MVOTO_SHARBER-V230524.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# <a id='toc1_'></a>[PCE:Couplage de Rdkit Crest-xTB avec AlGORITHME DE SCHARBER ](#toc0_)\n",
    "\n",
    "1. **MVOTO KONGO Patrick Sorrel**, sorrel.mvoto@facsciences-uy1.cm\n",
    "    * Department of Physics, Faculty of Science, University of Yaounde I \n",
    "Etudiant de Master au Laboratoire de \n",
    "    * Physique Atomique Moleculaire et Biophysique\n",
    "\n",
    "23 MAI 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='toc1_3_'></a>[DESCRIPTION](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Nos travaux de mémoire sont basés sur le flux de travail de Tartarus. L'algorithme est le suivant :\n",
    "<img src=\"./Graphics/Tartarus.png\" width=\"1000\"></center>\n",
    "\n",
    " Une illustration du cadre Tartarus, mettant en évidence les tâches de conception réelles qui sont définies et associées à des flux de travail et des ensembles de données de simulation dans Tartarus\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*  Notre objectif de recherche consiste à concevoir des petites molécules dotées de propriétés électroniques spécifiques, notamment des molécules capables d'effectuer la séparation des charges, inspirées de la conception photovoltaïque organique (OPV). Nous avons deux tâches individuelles :\n",
    "\n",
    "- Recherche d'une molécule donneuse organique à utiliser avec l'ester méthylique de l'acide l'ester méthylique de\n",
    "    l'acide ${[6,6]­phényl­C61­butyrique(PCBM)}$.\n",
    "- Recherche d'une molécule accepteuse à utiliser dans des dispositifs basés sur le poly[N­90­heptadécanyl­2,7­carbazole­alt­5,5­(40,70­di­2­thiényl­20,10,30­benzothiaMachine  (PCDTBT)).\n",
    "\n",
    "*  Notre travail actuel porte sur le modèle ci-dessous :\n",
    "<img src=\"./Graphics/opv.png\" width=\"2000\"></center>\n",
    "\n",
    " schématique du flux de travail de simulation de propriétés pour la conception de références photovoltaïques organiques\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_'></a>[Utilisation de Pandas  pour extraire les information dans le fichiers hce.csv et creer un DataFrame](#toc0_)\n",
    "<!-- ![MolecularDimension.png](attachment:MolecularDimension.png) -->\n",
    "![MolecularDimension.png](./Graphics/pandas.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024.03.4\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "#from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import rdDepictor\n",
    "from rdkit.Chem import AllChem\n",
    "from pathlib import Path\n",
    "#IPythonConsole.ipython_3d = True\n",
    "import py3Dmol\n",
    "\n",
    "import rdkit\n",
    "print(rdkit.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "P30T1= 'CCCCCCCCC1=CSC=C1'\n",
    "P3HT1= 'CCCCCCC1=C(-*)SC(-*)=C1'\n",
    "PCBM='COC(=O)CCCC1(C23C14C5=C6C7=C8C5=C9C1=C5C%10=C%11C%12=C%13C%10=C%10C1=C8C1=C%10C8=C%10C%14=C%15C%16=C%17C(=C%12C%12=C%17C%17=C%18C%16=C%16C%15=C%15C%10=C1C7=C%15C1=C%16C(=C%18C7=C2C2=C%10C(=C5C9=C42)C%11=C%12C%10=C%177)C3=C16)C%14=C%138)C1=CC=CC=C1'\n",
    "T_Q1= 'c1ccc2c(c1)c3c(c2)ccc3'\n",
    "DCP1= 'c1cc(c(c(c1)C#N)C#N)C=C'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='toc1_3_'></a>[Represntations des smiles avec leurs Masses Molaires](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smiles1 = [P30T1,\n",
    "          P3HT1, \n",
    "          PCBM,\n",
    "          T_Q1, \n",
    "          DCP1 ]\n",
    "\n",
    "values = [196.359,166.289,286,152.196,154.172]\n",
    "\n",
    "mols_ls = [Chem.MolFromSmiles(x) for x in smiles1]\n",
    "img = Draw.MolsToGridImage(mols_ls,molsPerRow=4,subImgSize=(200,200),legends=[str(x) for x in values])    \n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='toc1_3_'></a>[Representations des smiles en 3D avec py3Dmol.](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecule image saved as colored_molecule.png\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import rdMolDraw2D\n",
    "from rdkit.Chem import rdMolDescriptors\n",
    "\n",
    "# Fonction pour colorer les atomes donneurs et accepteurs\n",
    "def color_atoms(mol):\n",
    "    # Liste des donneurs et accepteurs communs\n",
    "    donneurs = ['N', 'O']  # Atomes donneurs typiques\n",
    "    accepteurs = ['N', 'O', 'F', 'Cl', 'Br', 'I']  # Atomes accepteurs typiques\n",
    "    \n",
    "    # Dictionnaire pour stocker les couleurs des atomes\n",
    "    atom_colors = {}\n",
    "    \n",
    "    for atom in mol.GetAtoms():\n",
    "        symbol = atom.GetSymbol()\n",
    "        idx = atom.GetIdx()\n",
    "        if symbol in donneurs:\n",
    "            atom_colors[idx] = (0, 0, 1)  # Bleu pour les donneurs\n",
    "        if symbol in accepteurs:\n",
    "            atom_colors[idx] = (1, 0, 0)  # Rouge pour les accepteurs\n",
    "    \n",
    "    return atom_colors\n",
    "\n",
    "# Exemple de SMILES\n",
    "smiles = \"COC(=O)CCCC1(C23C14C5=C6C7=C8C5=C9C1=C5C%10=C%11C%12=C%13C%10=C%10C1=C8C1=C%10C8=C%10C%14=C%15C%16=C%17C(=C%12C%12=C%17C%17=C%18C%16=C%16C%15=C%15C%10=C1C7=C%15C1=C%16C(=C%18C7=C2C2=C%10C(=C5C9=C42)C%11=C%12C%10=C%177)C3=C16)C%14=C%138)C1=CC=CC=C1\"  # Acide aminé alanine par exemple\n",
    "mol = Chem.MolFromSmiles(smiles)\n",
    "\n",
    "# Colorier les atomes\n",
    "atom_colors = color_atoms(mol)\n",
    "\n",
    "# Dessiner la molécule avec les atomes colorés\n",
    "drawer = rdMolDraw2D.MolDraw2DCairo(300, 300)\n",
    "options = drawer.drawOptions()\n",
    "\n",
    "# Appliquer les couleurs\n",
    "# for idx, color in atom_colors.items():\n",
    "#     options.setAtomPalette[idx] = color\n",
    "\n",
    "# Dessiner la molécule\n",
    "drawer.DrawMolecule(mol)\n",
    "drawer.FinishDrawing()\n",
    "drawer.WriteDrawingText(\"patrick.png\")\n",
    "\n",
    "print(\"Molecule image saved as colored_molecule.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def view3D(mol, output_file, fmt='mol'):\n",
    "        \"\"\"3D vizualisation of mol/xyz file with Py3DMol\n",
    "        \"\"\"\n",
    "\n",
    "        view = py3Dmol.view(width=400, height=400)\n",
    "\n",
    "        if fmt=='mol':\n",
    "            view.addModel(Chem.MolToMolBlock(mol), fmt)\n",
    "            # view.addModel(mol, fmt)\n",
    "        elif fmt=='xyz':\n",
