{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59e02e86-0f75-481b-9501-31d21e43eb84",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b190bf82-0b95-4eb6-b7fb-e78973d5a0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.plams import Settings, Molecule, AMSJob, Units, view, plot_image_grid\n",
    "from scm.plams.recipes.adffragment import ADFFragmentJob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53117bc2-7848-43f3-9ffa-816450e36cd2",
   "metadata": {},
   "source": [
    "## Define the molecules\n",
    "For convenience we define here two molecules, normally you would read them from xyz files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c91b68f-e152-4128-b037-18de1c6f99ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAACgCAYAAAB9uQooAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3y0lEQVR4nO3deZBdZZ3/8fe59567r7337e7sabLQSUiEBIghkcVBZFEk6CAYEcEFZ3DGUqum+KljlTNCjVUTalQUZApUFAoRZ4QEIUiFEcKSnexLJ5303ne/99z9/P6gzmM3QSUhye2mv6+qU8Fe0k9fb518zvN9nu+jmaZpIoQQQgghJgVbrQcghBBCCCHOHgl/QgghhBCTiIQ/IYQQQohJRMKfEEIIIcQkIuFPCCGEEGISkfAnhBBCCDGJSPgTQgghhJhEJPwJIYQQQkwiEv6EEEIIISYRCX9CCCGEEJOIhD8hhBBCiElEwp8QQgghxCQi4U8IIYQQYhKR8CeEEEIIMYlI+BNCCCGEmEQk/AkhhBBCTCIS/oQQQgghJhEJf0IIIYQQk4iEPyGEEEKISUTCnxBCCCHEJCLhTwghhBBiEpHwJ4QQQggxiUj4E0IIIYSYRCT8CSGEEEJMIhL+hBBCCCEmEUetByCEEEKIsyOfz5PL5SiXy3g8HjweDw6HRIHJRjNN06z1IMTElslk6O3txTAMHA4HNpuNuro66urq0HW91sMTQohJzzAMXn/9dVKpFB0dHUSjURwOB6lUCpfLRVNTE5qm1XqY4iyR8CdOmWEYbNq0iWQySTQapampiXA4jMvlolqt0t/fj8vloqWlBbvdXuvhCiHEpGQYBj//+c/RNI2uri7OOeccQqGQ+nwulyORSBCNRiUAThKy5k+ckng8zi9+8Qt6enpobGyktbWV1tZWgsEgLpcLj8fD9OnTqa+vp7u7m1KpVOshCyHEpGOaJk8++SS7d+/G4/Hg9XrxeDwAaJqGpml4vV6CwSCpVKrGoxVni4Q/cdLy+TwPPfQQsViMUChEMBgkFAqh67q6mViXy+Wio6OD3t5eZJJZCCHOrqGhIR5//HFM06RSqVAqlahWq2O+RtM0/H4/8Xhc7tOThIQ/cVJM02T9+vXs27cPt9uNruvouo7T6XzHr9c0DV3XCQQCjIyMnOXRCiHE5LZr1y6OHz9OLpcjlUqRSCRIJpOYpqmCnmmaVKtVcrkclUqlxiMWZ4OEP3FSstksv/nNb9S6kGq1qq6/xu/309PT8ze/TgghxOkzMDBAOp1meHiYoaEh+vv76enpYWRkhHK5rO7f6XSaQqEgM3+ThOzvFiflwIEDHD58mHPOOYdisUg+n8cwDAzDwO12/8XvM00Tm81GoVBQ602EEEKcWcFgkGKxSG9vL5FIBK/Xq+7FjY2NeL1eKpUKiUSC3t5eurq6aj1kcRZI+BMn5fDhwySTSXK5HJlMhnQ6TSKRIB6P4/F4cLvd77hbrFAoYLfbyWazEv6EEOIsmTt3LuFwmMHBQbxeL3a7nVKpRCaTYXBwEI/Hg2mapNNpQqGQdGaYJCT8iZOSy+XI5/Mkk0kV+nw+Hx6PB7vdTkNDAx6PB5vtrRUF1WqVfD5PIpEgm81SLpdr/BsIIcTkoWka559/PsPDw7z55ptUKhWy2SzxeJxQKITb7aZarVKpVLjtttuk1cskIeFPnJRwOEyxWGRkZIRIJILP58PpdGK326lUKhiGQTAYVDOApVJJ3WgGBweZPn16rX8FIYSYFNLpNP/93//NnDlzmDNnDlu2bOGRRx4hkUjQ19dHMBhE13Xcbje33347TU1NtR6yOEsk/ImTMmPGDHw+HyMjI/j9flwuF3a7HdM0KRaLZDIZAoGA+rgVCOPxOCMjIwQCgVr/CkII8b5XKpV4/PHHqVardHR04HK56OzsZMmSJWzatEk9qH/gAx/g9ttvZ9GiRTLrN4lI+BMnZdq0aXR1dbFx40Z6e3txOByYpkmpVMIwDFKp1JhQWK1WMQyDkZERZsyY8Vc3hQghhHjvTNNk3bp17Nixg66uLrxeL7FYjH379nH55Zfz1a9+lUqlQn19Pe3t7X+xVZd4/5LwJ06Kx+Ph1ltvZcuWLaRSKY4ePUqpVCKfz5PJZAgGg/h8PlwuFzabTa0vKRaLrF69Wp4shRCTRj6fJxaLUSgUcLlchEIhvF7vGb8Pbt++nWeeeYbOzk7C4TCZTIbDhw/j9/tZvXq1PIQLCX/i5K1cuZKbb76ZBx54gGQyqcq91gJir9eL0+lE0zTVDuZTn/oUzc3NtR66EEKcceVymR07dnD8+HE6Ojpob2/H5XKRTqc5fPgw06dPx+fznZGf3d/fz09/+lOampqor6+nUCjQ29tLOp3m1ltvleAnAAl/4hTous5dd92F3W7n4YcfJpVK0dvbSywWU+dGWuXg+vp6PvrRj1JXV1frYQshxBlXqVRYv349/f39LFy4kBkzZuD3+wHw+Xw0NTXR3d1NU1OT+vjpks1m+a//+i+cTifNzc2YpklfXx+9vb18+ctfpqGh4bT+PDFxSfgTp8Tn8/G1r32NpUuX8sgjj7Bt2zbi8TixWAzTNAmHw1x00UV85CMfobm5mWQyiWEYeL3eWg9dCCHOmB07dvDSSy+xZMkSwuGwuudZpV5N05g6dSqHDh1ixowZJ91XzzRNEokER44cYXBwEICGhgai0ShPPfUUfX19dHZ24nA4GBoa4ujRo1x33XXSaUGMIeFPnDKHw8GqVau4+OKL6e3t5dixYzzzzDM89NBDzJo1iylTplAulykWi+i6Tl9fHzNnzqz1sIUQ4oyoVCo89thjmKaJpmljAt9oNpuNQCBAJpMhFAq9679/eHiY3/3ud7z++uvkcjncbjdOpxOHw4Hb7WZgYICWlhY8Hg/JZJKenh4+8IEP8MEPflDWW4sxJPyJ98zpdDJt2jSmTZvG1KlTeeKJJzh+/Djt7e3q+CCn08n+/fvp6OiQnWVCiPelRCLB7t27mTZtGtlslnQ6TbFYHHPykWma6tq3bx+hUIi6ujrVJeEvhbTt27dz3333MTw8jN/vJxAI4Pf78Xg86LqOaZq0trZSKpVIJBKMjIzQ0NDAxz/+cRwO+adejCXvCHFatbW18bGPfYxHHnmEvr4+mpubCQaDRCIRisUiR44cYfbs2bUephBCnHaGYTA0NEQoFGJkZIT+/n5CoRDRaFQ99JqmiWEYJBIJfD4fDoeD7u5uMpkM+XyeSCRCc3MzdXV16ji2w4cP86//+q/qCDaXy4XX6yUUChEOh3G73ZimSS6XUycvVatVbrvttjO2sURMbBL+xGlls9m46aab+OUvf0lvby+tra3U1dXhdrvVTWzmzJnq+DchhHi/sGbhjh8/TkNDAz6fD7vdjmEY1NXV4XQ61czc8ePHCQQC2O121SKrUCiQyWTYu3cv6XQa0zTx+Xw899xz9PX1EQ6HsdvtOJ1OvF4vwWCQuro6fD4fmqaRyWTQNI1yuYzf7z+pkrKYXCT8idNuzpw5XHrppTz//POq9Ov3+wkGg8RiMfr7+4lGo7UephBCnFbhcJh58+axYcMGFexKpRLJZJJwOIzL5aJSqZBOp+nu7sYwDEKhkCrfut1udF3H4XAQCoUoFotks1lKpRKapqm1hDabTYVAt9uNz+dTD9SGYZBOpxkaGmLbtm1ceOGFNX5VxHgk4U+cdg6HgzVr1rBu3Tr6+vpoamoiFAqpsseOHTtobW2VBchCiPcVu93OTTfdxIsvvsi+ffsol8tks1mGh4dVubZSqZBIJNi+fTuHDx8mEAhQV1dHOBweEwStMOhwOMhkMlSrVUzTpFqtUqlU1J/WZbHCoWmabNu2jQsuuOCkdxSL9z8Jf+KMOP/881myZAm7du1SpV+fz4fX62VgYIB0Ok0wGKz1MIUQ4rRaunQpX/3qV7n33nvZvXs3sViMxsZGgsEguq5TLBYJBoP8v//3/0gkEmzZsoVt27Zx8OBB4vG4KgMHAgECgQBer5d9+/ZRqVQol8uUSiWKxSKGYZDNZkmlUpimicPhIJ/Pk8/nKZVKVCoVent7KZfLEv7ECST8iTPC4/GwZs0avvCFLzAwMEBDQwOhUAiHw0GlUmHbtm188IMfrPUwhRDitLLb7dxyyy00NDTwwAMPsH//fgYGBnC5XDQ0NLBy5Upuv/12tfRl+fLllMtlDMPg+PHj7Nq1i82bN7Njxw527dpFNpvFNE0CgQClUolCoYBhGGQyGXRdB946Rs4qMWcyGXK5nDpdafSsoBAWzTRNs9aDEO9PiUSCq666iuHhYRYsWEBnZ6fa9Wa32/nUpz6Fx+Op9TCFEOKMSKVSHDhwgOHhYXRdZ8aMGbS1tf3N1iumaVIsFhkaGuIrX/kKmzdvVuumfT6favVitXuxysOVSoV8Pk82myWXyxGJRPje976Hy+U6S7+xmChk5k+cMaFQiJtvvpm7776boaEh6uvr8fv9hMNhCoUCb775JlOmTMFms6legNKPSgjxfhEMBlm8ePFJf5+mabhcLtra2ujq6uK1114jn8+j6zp2u11t7qhUKhQKBVwul1rnZ5WF8/m8OulDiLeTd4U4YzRN49prr+W+++5Tpd9gMKhuVI8//jjpdBpd1/F6vUSjUebOnauORZINIUKIyUzTNFatWsWjjz6qyr1W8KtWq5TLZQqFggqFgFobWC6XWbJkiaz3E+9Iwp84o5qbm7nhhhv44Q9/yMjICJFIBI/HQ7lcJpFIYLfbMU2TfD7PoUOHOHToEC+//DLLly/noosuknKFEGJSW7p0KRdeeCEvvPAChmEAb5WFrZBXLBZxOBwnhMKOjg6WLVtWy6GLcUzCnzijbDYbK1as4P777yeZTBKPxymXy+osSrfbjcvlUqUJq0v9M888Q09PD9dff/1p6VBvGAaxWIxSqYTNZlNrZqwF00IIMR55vV6+/vWvc+DAAY4ePYphGFQqFVXetdZQjy4Fm6aJ0+mkWq3WePRivJLwJ86o/fv38+Mf/5hZs2YRDAZVCcI6nsjn8+HxeHA6ndhsNlXGMAyDnTt3Ypomq1evPuWNIZlMhldeeYX+/n7q6upoaWkhEolQKBRUW4X29nZZFyOEGLfmz5/Pv//7v/Ptb3+b/fv3q3YuVsnX4XCoJtAAnZ2dVCoVDh48eEprDsX7n5yxJc6YVCrFPffcQ09Pjzr2yOFwoOu6Cn+BQIBwOExdXR11dXXU19cTiURUf6s333yTF154gVPZlN7b28vatWvZtm0bgUCAaDRKe3s77e3ttLS0MGPGDKLRKN3d3eTz+TPwCgghxHunaRorVqzgZz/7GWvWrKG+vl61h8lkMqRSKTRNI5lMqvV/pmmyadMmafUi3pGEP3HGPPXUU7z22mvY7fYxRxI5HA51LJHX61UtDMLhsOpyHwgE8Hg8OBwO/vSnP9Hf339SPzsWi3HvvfcyMjJCOBwmGAwSCoVUr0FN09A0TbVfGBkZoVwun6FXQggh3rtp06bx7W9/m7vuuguAaDTKokWLuOCCC/jSl77EvHnzyGazZLNZ4K3KSzqdruGIxXgl4U+cEclkkl//+tfqLMrRlxUCdV1XIdDj8eD1esdcbrcbp9NJLpfjpZdeolQqveNVLBbHXPl8nieffJKDBw/i9XrRdV3NNuq6PmYXsTWepqYmYrFYDV8xIYT42+x2O5dddhnBYJByuYzT6SQcDtPb28tHP/pRMpmMOg84k8mwb9++Wg9ZjEOy0EmcETt27ODQoUM4nU5M0xxzjTY6EFoLlh0OB3a7XS1i1jSNV155hWQyCaBmD60ZRWsDyejvq1QqzJkz56/+7NEcDgfZbFb9XUIIMV61traybNkyNm7cSDabJRwO09/fz9KlS9F1nXQ6TS6Xw+l08qc//UlavogTyL9y4ozYtm0bhmGg6zrValVd1iHkVh+q0TN38Oc2BdYh5vDnHcCFQoFQKITT6VSzhn/psk4UWbdunTrvMp/PUy6XT5j9s/j9frLZLKFQ6Ky+VkIIcTIcDgfXXnstzz77LJlMhkKhAMDAwADnnnsue/fuVfey7u5uRkZGaGpqqvGoxXgiZV9xRhw+fFiFPevPtwe+fD6PYRjkcjmy2awqV+RyOfL5PMViUQVBq0WLz+dTZWGPx6NaxbhcLhUKrRnAhoYGyuUy2WyWdDpNMplUa2HePgtomqYamxBCjHcXXngh9fX1apbPZrOxf/9+Lr/8crXur1gsks1m2blzZ62HK8YZmfkTZ0Qul1OBavQ1OvjlcjlViiiVSqrnXrFYxDAMDMOgUChQKpUol8sMDg6OKfGOvqxS8Oj/bRgGiUQCr9dLLBbD7/fj8XhUnz+rzGyapvrahoaGmr1mQgjxbjU0NHDJJZfw+9//nkwmQzgcZnh4mGXLluHxeFQodLlcbN68mRUrVsiSFqHIO0GcET6fT5Vw3yn4vb0paT6fVzcmq9ff6BnAYrHIgQMHcLvdan2g9XdYf779SqfT9Pb24nQ6T+glWF9fj9frxWazUSwWicfjDA0NEY1Ga/myCSHEu2K327n22mt58sknVelX0zT6+/tZvHgxW7ZsGVP6HRoaorW1tdbDFuOEhD9xRsycOVNtxrCOH7I2Y4wOflZJ1zAMFf6s7vWFQkGFP8Mw6OnpoVKpnLBeT9M0NYNodbbPZrMUCgV1ioi1zq9SqYyZEbTb7ZRKJRKJBLlcDr/ff3ZfKCGEOEWLFy8mGo2SSqXIZrN4vV5V+n3ppZfIZDIUi0U0TWPnzp0S/oQi4U+cEQsWLMDj8aiNGlb4s3bvwp/PpywWi6oxqdWlfvRMYaFQIBqN8uUvf1l9/+iyrbXmD94Kgi6XiwcffJCf/exnKjQC6mdlMhmCwaAqAZdKJZLJJBdeeKHsiBNCTBjhcJhLL72URx99lEwmQyQSIZFI0NLSQiAQIJ1OYxgGbrebzZs3s2rVKin9CkDCnziD3G63mrl7p+BnlYVHH0xus9lUKBx9fuWFF17IZZdd9o67dN/JF77wBbZs2cLmzZsZHBykUqlQKBTUDTIQCKjAmM/nmTt3LvPnzz9jr4UQQpxuNpuNq6++mp///OdkMhny+Txer5fjx4+zYsUKNmzYQDqdJhQKcfToUUZGRmhubq71sMU4ILt9xWmXzWb50Y9+hNfrpaOjA0Cd12u1XLF2+b59p28mkyGTyZDL5dTXBIPBkwp+8FYfrHvuuYelS5cCMDQ0xIEDB9i7dy979uxh9+7d7Ny5kzfffJNoNMq1114rT8RCiAlnwYIFzJgxQ5V+NU3j0KFDrFy5knw+r3b9FotFtm/fXuvhinFCwp84rUzT5De/+Q27du0iGAzS3t7O1KlTKZfLKvRZIXB0ALRCoNXqxQqCxWKR1atX09LSctJjmTNnDj/5yU/4xje+QVdXFw6Hg6GhIY4ePUpvby+BQIA77riD2267DZ/PdwZeDSGEOLN8Ph8f/vCH1YNzpVIhFovR09NDS0uLWtesaRobN25kz549cpSlQDP/2rEHQpyk7u5ubr31VsrlMg0NDfj9fkqlElu3biWVSmGz2dQaPevYtdElX2vNn3Vdf/313HnnnTidzlMek2mapNNpjh07xvDwMLquE41GaWlpUaVfIYSYqLZu3cq1115LS0sLM2bMwOPxkEwm1dGZ1lGZALquM3/+fK655hrC4XBtBy5qRupc4rQplUr88Ic/JJ1O09jYiMfjUTt+6+vryeVyqq1LqVTC6XSO6ctnHfVWrVZxu9188pOf5I477nhPwQ/e2gQSDAaZN2/eafpNhRBi/JgzZw5z5szh6NGjpNNpisUiTqcTr9dLIBDA5/PhdrvRNI1isciuXbtIJpPcfPPNEgAnKSn7itPmpZdeYsOGDQSDQXw+Hw6Hg1KpRDabZcqUKaxYsUJt6CiXyxiGoUoV6XSadDpNPp+ns7OT733ve9x55514PJ5a/1pCCDGuuVwuPvShD1EoFMhms1QqFdXfNBAIUF9fT1NTEy0tLTQ3NxMOh+nt7eWZZ56REvAkJTN/4rSIx+OsXbsWp9OJ3+/H7XZjmqY6T/fqq6/mwQcfpK2tDa/XS7FYJJVKqcPHOzs76erqYtWqVZx//vnSb08IId6lfD7Pzp07iUQiY/qpulwufD4foVCIcDiMruvk83nV7H779u0sWbKEzs7Od/2zyuUyyWRS9VT1+XzSImsCkvAn3rNqtcovfvELjhw5QlNTk2qenM/nyWQyXHDBBezcuZN4PE5TUxORSASn00kulyMWi3HjjTdyyy23oOu66t0nhBDi3dmzZw/btm0bs4bZOv3I6XTidrvxer24XC4cDgeFQoF0Ok0qleKNN95g9uzZf7ObQqVS4bXXXmP37t20tLTQ2tpKOBzm6NGj1NXV0draelIdGURtSfgT79nevXv51a9+pdaWOJ1OyuUyuVwOr9dLV1cX9957ryoH67quysFTp07lhhtukI0XQghxirZu3YphGOi6TrVapVqtql6po69qtaq+x1pjvWvXLtatW0dzczOBQIBQKITH