "#    pickle.dump(db, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ccb81bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('delta_data/CSD-10k_combined_w_kpts_11_pc_w_DFTB.pickle', 'rb') as f:\n",
    "    reloaded = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11cc9110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24618\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for name in names:\n",
    "    for frm in db[name]:\n",
    "        i+=1\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d0e4882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24618\n"
     ]
    }
   ],
   "source": [
    "j=0\n",
    "for name in names:\n",
    "    for frm in reloaded[name]:\n",
    "        if 'DFTB_energy' in frm.info.keys():\n",
    "            j+=1\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fea8a7fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create parity plot of forces and energies\n",
    "n=10; forces_pbe=[]; forces_dftb=[]; energies_pbe=[]; energies_dftb=[]\n",
    "for name in names[:n]:\n",
    "    for frm in reloaded[name]:\n",
    "        forces_pbe.extend(frm.arrays['PBE-D2_forces'])\n",
    "        forces_dftb.extend(frm.arrays['DFTB_forces'])\n",
    "\n",
    "plt.scatter(forces_pbe, forces_dftb, label='Forces')\n",
    "plt.plot([-6,6],[-6,6], 'red')\n",
    "plt.title('Parity plot DFTB vs. PBE-D2 forces')\n",
    "plt.xlabel('PBE-D2 forces (eV/Å)')\n",
    "plt.ylabel('DFTB forces (eV/Å)')\n",
    "plt.legend()\n",
    "plt.plot(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab66a8d0",
   "metadata": {},
   "source": [
    "## Recompute for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "086559dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import test set\n",
    "csd_2k = read('raw_data/CSD-1k_combined.xyz', ':')\n",
    "#Only get CHNO\n",
    "csd_1k = []\n",
    "for frm in csd_2k:\n",
    "    frm.wrap(eps=1e-13)\n",
    "    good = True\n",
    "    for elem in np.unique(frm.get_chemical_symbols()):\n",
    "        if elem not in ['H', 'C', 'N', 'O']:\n",
    "            good = False\n",
    "    if good == True:\n",
    "        csd_1k.append(frm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "38134162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Name': 'LOGHEV',\n",
       " 'energy_unit': 'eV',\n",
       " 'force_unit': 'eV/angstrom',\n",
       " 'PBE-D2_energy': -25072.04180874}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csd_1k[2].info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b28079",
   "metadata": {},
   "source": [
    "CSD-1k set already has energies and forces named as PBE-D2_energies and PBE-D2_forces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a005e16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set preliminary DFTB params\n",
    "#These should be the exact same as I have found in DFTB input scripts from Edgar/Aditi (I thoroughly checked)\n",
    "calc_preliminary = Dftb(Hamiltonian_='DFTB',\n",
    "           Hamiltonian_Charge=0,\n",
    "           Hamiltonian_DampXH='Yes',\n",
    "           Hamiltonian_DampXHExponent = 4.0,\n",
    "           Hamiltonian_Dispersion_ = 'DftD3',\n",
    "           Hamiltonian_Dispersion_Damping='BeckeJohnson{}',\n",
    "           Hamiltonian_Eigensolver='RelativelyRobust{}',\n",
    "           Hamiltonian_HubbardDerivs_='',\n",
    "           Hamiltonian_MaxAngularMomentum_='',\n",
    "           Hamiltonian_SCC='Yes',\n",
    "           Hamiltonian_SCCTolerance=1e-4,\n",
    "           Hamiltonian_MaxSCCIterations=1000,\n",
    "           Hamiltonian_SlaterKosterFiles_='Type2FileNames',\n",
    "           Hamiltonian_SlaterKosterFiles_Prefix='\"./dftb-param/\"',\n",
    "           Hamiltonian_SlaterKosterFiles_Separator='\"-\"',\n",
    "           Hamiltonian_SlaterKosterFiles_Suffix='\".skf\"',\n",
    "           Hamiltonian_SlaterKosterFiles_LowerCaseTypeName='No',\n",