    "            view.addModel(mol, fmt)\n",
    "\n",
    "        view.setStyle({'stick': {}, 'sphere': {'scale': .30}})\n",
    "        view.zoomTo()\n",
    "        view.show()\n",
    "        # view.png(output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[14:59:31] UFFTYPER: Unrecognized atom type: *_ (8)\n",
      "[14:59:31] UFFTYPER: Unrecognized atom type: *_ (11)\n"
     ]
    },
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       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
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       "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.1.0/3Dmol-min.js');\n",
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       "viewer_17166455715772598 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17166455715772598\"),{backgroundColor:\"white\"});\n",
       "viewer_17166455715772598.zoomTo();\n",
       "\tviewer_17166455715772598.addModel(\"\\n     RDKit          3D\\n\\n 18 18  0  0  0  0  0  0  0  0999 V2000\\n    0.5542   -1.9346   -1.1823 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.4277   -1.6622   -0.2667 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.5720   -0.3870    0.3294 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.3375    0.5389   -0.0785 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3569    0.3093   -1.0091 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.4457   -0.9515   -1.5537 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.2820    1.3570   -1.3848 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.0211    2.2014   -1.6846 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.3112    1.8925    0.4432 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2946    2.9742    0.8391 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.6373   -0.1868    1.2819 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.9738    0.8494    1.9728 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.5989   -2.9437   -1.5968 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.1262   -2.4409    0.0212 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.2297   -1.1342   -2.2707 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.3014   -1.0766    1.4662 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.8580    0.7845    2.6701 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5354    1.8103    2.0032 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  4  5  1  0\\n  5  6  2  0\\n  5  7  1  0\\n  7  8  3  0\\n  4  9  1  0\\n  9 10  3  0\\n  3 11  1  0\\n 11 12  2  0\\n  6  1  1  0\\n  1 13  1  0\\n  2 14  1  0\\n  6 15  1  0\\n 11 16  1  0\\n 12 17  1  0\\n 12 18  1  0\\nM  END\\n\",\"mol\");\n",
       "\tviewer_17166455715772598.setStyle({\"stick\": {}, \"sphere\": {\"scale\": 0.3}});\n",
       "\tviewer_17166455715772598.zoomTo();\n",
       "viewer_17166455715772598.render();\n",
       "});\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Création du dataframe\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from rdkit.Chem import Descriptors\n",
    "smile_dict = {\n",
    "    'P30T':'CCCCCCCCC1=CSC=C1',\n",
    "    'P3HT1':'CCCCCCC1=C(-*)SC(-*)=C1',\n",
    "    'T_Q1': 'c1ccc2c(c1)c3c(c2)ccc3',\n",
    "    'DCP1': 'c1cc(c(c(c1)C#N)C#N)C=C'\n",
    "}\n",
    "properties = ['smiles_key','mol_rdkit' ,'smiles', 'Poids_Moléculaire', 'LogP', 'TPSA', 'QED']\n",
    "data = []\n",
    "\n",
    "for smi_key, smi in smile_dict.items():\n",
    "    mol_rdkit1 = Chem.MolFromSmiles(smi)\n",
    "    if mol_rdkit1 is not None:\n",
    "        # Ajoute les hydrogènes explicites\n",
    "        mol_rdkit1 = Chem.AddHs(mol_rdkit1)\n",
    "\n",
    "        # Génère la conformation 3D initiale de la molécule\n",
    "        AllChem.EmbedMolecule(mol_rdkit1)\n",
    "\n",
    "        # Calcul des descripteurs\n",
    "        molwt = Descriptors.MolWt(mol_rdkit1)\n",
    "        logp = Descriptors.MolLogP(mol_rdkit1)\n",
    "        tpsa = Descriptors.TPSA(mol_rdkit1)\n",
    "        qed = Descriptors.qed(mol_rdkit1)\n",
    "\n",
    "        # Ajout des propriétés à la liste de données\n",
    "        data.append([smi_key, mol_rdkit1,smi, molwt, logp, tpsa, qed])\n",
    "\n",
    "        # Création du sous-répertoire \"my_data_{smi_key}\"\n",
    "        working_dir = Path(f'./my_data/{smi_key}')\n",
    "        working_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "        # Définition des chemins de fichiers\n",
    "        path_xtb_xyz = working_dir / f'{smi_key}_opt.xyz'\n",
    "        path_xtb_log = working_dir / f'{smi_key}_opt.log'\n",
    "        path_xtb_csv = working_dir / f'{smi_key}_xtb.csv'\n",
    "        path_xtb_png = working_dir / f'{smi_key}_opt.png'\n",
    "\n",
    "        # Génération de la représentation 3D de la molécule\n",
    "        path_3d_smi1 = working_dir / f'{smi_key}_3d_smi.png'\n",
    "        view3D(mol_rdkit1, path_3d_smi1)\n",
    "\n",
    "        # Écriture des résultats dans un fichier\n",
    "        with open(working_dir / 'results.txt', 'w') as f:\n",
    "            f.write(f'smiles_key: {smi_key}\\n')\n",
    "            f.write(f'SMILE: {smi}\\n')\n",
    "            f.write(f'Poids moléculaire: {molwt}\\n')\n",
    "            f.write(f'LogP: {logp}\\n')\n",
    "            f.write(f'TPSA: {tpsa}\\n')\n",
    "            f.write(f'QED: {qed}\\n')\n",
    "\n",
    "df = pd.DataFrame(data, columns=properties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='toc1_1_'></a>[Representation Moleculaire et Descripteurs](#toc0_)\n",
    "1.**Descripteurs 0D**. Concernant les discripteurs « Composition », ne fournissent aucune information sur la structure moléculaire ou la connectivité des atomes.\n",
    "\n",
    "2.**Descripteurs 1D**. Lié à des ensembles de sous-structures telles que des groupes fonctionnels.\n",
    "\n",
    "3.**Descripteurs 2D**. Fonctionnalités fournissant des informations sur la topologie moléculaire basées sur la représentation graphique des molécules.\n",
    "\n",
    "4.**Descripteurs 3D**. Caractéristiques géométriques fournissant des informations sur les coordonnées spatiales des atomes d'une molécule.\n",
    "\n",
    "5.**Descripteurs 4D**. Également appelés **descripteurs basés sur une grille**, introduisent une quatrième dimension caractérisant les interactions entre les molécules et les sites actifs d'un récepteur ou les multiples états conformationnels des molécules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ![MolecularDimension.png](attachment:MolecularDimension.png) -->\n",
    "![MolecularDimension.png](./Graphics/MolecularDimension.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles_key</th>\n",
       "      <th>mol_rdkit</th>\n",
       "      <th>smiles</th>\n",
       "      <th>Poids_Moléculaire</th>\n",
       "      <th>LogP</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>QED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P30T</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0&gt;</td>\n",
       "      <td>CCCCCCCCC1=CSC=C1</td>\n",
       "      <td>196.359</td>\n",
       "      <td>4.65110</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.556752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P3HT1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040&gt;</td>\n",
       "      <td>CCCCCCC1=C(-*)SC(-*)=C1</td>\n",
       "      <td>166.289</td>\n",
       "      <td>2.22010</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.588361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_Q1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0&gt;</td>\n",
       "      <td>c1ccc2c(c1)c3c(c2)ccc3</td>\n",
       "      <td>152.196</td>\n",
       "      <td>3.03680</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.535668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DCP1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0&gt;</td>\n",
       "      <td>c1cc(c(c(c1)C#N)C#N)C=C</td>\n",
       "      <td>154.172</td>\n",
       "      <td>2.07296</td>\n",
       "      <td>47.58</td>\n",
       "      <td>0.621214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smiles_key                                         mol_rdkit  \\\n",
       "0       P30T  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0>   \n",
       "1      P3HT1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040>   \n",
       "2       T_Q1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0>   \n",
       "3       DCP1  <rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0>   \n",
       "\n",
       "                    smiles  Poids_Moléculaire     LogP   TPSA       QED  \n",
       "0        CCCCCCCCC1=CSC=C1            196.359  4.65110   0.00  0.556752  \n",
       "1  CCCCCCC1=C(-*)SC(-*)=C1            166.289  2.22010   0.00  0.588361  \n",
       "2   c1ccc2c(c1)c3c(c2)ccc3            152.196  3.03680   0.00  0.535668  \n",
       "3  c1cc(c(c(c1)C#N)C#N)C=C            154.172  2.07296  47.58  0.621214  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles_key</th>\n",
       "      <th>mol_rdkit</th>\n",
       "      <th>smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P30T</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0&gt;</td>\n",
       "      <td>CCCCCCCCC1=CSC=C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P3HT1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040&gt;</td>\n",
       "      <td>CCCCCCC1=C(-*)SC(-*)=C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_Q1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0&gt;</td>\n",
       "      <td>c1ccc2c(c1)c3c(c2)ccc3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DCP1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0&gt;</td>\n",
       "      <td>c1cc(c(c(c1)C#N)C#N)C=C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smiles_key                                         mol_rdkit  \\\n",
       "0       P30T  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0>   \n",
       "1      P3HT1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040>   \n",
       "2       T_Q1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0>   \n",
       "3       DCP1  <rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0>   \n",
       "\n",
       "                    smiles  \n",
       "0        CCCCCCCCC1=CSC=C1  \n",
       "1  CCCCCCC1=C(-*)SC(-*)=C1  \n",
       "2   c1ccc2c(c1)c3c(c2)ccc3  \n",
       "3  c1cc(c(c(c1)C#N)C#N)C=C  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xtb, crest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      -----------------------------------------------------------      \n",
      "     |                   =====================                   |     \n",
      "     |                           x T B                           |     \n",
      "     |                   =====================                   |     \n",
      "     |                         S. Grimme                         |     \n",
      "     |          Mulliken Center for Theoretical Chemistry        |     \n",
      "     |                    University of Bonn                     |     \n",
      "      -----------------------------------------------------------      \n",
      "\n",
      "   * xtb version 6.7.1 (edcfbbe) compiled by 'albert@albert-system' on 2024-07-22\n",
      "\n",
      "normal termination of xtb\n"
     ]
    }
   ],
   "source": [
    "!xtb --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "       ╔════════════════════════════════════════════╗\n",
      "       ║            ___ ___ ___ ___ _____           ║\n",
      "       ║           / __| _ \\ __/ __|_   _|          ║\n",
      "       ║          | (__|   / _|\\__ \\ | |            ║\n",
      "       ║           \\___|_|_\\___|___/ |_|            ║\n",
      "       ║                                            ║\n",
      "       ║  Conformer-Rotamer Ensemble Sampling Tool  ║\n",
      "       ║          based on the xTB methods          ║\n",
      "       ║                                            ║\n",
      "       ╚════════════════════════════════════════════╝\n",
      "       Version 3.0.1, Mon May  6 18:43:33 UTC 2024\n",
      "       commit (1782d7d) compiled by 'runner@fv-az772-53'\n",
      " \n",
      " crest 3.0.1\n"
     ]
    }
   ],
   "source": [
    "!crest --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='toc1_3_'></a>[Utilisation de crest et xTB pour la recherches des conformers](#toc0_)\n",
    " <center> <img src = \"./Graphics/CREST.jpeg\" width = \"600\">\n",