40HX9TFHYj711FNs2bKFBQsW0NHRwdSpU/H5fGiaRiaToa+vTwLgBCLhT7wn+Xye++67j1KpRCQSUWv0rFYu119/PU888QR2u/2EcnClUuGzn/2sLDgWQoj3IJFIUCqVqFQqJ5yOlMvlyGQyOBwOisUipVJJnbxUKpVUcIvFYqrXqd1uR9d1Vdotl8s8/fTTdHZ2EgqFiEQi+Hw+Vanx+/1Uq1WGhoZoamqq5Ush3iUJf+I9Wb9+PZs2bSISieD1etUNJpvNMmPGDIrFIvv27VPlYKvkkMlkWL58uWrCLIQQ4tRYG+mscGcFv2w2q2bvrA4LlUpFbbSz1mTbbDbVFmZ0axjrstvtJJNJ+vv7sdlsOByOMTN8VkeFffv20djYKLN/E4CEP3HKBgcH+clPfoLH48Hv9+NyuahUKuRyOUzT5LLLLmPt2rWqHOxyuVQ5OBAI8NnPfhZd12v9awghxITW0dGh2rgUCgXVP9VqoVWpVDAMA4fDoSov2WxWNdrfvHkzwWAQv99PIBAgEAgQDAbVfwcCARwOB/l8Xp2+ZLUIHh30PB6PCplifJPwJ05JtVrlwQcfZGBggMbGRrxeL5qmqafNlStX8tJLL2EYhpr1sz5vGAaf/vSnaWtrq/WvIYQQE97ixYupr69naGhIhTwr+JmmqdZYW2Vda3Ywl8uRSqXo7e2lUqngcDjw+Xwq/FlXKBTi0KFD6LpOLBZjcHCQSCRCMBhUIdDq4Tp6XaEYvyT8iVOydetWfvvb36pZPV3XKZfLZLNZIpEIU6ZM4YknniAcDquef9bnOzs7+chHPiKlASGEOA06Ojq48sorefjhhzEMQ63FM02TSqVCoVBQ5Vt46+G9WCwSiUT4/ve/j8vlIplMMjQ0RF9fH729vQwMDDA0NER3dzd9fX3k83nmzp1LfX09gUAAXddpb29X6/3i8Tj9/f3MmDGjli+FeJck/ImTlsvlWLt2LcAJmzgKhQLXX389jz32GC6Xi0AgoD5vGAYAt956q5ylK4QQp4ndbueOO+5g48aNHDx4kGw2q4JfqVRSpWDrCM1qtYrNZuPGG29k+vTpaJp2QiXGasZfKpW49957+dnPfkZ3d7dq5VUqlYjH4wQCAUzTJB6P4/F41OyiGN/k/yVxUkzT5Le//S07duygvr5ezepZa0jmzZvH4OAgx44do6mpCY/HM6bn35VXXsnChQtr/WsIISYh0zQZHBzkyJEjpNNpdF3H7XbT2tpKa2vrhA4uxWIRTdOor68nHo+TyWRU8HM6nSr8maaJx+Phc5/7HJdddtlfrMBomqbOX1+zZg3/93//x759+9TmkXQ6TV9fHz6fT4XJT37yk2f5txanauK+00VN9PT08OCDD+L1elVPP2sxsd1uZ/ny5fzgBz9QPf2sTR7ZbJaGhgZuuukm6QYvhDjryuUyGzdupL+/n5kzZzJ37lx1klAqlWLr1q3MnTt3QlYlisUia9eupVwu09nZSbFYZO/evRQKBYrFomrd4vP5WLBgAZ///OdZtmzZu74XT506lf/4j//g7rvvZseOHaRSKfr6+lR7r1mzZrFmzRpCodAZ/k3F6SLhT7wrVsD70Y9+RCqVGrOJw9oBduWVV/LMM89QqVQIBAJjev4Vi0VuueUW6QElhDjrTNPkD3/4A4cPH2bRokXMnDmTSCSiZr2sDQ779+9n/vz5E64LwbPPPssrr7xCc3OzqrYsXryYa665hoGBAcrlMs3NzcyfP5+5c+fidrtP+md0dXXx0EMP8dxzz7Fp0yZisRitra1cfPHFXHLJJRMyNE9mEv7EX5VMJnn11Vd5/fXX6enpYffu3aqnn