    "           Hamiltonian_ThirdOrderFull='Yes',\n",
    "           Hamiltonian_Filling_='Fermi',\n",
    "           Hamiltonian_Filling_Temperature=300*0.316681534524639E-05, #convert from K to Hartree!\n",
    "           #Options_WriteResultsTag='No',\n",
    "           #Options_WriteDetailedOut='No',\n",
    "           #Options_WriteBandOut='No',\n",
    "           ParserOptions_='',\n",
    "           ParserOptions_ParserVersion=4,\n",
    "           ParserOptions_IgnoreUnprocessedNodes = 'No',\n",
    "           do_mulliken=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84202245",
   "metadata": {},
   "outputs": [],
   "source": [
    "kpoint_density=0.06 #from EAE NQE paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2add9266",
   "metadata": {},
   "outputs": [],
   "source": [
    "for frm in csd_1k:\n",
    "    #get lengths and angles\n",
    "    cleang = frm.cell.cellpar()\n",
    "    #get k points\\n\",\n",
    "    kpoint = [int(math.ceil(\n",
    "              1/( kpoint_density*float(cleang[it]))\n",
    "              )) for it in range(0,3)]\n",
    "    #translate k points to string, with space between each k point, to input into control.in file\n",
    "    k_points_for_file= \" \".join(str(k) for k in kpoint)\n",
    "\n",
    "    #add k-points, formula, and volume info to the frame\n",
    "    frm.info['kpts'] = np.asarray(kpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c9875b21",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Config Progress:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compute DFTB energies and forces, and add them to the frames\n",
    "for frm in tqdm(csd_1k, desc=\"Config Progress\", leave=False):\n",
    "\n",
    "    kpts = np.array2string(frm.info['kpts'], separator=' ')[1:-1] #Get K-points\n",
    "    if len(kpts) != 5: #Print k points if they are longer than useful (for security) \n",
    "        print(name, frm) \n",
    "    kpts += ' 1.0' #add the k points \"weight\" for DFTB+\n",
    "\n",
    "    calc=Dftb(**calc_preliminary.todict()) #Get preliminary calculator\n",
    "    calc.set(Hamiltonian_KPointsAndWeights=\"{\"+kpts+\"}\") #Set k points\n",
    "\n",
    "    #Set Hubbard derivs and max angular momenta for the relevant species present\n",
    "    if \"H\" in frm.get_chemical_symbols():\n",
    "        calc.set(Hamiltonian_HubbardDerivs_H=-0.1857)\n",
    "        calc.set(Hamiltonian_MaxAngularMomentum_H='\"s\"')\n",
    "    if \"C\" in frm.get_chemical_symbols():\n",
    "        calc.set(Hamiltonian_HubbardDerivs_C=-0.1492)\n",
    "        calc.set(Hamiltonian_MaxAngularMomentum_C='\"p\"')\n",
    "    if \"N\" in frm.get_chemical_symbols():\n",
    "        calc.set(Hamiltonian_HubbardDerivs_N=-0.1535)\n",
    "        calc.set(Hamiltonian_MaxAngularMomentum_N='\"p\"')\n",
    "    if \"O\" in frm.get_chemical_symbols():\n",
    "        calc.set(Hamiltonian_HubbardDerivs_O=-0.1575)\n",
    "        calc.set(Hamiltonian_MaxAngularMomentum_O='\"p\"')\n",
    "\n",
    "    frm.set_calculator(calc) #set calculator for the frame\n",
    "\n",
    "    frm.info['DFTB_energy'] = frm.get_potential_energy() #calculate potential energy, and add it as info\n",
    "    frm.arrays['DFTB_forces'] = frm.get_forces() #calculate forces, and add them as info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a00dca41",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save dataset\n",
    "#with open('raw_data/CSD-1k_hcno_w_DFTB.pickle', 'wb') as f:\n",
    "#    pickle.dump(csd_1k, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a2163ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reload dataset\n",
    "with open('raw_data/CSD-1k_hcno_w_DFTB.pickle', 'rb') as f:\n",
    "    new_csd_1k = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "545e2e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([i.info['DFTB_energy'] for i in new_csd_1k])"
   ]
  }
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