    " <img src = \"./Graphics/xtb.svg\" width = \"600\"> </center> \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <a id='toc1_3_'></a>[Workflow de Crest et xTB dans la recherches des conformers ](#toc0_)\n",
    " <center> <img src = \"./Graphics/XTB-CREST_WORKFLOW.jpeg\" width = \"600\">\n",
    " <img src = \"./Graphics/FLOW-CREST.png\" width = \"600\"> </center> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "import subprocess as sp\n",
    "import re\n",
    "\n",
    "def xtb_calculations(df):\n",
    "    yesso= [\"HOMO-LUMO GAP (eV)\",\"TOTAL ENERGY (a.u.)\", \"HOMO Index\", \"LUMO Index\", \"HOMO Energy (eV)\", \"LUMO Energy (eV)\"]\n",
    "    dtb = []\n",
    "    for i in range(len(df)):\n",
    "        mol_rdkit = Chem.MolFromSmiles(df.loc[i, 'smiles'])\n",
    "        smi_key=df.loc[i, 'smiles_key']\n",
    "        smi=df.loc[i, 'smiles']\n",
    "        # Extract the SMILES string for the current row\n",
    "        smile1 = df.loc[i, \"mol_rdkit\"]\n",
    "\n",
    "       # Setting various paths for xtb files\n",
    "        path_xtb_xyz = working_dir / f'{df.loc[i, \"smiles_key\"]}_opt.xyz'\n",
    "        path_xtb_log = working_dir / f'{df.loc[i, \"smiles_key\"]}_opt.log'\n",
    "        path_xtb_csv = working_dir / f'{df.loc[i, \"smiles_key\"]}_xtb.csv'\n",
    "        path_xtb_png = working_dir / f'{df.loc[i, \"smiles_key\"]}_opt.png'\n",
    "\n",
    "        # Check if path exists, if not create it\n",
    "        if not path_xtb_xyz.exists():\n",
    "            print(f'path_xtb_xyz does not exist and will be created for {smile1}')\n",
    "\n",
    "        # Convert RDKit molecule to XYZ format\n",
    "        mol_rdkit_xyz = Chem.MolToXYZBlock(smile1)\n",
    "        with open(\"mol_rdkit.xyz\", \"w\") as f:\n",
    "            f.write(mol_rdkit_xyz)\n",
    "\n",
    "        # Geometry pre-optimization using GFn-XTB with different parameters\n",
    "        sp.run([\"xtb\", \"mol_rdkit.xyz\", \"--gfn2\", \"--opt\", \"vtight\", \"--parallel 1\"],\n",
    "               stdout=sp.PIPE, stderr=sp.PIPE, text=True, universal_newlines=True)\n",
    "\n",
    "        # Best conformation search\n",
    "        sp.run(['crest', 'xtbopt.xyz', '-gff', '-mquick', '--noreftopo'], stdout=sp.DEVNULL, stderr=sp.DEVNULL)\n",
    "\n",
    "        # xtb geometry optimization of the crest best conformation\n",
    "        xtb_process = sp.run([\"xtb\", 'crest_best.xyz', \"--gfn2\", \"--opt\", \"vtight\", \"--parallel 1\"],\n",
    "                              stdout=sp.PIPE, stderr=sp.PIPE, text=True, universal_newlines=True)\n",
    "\n",
    "        # Store the xtbopt.xyz file in path_xtb_xyz\n",
    "        sp.run(['cp', 'xtbopt.xyz', path_xtb_xyz], stdout=sp.PIPE, stderr=sp.DEVNULL)\n",
    "\n",
    "        with open(path_xtb_log, \"w\") as fl:\n",
    "            fl.write(xtb_process.stdout)\n",
    "\n",
    "        # Extract the total energy and Homo-lumo gap from the xtb output file\n",
    "        # Find TOTAL ENERGY value\n",
    "        tot_energy_line = [li for li in path_xtb_log.read_text().splitlines() if 'TOTAL ENERGY' in li]\n",
    "        tot_energy_line = tot_energy_line[0].split()\n",
    "        tot_energy_xtb = float(tot_energy_line[3])\n",
    "\n",
    "        # Find HOMO-LUMO GAP value\n",
    "        HL_gap_line = [li for li in path_xtb_log.read_text().splitlines() if 'HOMO-LUMO GAP' in li]\n",
    "        HL_gap_line = HL_gap_line[0].split()\n",
    "        HL_gap_xtb = float(HL_gap_line[3])\n",
    "\n",
    "        # Find HOMO index\n",
    "        Homo_idx_line = [li for li in path_xtb_log.read_text().splitlines() if '(HOMO)' in li]\n",
    "        Homo_idx_line = Homo_idx_line[0].split()\n",
    "        Homo_idx_xtb = int(Homo_idx_line[0])\n",
    "        EHomo_xtb = float(Homo_idx_line[3])\n",
    "\n",
    "        # Find LUMO index\n",
    "        Lumo_idx_line = [li for li in path_xtb_log.read_text().splitlines() if '(LUMO)' in li]\n",
    "        Lumo_idx_line = Lumo_idx_line[0].split()\n",
    "        Lumo_idx_xtb = int(Lumo_idx_line[0])\n",
    "        ELumo_xtb = float(Lumo_idx_line[2])\n",
    "\n",
    "        # Extract the total energy, Homo-lumo gap, Homo energy, and Lumo energy from the xtb output file\n",
    "        # Find HOMO-LUMO GAP value\n",
    "        match_gap = re.search(r\"HOMO-LUMO GAP\\s+(\\d+\\.\\d+)\\s+eV\", path_xtb_log.read_text())\n",
    "        if match_gap:\n",
    "            HL_gap_xtb = float(match_gap.group(1))\n",
    "\n",
    "        # Find TOTAL ENERGY value\n",
    "        match_energy = re.search(r\"TOTAL ENERGY\\s+(-?\\d+\\.\\d+)\", path_xtb_log.read_text())\n",
    "        if match_energy:\n",
    "            tot_energy_xtb = float(match_energy.group(1))\n",
    "\n",
    "        # Find HOMO energy\n",
    "        match_homo = re.search(r\"(\\d+)\\.\\s+\\d+\\.\\d+\\s+(-\\d+\\.\\d+)\\s+\\(HOMO\\)\", path_xtb_log.read_text())\n",
    "        if match_homo:\n",
    "            Homo_idx_xtb = int(match_homo.group(1))\n",
    "            EHomo_xtb = float(match_homo.group(2))\n",
    "\n",
    "        # Find LUMO energy \n",
    "        match_lumo = re.search(r\"(\\d+)\\.\\s+\\S+\\s+(-\\d+\\.\\d+)\\s+\\(LUMO\\)\", path_xtb_log.read_text())\n",
    "        if match_lumo:\n",
    "            Lumo_idx_xtb = int(match_lumo.group(1))\n",
    "            ELumo_xtb = float(match_lumo.group(2))\n",
    "    \n",
    "        # Create the xtb results dataframe\n",
    "        dtb.append([ HL_gap_xtb, tot_energy_xtb, Homo_idx_xtb, Lumo_idx_xtb, EHomo_xtb, ELumo_xtb])\n",
    "    df_xtb = pd.DataFrame(dtb, columns=yesso)\n",
    "\n",
    "    return df_xtb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_xtb=xtb_calculations(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOMO-LUMO GAP (eV)</th>\n",
       "      <th>TOTAL ENERGY (a.u.)</th>\n",
       "      <th>HOMO Index</th>\n",
       "      <th>LUMO Index</th>\n",
       "      <th>HOMO Energy (eV)</th>\n",
       "      <th>LUMO Energy (eV)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.906407</td>\n",
       "      <td>-39.140278</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>-10.3390</td>\n",
       "      <td>-6.4490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.906110</td>\n",
       "      <td>-39.140277</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>-10.3467</td>\n",