67rqqdfS0sLoVCILVu2UF9ff8LnFy5cyKpVq2r96wghJqFjx47xwgsvsGTJEhobGwmFQuqEC3grHAYCARobG+np6ZlQmxb6+/v50Y9+hN/vVzOZ2WyWL33pS/zd3/2d+rrTscEuEolwww03cP3116uPyZGcE5OEP/GOisUizz33HE8++SRDQ0M4nU5cLhfTp0/HZrOphcNWB/jp06fz2GOPqXKw2+2mWq2Sy+XQdZ3Pfe5zcoSbEKImnn/+eQBcLhcul0vdvyzWvczv93Pw4EGmTp06IZanVCoVfvrTnxKLxWhra8PpdJJOpzn33HNZtWrVGeuoIIFv4pPwJ06QSqX48Y9/zIYNG9B1XR0IbnV913Vd9ZDy+/1EIhGGh4cJBoM4HA68Xi82m001EF29ejWzZ8+u9a8lhJiEKpUKe/bsoVKpUCwWKRaLmKaJaZonhCPr/Nt8Pj8hypivvvoqTz/9tFp7Z236WLNmzSmVdsXkIeFPjGEYBj/4wQ944YUX1IYNl8s15tgfl8ulwl+1WlU7yux2O+l0ekxT0Wg0yic+8Ql5UhRC1ITVZiqbzZJIJBgeHqahoYFQKKQCoHXPSiQSZDIZ1bh4PEulUqxduxaXy0UoFMJms5FMJvnYxz5GZ2dnrYcnxjkJfxOE1UTz6NGjxGIxAJxOJ6FQiObmZhobG99zwDJNk1//+tesX79eNW7WdR2n04nb7R5T0nU6nerYIOvUDquNQDabVWdG3nbbbYTD4dPwCgghxMmz2+20tbXxxz/+kd7eXsLhMC6Xi46ODvx+vzoWbWRkhGPHjhGPx8f9rJlpmjz66KMcOnSIaDSKy+Uim83S3t7O9ddfLw30xd8k4W8CyGQyvPTSSwwNDdHS0kJTUxOBQEC1WRkYGODgwYN0dXURCARO+ed0d3fz8MMPo2kadrtdtQewwp917I/V4sVms1Eul8cEv0qlQrlcplgssmzZMpYuXXoaXwkhhDg5mqaxatUqfvOb33Dw4EF130wkEoRCIRwOB4VCgXg8Tnd3N9OnTx/3/f727t3LL3/5SyKRCD6fTzVjXrNmDcFgsNbDExPA+H6HC0ZGRnj00Ufxer3MmjWLtrY2GhoaVJd10zRpaWkhmUyya9cuZs2aRX19/Un/HKt588jIiCohWAHQ4XCcEADdbrdq9mkdFm6FPqfTSXNzM7fccsuEa5kghHj/WbRoEZdffrlqRZXJZBgcHCQQCOBwONTMn8PhoFqtkkqlxm2IyufzrF27lmq1qsJrPB5nxYoVLF68uNbDExOEhL9xLJfL8eCDD6LrOtFolObmZpqbm9VCZGtq32az0dDQgMPh4ODBg3g8HrxeL4Bau2L9aa3Tq1ar6mmxUqmQzWZ58cUX1d9ptUGwZgGtAGit/3O73eqIH9M0VRd5h8OBw+FQZQghhKg1Xdf5p3/6JwqFAn/4wx8YGBigubmZYDCIzWYjl8vR0dHBxz/+cSqVCps2bWLlypXj8uH16aef5o033qC5uRmXy4VhGIRCIW6++eZxP2Mpxg95p4xjGzZsYGBggIULFxIOh1V/PeCENgUAwWCQpqYmtmzZgqZp5HI5CoUCpVKJcrms/jufz1OtVlWZ1toFZ/W4snbCjV70rGmamg202Ww4HA5V6rXC4egWMMVikUOHDtHV1XV2XzQhhHgHkUiE7373u1xxxRX8z//8D0eOHMEwDHRdZ2hoiEWLFpFKpQiHw9jtdl555RUuvvjicbVZrbe3l/vvv59AIKDO1DUMg8997nPSQF+cFAl/41Q2m+Xpp5+msbERt9uN2+3G4/GMaUw6mjVDZ90QTNPE5XKh6zqmaVKtVlUbg3K