       "      <td>-6.4471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.449581</td>\n",
       "      <td>-29.704334</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-9.7614</td>\n",
       "      <td>-8.4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.166748</td>\n",
       "      <td>-30.195367</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-11.4556</td>\n",
       "      <td>-8.3660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HOMO-LUMO GAP (eV)  TOTAL ENERGY (a.u.)  HOMO Index  LUMO Index  \\\n",
       "0            3.906407           -39.140278          37          38   \n",
       "1            3.906110           -39.140277          37          38   \n",
       "2            1.449581           -29.704334          28          29   \n",
       "3            3.166748           -30.195367          28          29   \n",
       "\n",
       "   HOMO Energy (eV)  LUMO Energy (eV)  \n",
       "0          -10.3390           -6.4490  \n",
       "1          -10.3467           -6.4471  \n",
       "2           -9.7614           -8.4812  \n",
       "3          -11.4556           -8.3660  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_xtb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the xtb results dataframe to a file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_df=pd.concat([df, df_xtb], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles_key</th>\n",
       "      <th>mol_rdkit</th>\n",
       "      <th>smiles</th>\n",
       "      <th>Poids_Moléculaire</th>\n",
       "      <th>LogP</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>QED</th>\n",
       "      <th>HOMO-LUMO GAP (eV)</th>\n",
       "      <th>TOTAL ENERGY (a.u.)</th>\n",
       "      <th>HOMO Index</th>\n",
       "      <th>LUMO Index</th>\n",
       "      <th>HOMO Energy (eV)</th>\n",
       "      <th>LUMO Energy (eV)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P30T</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0&gt;</td>\n",
       "      <td>CCCCCCCCC1=CSC=C1</td>\n",
       "      <td>196.359</td>\n",
       "      <td>4.65110</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.556752</td>\n",
       "      <td>3.906407</td>\n",
       "      <td>-39.140278</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>-10.3390</td>\n",
       "      <td>-6.4490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P3HT1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040&gt;</td>\n",
       "      <td>CCCCCCC1=C(-*)SC(-*)=C1</td>\n",
       "      <td>166.289</td>\n",
       "      <td>2.22010</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.588361</td>\n",
       "      <td>3.906110</td>\n",
       "      <td>-39.140277</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>-10.3467</td>\n",
       "      <td>-6.4471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_Q1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0&gt;</td>\n",
       "      <td>c1ccc2c(c1)c3c(c2)ccc3</td>\n",
       "      <td>152.196</td>\n",
       "      <td>3.03680</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.535668</td>\n",
       "      <td>1.449581</td>\n",
       "      <td>-29.704334</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-9.7614</td>\n",
       "      <td>-8.4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DCP1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0&gt;</td>\n",
       "      <td>c1cc(c(c(c1)C#N)C#N)C=C</td>\n",
       "      <td>154.172</td>\n",
       "      <td>2.07296</td>\n",
       "      <td>47.58</td>\n",
       "      <td>0.621214</td>\n",
       "      <td>3.166748</td>\n",
       "      <td>-30.195367</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-11.4556</td>\n",
       "      <td>-8.3660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smiles_key                                         mol_rdkit  \\\n",
       "0       P30T  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0>   \n",
       "1      P3HT1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040>   \n",
       "2       T_Q1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0>   \n",
       "3       DCP1  <rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0>   \n",
       "\n",
       "                    smiles  Poids_Moléculaire     LogP   TPSA       QED  \\\n",
       "0        CCCCCCCCC1=CSC=C1            196.359  4.65110   0.00  0.556752   \n",
       "1  CCCCCCC1=C(-*)SC(-*)=C1            166.289  2.22010   0.00  0.588361   \n",
       "2   c1ccc2c(c1)c3c(c2)ccc3            152.196  3.03680   0.00  0.535668   \n",
       "3  c1cc(c(c(c1)C#N)C#N)C=C            154.172  2.07296  47.58  0.621214   \n",
       "\n",
       "   HOMO-LUMO GAP (eV)  TOTAL ENERGY (a.u.)  HOMO Index  LUMO Index  \\\n",
       "0            3.906407           -39.140278          37          38   \n",
       "1            3.906110           -39.140277          37          38   \n",
       "2            1.449581           -29.704334          28          29   \n",
       "3            3.166748           -30.195367          28          29   \n",
       "\n",
       "   HOMO Energy (eV)  LUMO Energy (eV)  \n",
       "0          -10.3390           -6.4490  \n",
       "1          -10.3467           -6.4471  \n",
       "2           -9.7614           -8.4812  \n",
       "3          -11.4556           -8.3660  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles_key</th>\n",
       "      <th>mol_rdkit</th>\n",
       "      <th>smiles</th>\n",
       "      <th>Poids_Moléculaire</th>\n",
       "      <th>LogP</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>QED</th>\n",
       "      <th>HOMO-LUMO GAP (eV)</th>\n",
       "      <th>TOTAL ENERGY (a.u.)</th>\n",
       "      <th>HOMO Index</th>\n",
       "      <th>LUMO Index</th>\n",
       "      <th>HOMO Energy (eV)</th>\n",
       "      <th>LUMO Energy (eV)</th>\n",
       "      <th>Gap_calibrated</th>\n",
       "      <th>homo_calibrated</th>\n",
       "      <th>lumo_calibrated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P30T</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0&gt;</td>\n",
       "      <td>CCCCCCCCC1=CSC=C1</td>\n",
       "      <td>196.359</td>\n",
       "      <td>4.65110</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.556752</td>\n",
       "      <td>3.906407</td>\n",
       "      <td>-39.140278</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>-10.3390</td>\n",
       "      <td>-6.4490</td>\n",
       "      <td>3.910266</td>\n",
       "      <td>-5.786283</td>\n",
       "      <td>1.876017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P3HT1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040&gt;</td>\n",
       "      <td>CCCCCCC1=C(-*)SC(-*)=C1</td>\n",
       "      <td>166.289</td>\n",
       "      <td>2.22010</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.588361</td>\n",
       "      <td>3.906110</td>\n",
       "      <td>-39.140277</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>-10.3467</td>\n",
       "      <td>-6.4471</td>\n",
       "      <td>3.918135</td>\n",
       "      <td>-5.792482</td>\n",
       "      <td>1.874347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_Q1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0&gt;</td>\n",
       "      <td>c1ccc2c(c1)c3c(c2)ccc3</td>\n",
       "      <td>152.196</td>\n",
       "      <td>3.03680</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.535668</td>\n",
       "      <td>1.449581</td>\n",
       "      <td>-29.704334</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-9.7614</td>\n",
       "      <td>-8.4812</td>\n",
       "      <td>1.659369</td>\n",
       "      <td>-5.321255</td>\n",
       "      <td>3.661886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DCP1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0&gt;</td>\n",
       "      <td>c1cc(c(c(c1)C#N)C#N)C=C</td>\n",
       "      <td>154.172</td>\n",
       "      <td>2.07296</td>\n",
       "      <td>47.58</td>\n",
       "      <td>0.621214</td>\n",
       "      <td>3.166748</td>\n",
       "      <td>-30.195367</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-11.4556</td>\n",
       "      <td>-8.3660</td>\n",
       "      <td>3.124610</td>\n",
       "      <td>-6.685261</td>\n",
       "      <td>3.560650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smiles_key                                         mol_rdkit  \\\n",
       "0       P30T  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0>   \n",
       "1      P3HT1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040>   \n",
       "2       T_Q1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0>   \n",
       "3       DCP1  <rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0>   \n",
       "\n",
       "                    smiles  Poids_Moléculaire     LogP   TPSA       QED  \\\n",
       "0        CCCCCCCCC1=CSC=C1            196.359  4.65110   0.00  0.556752   \n",
       "1  CCCCCCC1=C(-*)SC(-*)=C1            166.289  2.22010   0.00  0.588361   \n",
       "2   c1ccc2c(c1)c3c(c2)ccc3            152.196  3.03680   0.00  0.535668   \n",
       "3  c1cc(c(c(c1)C#N)C#N)C=C            154.172  2.07296  47.58  0.621214   \n",
       "\n",
       "   HOMO-LUMO GAP (eV)  TOTAL ENERGY (a.u.)  HOMO Index  LUMO Index  \\\n",
       "0            3.906407           -39.140278          37          38   \n",
       "1            3.906110           -39.140277          37          38   \n",
       "2            1.449581           -29.704334          28          29   \n",
       "3            3.166748           -30.195367          28          29   \n",
       "\n",
       "   HOMO Energy (eV)  LUMO Energy (eV)  Gap_calibrated  homo_calibrated  \\\n",
       "0          -10.3390           -6.4490        3.910266        -5.786283   \n",
       "1          -10.3467           -6.4471        3.918135        -5.792482   \n",
       "2           -9.7614           -8.4812        1.659369        -5.321255   \n",
       "3          -11.4556           -8.3660        3.124610        -6.685261   \n",
       "\n",
       "   lumo_calibrated  \n",
       "0         1.876017  \n",
       "1         1.874347  \n",
       "2         3.661886  \n",
       "3         3.560650  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def calibrate_data(my_df):\n",
    "    # Iterate through each row in the DataFrame\n",
    "    for i in range(len(my_df)):\n",
    "        # Extract the current row's data\n",
    "        homo_lumo_val = my_df.loc[i, \"HOMO-LUMO GAP (eV)\"]\n",
    "        HL_range_rest = homo_lumo_val  # Initial value of HL_range_rest\n",
    "\n",
    "        # Determine the calibrated value of HL_range_rest based on its range\n",
    "        if 0.8856 <= HL_range_rest <= 3.2627:\n",
    "            HL_range_rest = 1.0\n",
    "        elif HL_range_rest < 0.8856:\n",
    "            HL_range_rest = 0.1144 + homo_lumo_val\n",
    "        else:\n",
    "            HL_range_rest = 4.2627 - HL_range_rest\n",
    "        # Calculate calibrated homo and lumo levels\n",
    "        homo_cal = my_df.loc[i, \"HOMO Energy (eV)\"] * 0.8051030400316004 + 2.5376777453204133\n",
    "        lumo_cal= my_df.loc[i, \"LUMO Energy (eV)\"] * 0.8787863933542347 + 3.7912767464357200\n",
    "        HL_range_rest1=abs(homo_cal)-abs(lumo_cal)\n",
    "        # Update the DataFrame with calibrated values\n",
    "        my_df.loc[i, \"Gap_calibrated\"] = HL_range_rest1\n",
    "        my_df.loc[i, \"homo_calibrated\"] = homo_cal\n",
    "        my_df.loc[i, \"lumo_calibrated\"] = abs(lumo_cal)\n",
    "    return my_df\n",
    "# Assuming 'df' is your DataFrame containing the data\n",
    "my_df=calibrate_data(my_df)\n",
    "my_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "## <a id='toc1_4_'></a>[MODEL DE SHARBER](#toc0_)\n",
    "<div  class=\"alert alert-info\">\n",
    "Courbes densité de courant-tension (J/V) dans l’obscurité (ronds noirs) et sous illumination\n",
    "(ronds blancs*) et caractéristiques principales des cellules solaires\n",
    "\n",
    "</div>\n",
    "<center><img src=\"./Graphics/Densite.png\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    " ###  <a id='toc1_4_'></a>[Densité de courant de court-circuit(${J_{sc}}$)](#toc0_)\n",
    "\n",
    "\n",
    " <div  class=\"alert alert-info\">\n",
    "Dépend du nombre de photons absorbes par le matériau et donc de l’ ́épaisseur\n",
    "de la couche active ainsi que de son spectre d’absorption et représente la quantité maximale courant électrique que cellule solaire organique peut générer dans des conditions de court-circuit\n",
    "\n",
    "</div> \n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "\n",
    "{J_{sc}}= Ae^{-E_{GAP}^2/B}\\\\.\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "A et B représente la paramètres d'ajustements et déjà fixés dans TartarusA = 433.11633173034136 ,B = 2.3353220382662894, ${E_{GAP}}$  représente le énergies du Gap du donneurs correspondant a la différences de deux orbitales moléculaire Homo et Lumo sa valeur maximale dans les travaux CEP est estimée ${3.8 eV}$ et donc les gamme de valeurs normales se situe entre ${[0.8856;3.2627 ]eV}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <a id='toc1_4_'></a>[Tension de circuit ouvert(V_oc)](#toc0_)\n",
    "\n",
    "\n",
    " <div  class=\"alert alert-info\">\n",
    "Représentent la tension maximale qu’une cellule solaire peut appliquer a une charge externes\n",
    "\n",
    "* Ecart entre la HOMO du composé donneur et la LUMO du composé accepteur\n",
    "\n",
    "* Influencée par la recombinaison des charges qui ne peut être totalement évitée \n",
    "\n",
    "</div> \n",
    "        \n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "{V_{oc}}={\\frac{1}{e}|E^{Do}HOMO|-|E^{AC}LUMO| -0.3}\\\\.\n",
    "\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "* ${e}$ represente la charge de l'electrons \n",
    "* $E^{Do}HOMO$ represente l'énergie  Homo du donneur sa valeur maximale dans les travaux CEP est estimée ${-3,62 eV}$  et donc la gamme de valeurs normales se situe entre ${[-5.7 ; -4.5]eV}$\n",
    "* $E^{AC}LUMO$ represente l'énergie Lumo de l'accepteur sa valeur maximale dans les travaux CEP est estimée ${-1,15 eV}$ et donc la gamme de valeurs normales se situe entre ${[-4 ; -3]eV}$\n",
    "* -0.3 est la tension seuil en dessous du quelle il n'y'a separation des excitons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <a id='toc1_4_'></a>[Facteur de forme / facteur de remplissage  (FF) ](#toc0_)\n",
    "\n",
    "\n",
    " <div  class=\"alert alert-info\">\n",
    " \n",
    "* Informe sur la capacité du transport des charges dans le dispositif et sur la qualité de l’interface entre le donneur et l’accepteur estimé a 65 %\n",
    "\n",
    "* Est proportionnelle a l'efficacite quantique externe lorsques des énergies photons absorbé >${E_{GAP}}$  \n",
    "</div> \n",
    "        \n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "                 \n",
    "{FF}= \\frac{V_{oc}}{V_{oc}+a*{K_{b}}*T}\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "* ${a}$ le coefficient d'absorption \n",
    "* $ {K_{b}}$ la constance de Boltzmann\n",
    "* ${T}$ la temperature externes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <a id='toc1_4_'></a>[Efficacité de conversion (PCE)](#toc0_)\n",
    "\n",
    "\n",
    " <div  class=\"alert alert-info\">\n",
    "Rapport entre la puissance électrique produite par la cellule et la puissance lumineuse incidente  sa valeurs actuelles est estimée a 12% l'objectif est les 20%\n",
    "\n",
    "</div> \n",
    "        \n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "                 \n",
    "{PCE}={100} \\frac{V_{oc}*FF*J_{sc}}{P_{in}}\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "* puissance lumineuse incidente $ {P_{in}} $ \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian(x, A, B):\n",
    "    return A * np.exp(-x** 2 / B)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define parameters for Scharber model\n",
    "A = 433.11633173034136\n",
    "B = 2.3353220382662894\n",
    "Pin = 900.1393292842149"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import norm  # Assuming 'norm' is used for the Gaussian function\n",
    "\n",
    "def calculate_voc_pce_jsc(my_df, Pin, A, B):\n",
    "    \"\"\"\n",
    "    This function calculates VOC, PCE, and Jsc for each row in the DataFrame and adds them as new columns.\n",
    "\n",
    "    Args:\n",
    "        my_df (pandas.DataFrame): The DataFrame containing molecule data with 'homo_calibrated' and 'lumo_calibrated' columns.\n",
    "        Pin (float): The incident light power density.\n",
    "        A (float): Gaussian function parameter A.\n",
    "        B (float): Gaussian function parameter B.\n",
    "\n",
    "    Returns:\n",
    "        pandas.DataFrame: The modified DataFrame with VOC, PCE, and Jsc columns.\n",
    "    \"\"\"\n",
    "    for i in my_df.index:\n",
    "        # Scharber model objective 1: Optimization of donor for phenyl-C61-butyric acid methyl ester (PCBM) acceptors\n",
    "        voc_1 = (abs(my_df.loc[i,\"homo_calibrated\"]) - abs(-4.3)) - 0.3\n",
    "        if voc_1 < 0.0:\n",
    "            voc_1 = 0.0\n",
    "        lumo_offset_1 = my_df.loc[i, \"lumo_calibrated\"]  + 4.3\n",
    "        if lumo_offset_1 < 0.3:\n",
    "            pce_1 = 0.0\n",
    "        else:\n",
    "            jsc_1 = gaussian(my_df.loc[i, \"Gap_calibrated\"] , A, B)\n",
    "        if jsc_1 > 415.22529811760637:\n",
    "            jsc_1 = 415.22529811760637\n",
    "        pce_1 = 100 * voc_1 * 0.65 * jsc_1 / Pin\n",
    "\n",
    "        # Scharber model objective 2: Optimization of acceptor for poly[N-90-heptadecanyl-2,7-carbazole-alt-5,5-(40,70-di-2-thienyl-20,10,30-benzothiadiazole)] (PCDTBT) donor\n",
    "        voc_2 = (abs(-5.5) - abs(my_df.loc[i, \"lumo_calibrated\"])) - 0.3\n",
    "        if voc_2 < 0.0:\n",
    "            voc_2 = 0.0\n",
    "        lumo_offset_2 = 3.6 + my_df.loc[i, \"lumo_calibrated\"]\n",
    "        if lumo_offset_2 < 0.3:\n",
    "            pce_2 = 0.0\n",
    "        else:\n",
    "            jsc_2 = gaussian(my_df.loc[i, \"Gap_calibrated\"], A, B)\n",
    "        if jsc_2 > 415.22529811760637:\n",
    "            jsc_2 = 415.22529811760637\n",
    "        pce_2 = 100 * voc_2 * 0.65 * jsc_2 / Pin\n",
    "\n",
    "\n",
    "\n",
    "        # Add separate VOC, PCE, and Jsc for each objective\n",
    "        my_df.loc[i, \"voc_pcbm\"] = voc_1\n",
    "        my_df.loc[i, \"jsc_pcbm\"] = jsc_1\n",
    "        my_df.loc[i, \"pce_pcbm\"] = pce_1\n",
    "\n",
    "        my_df.loc[i, \"voc_pcdtbt\"] = voc_2\n",
    "        my_df.loc[i, \"jsc_pcdtbt\"] = jsc_2\n",
    "        my_df.loc[i, \"pce_pcdtbt\"] = pce_2\n",
    "\n",
    "    return my_df\n",
    "\n",
    "# Assuming you have defined the values for Pin, A, and B\n",
    "\n",
    "my_df = calculate_voc_pce_jsc(my_df, Pin, A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles_key</th>\n",
       "      <th>mol_rdkit</th>\n",
       "      <th>smiles</th>\n",
       "      <th>Poids_Moléculaire</th>\n",
       "      <th>LogP</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>QED</th>\n",
       "      <th>HOMO-LUMO GAP (eV)</th>\n",
       "      <th>TOTAL ENERGY (a.u.)</th>\n",
       "      <th>HOMO Index</th>\n",
       "      <th>...</th>\n",
       "      <th>LUMO Energy (eV)</th>\n",
       "      <th>Gap_calibrated</th>\n",
       "      <th>homo_calibrated</th>\n",
       "      <th>lumo_calibrated</th>\n",
       "      <th>voc_pcbm</th>\n",
       "      <th>jsc_pcbm</th>\n",
       "      <th>pce_pcbm</th>\n",
       "      <th>voc_pcdtbt</th>\n",
       "      <th>jsc_pcdtbt</th>\n",
       "      <th>pce_pcdtbt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P30T</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0&gt;</td>\n",
       "      <td>CCCCCCCCC1=CSC=C1</td>\n",
       "      <td>196.359</td>\n",
       "      <td>4.65110</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.556752</td>\n",
       "      <td>3.906407</td>\n",
       "      <td>-39.140278</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.4490</td>\n",
       "      <td>3.910266</td>\n",
       "      <td>-5.786283</td>\n",
       "      <td>1.876017</td>\n",
       "      <td>1.186283</td>\n",
       "      <td>0.621048</td>\n",
       "      <td>0.053201</td>\n",
       "      <td>3.323983</td>\n",
       "      <td>0.621048</td>\n",
       "      <td>0.149069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P3HT1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040&gt;</td>\n",
       "      <td>CCCCCCC1=C(-*)SC(-*)=C1</td>\n",
       "      <td>166.289</td>\n",
       "      <td>2.22010</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.588361</td>\n",
       "      <td>3.906110</td>\n",
       "      <td>-39.140277</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.4471</td>\n",
       "      <td>3.918135</td>\n",
       "      <td>-5.792482</td>\n",
       "      <td>1.874347</td>\n",
       "      <td>1.192482</td>\n",
       "      <td>0.604880</td>\n",
       "      <td>0.052086</td>\n",
       "      <td>3.325653</td>\n",
       "      <td>0.604880</td>\n",
       "      <td>0.145261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_Q1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0&gt;</td>\n",
       "      <td>c1ccc2c(c1)c3c(c2)ccc3</td>\n",
       "      <td>152.196</td>\n",
       "      <td>3.03680</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.535668</td>\n",
       "      <td>1.449581</td>\n",
       "      <td>-29.704334</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.4812</td>\n",
       "      <td>1.659369</td>\n",
       "      <td>-5.321255</td>\n",
       "      <td>3.661886</td>\n",
       "      <td>0.721255</td>\n",
       "      <td>133.211487</td>\n",
       "      <td>6.937998</td>\n",
       "      <td>1.538114</td>\n",
       "      <td>133.211487</td>\n",
       "      <td>14.795638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DCP1</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0&gt;</td>\n",
       "      <td>c1cc(c(c(c1)C#N)C#N)C=C</td>\n",
       "      <td>154.172</td>\n",
       "      <td>2.07296</td>\n",
       "      <td>47.58</td>\n",
       "      <td>0.621214</td>\n",
       "      <td>3.166748</td>\n",
       "      <td>-30.195367</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.3660</td>\n",
       "      <td>3.124610</td>\n",
       "      <td>-6.685261</td>\n",
       "      <td>3.560650</td>\n",
       "      <td>2.085261</td>\n",
       "      <td>6.621654</td>\n",
       "      <td>0.997081</td>\n",
       "      <td>1.639350</td>\n",
       "      <td>6.621654</td>\n",
       "      <td>0.783866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  smiles_key                                         mol_rdkit  \\\n",
       "0       P30T  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c45f0>   \n",
       "1      P3HT1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c4040>   \n",
       "2       T_Q1  <rdkit.Chem.rdchem.Mol object at 0x74d45f1c43c0>   \n",
       "3       DCP1  <rdkit.Chem.rdchem.Mol object at 0x74d44c8fa6d0>   \n",
       "\n",
       "                    smiles  Poids_Moléculaire     LogP   TPSA       QED  \\\n",
       "0        CCCCCCCCC1=CSC=C1            196.359  4.65110   0.00  0.556752   \n",
       "1  CCCCCCC1=C(-*)SC(-*)=C1            166.289  2.22010   0.00  0.588361   \n",
       "2   c1ccc2c(c1)c3c(c2)ccc3            152.196  3.03680   0.00  0.535668   \n",
       "3  c1cc(c(c(c1)C#N)C#N)C=C            154.172  2.07296  47.58  0.621214   \n",
       "\n",
       "   HOMO-LUMO GAP (eV)  TOTAL ENERGY (a.u.)  HOMO Index  ...  LUMO Energy (eV)  \\\n",
       "0            3.906407           -39.140278          37  ...           -6.4490   \n",
       "1            3.906110           -39.140277          37  ...           -6.4471   \n",
       "2            1.449581           -29.704334          28  ...           -8.4812   \n",
       "3            3.166748           -30.195367          28  ...           -8.3660   \n",
       "\n",
       "   Gap_calibrated  homo_calibrated  lumo_calibrated  voc_pcbm    jsc_pcbm  \\\n",
       "0        3.910266        -5.786283         1.876017  1.186283    0.621048   \n",
       "1        3.918135        -5.792482         1.874347  1.192482    0.604880   \n",
       "2        1.659369        -5.321255         3.661886  0.721255  133.211487   \n",
       "3        3.124610        -6.685261         3.560650  2.085261    6.621654   \n",
       "\n",
       "   pce_pcbm  voc_pcdtbt  jsc_pcdtbt  pce_pcdtbt  \n",
       "0  0.053201    3.323983    0.621048    0.149069  \n",
       "1  0.052086    3.325653    0.604880    0.145261  \n",
       "2  6.937998    1.538114  133.211487   14.795638  \n",
       "3  0.997081    1.639350    6.621654    0.783866  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
   "metadata": {},
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   "source": []
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