5rEKfdZXLZYLBILt27VJf//bL+jrrstvtlMvlMX+3dcFbzUffqZ2CEELUgsvl4rLLLmPVqlUUCgVM06RYLPKNb3yDl19+GZfLxdy5c/F4PAwMDLB//37OOeecmozVMAyGhoYYHh4GoK6ujgceeIB0Ok1bWxsOh4N0Os15553HJZdcUpMxiolLwt84dfToUQYHB2lublazae8mRNntdrxeL01NTWiapoKaNftXKpX+4mWz2XC73eRyuTGhsFQqUSwWyefz6knZmvGzjn0bPcNohctSqXQWXikhhDg51n0SwOfzcffdd/PFL36R1157DYfDwfTp0/H7/bzxxhv4fD7a29vP2tjS6TQbNmxgw4YNxGIx7HY7TqcTu91OX18fDQ0Nqqefw+FgzZo10kBfnDQJf+NUb28vhmGMaUz6t8KUtenC2nhhs9nU91oHfI/+8+0fy+VyKsBZ4c3q4WcYhlrTB6i+fpVKhXw+TzabxTAMNc5yuSzrT4QQE0JHRwff+c53+Od//me1mS0ajeL1ennxxRe56qqrzkrLqqNHj/KDH/yAffv2ndBay263M23aNHXvTiaTfPKTn2TatGlnfFzi/Uf+dR6nrCbJuVyObDZLJpMhlUrh9/txOBwnzAKObqycTqcZGRnBNE3y+by6WbxT8Bsd/vL5PKlUSv18K/hZmzis3cWlUkkdkVStVlVwzOVy5PN5NVPY3NwsJV8hxISwePFi7rzzTv7zP/9TzaTV19dTrVZZv3491113HZqmkclkqFar6LqOz+c7bQ+5g4OD3H333fT09BAKhdTGPb/fj8fjUV0VrPut2+1m+fLlco8Vp0TC3zgViUQoFAokEgkSiQSxWEy1WKmrqzvhhmN1p4/FYgwMDLBnzx5Vjh19WRs+rBk6a+MHvFVuSCaTY0oKVrsXa9Gz1dTZ6vNnrSEsFArk83l1aZrGnDlzzvrrJoQQp0LTNK677jr279/PunXr0HWdzs5OfD4f8Xic+++/n56eHrLZrNrk1tjYyHnnnceFF174V2cGR2+gy+fz6kF5cHBQ3bNff/119u3bRygUQtd11VorFAoRCARwuVxUq1W19CaVSvHss8/ymc98Rqos4qTJO2acmj59Om63m4GBAdXI0zpVo1AoEAwGcTqd6kkwnU4Tj8fp6+vjyJEjHDp0CJfLhdPpJBgM4vf78fl8+P1+tVMsEAjg9/vVhpLHHnuM+++/X80GWqHPerK0jnJzuVxjdvtaJWIrUBqGwbRp05g6dWqNX0UhhHj3HA4Hd955J4cPH2br1q2q1Gqz2di5c6eacbPuvcePH+fYsWO8/PLLfOITn2DOnDmq2hKLxYjFYgwPDzMwMEA8Hicej5NOp1WlpVqtqntpqVRS5V2rtZbX6yUUCqmze03TJJ1OA2898O/evZvjx4/LvVacNAl/41R9fT2XXHIJv/vd7wgGg7hcLjRNo1QqkU6n1ZOg1VYlm80Sj8fp6elh2rRp/OM//qOauRsd4P6aT3/60zz99NP09PSoti3w56dWK+SNDoaAmv0bvbHkyiuvVAuqhRBioggEAnzrW9/i85//PDt27FB9UA3DwO/34/V61fnm1r0vkUionqyGYahNc9a912qPZd2TPR4PPp9PPUADxONxqtXqmLZaVl9V62Ql6/P5fJ50Ok0mk+G1115jypQpUv4VJ0XC3zhlt9v5zGc+w4svvsjBgwfVWrtcLkcikcDn840JhLlcjqGhIdLpNDfccMMpBa8pU6Zwxx138J3vfIdCoaAOPLdav1gbSUaHSuAdj3Vbvnz56X5JhBDirIhGo6xZs4Z/+7d/Q9M0fD4fwWBQVUy8Xi8Oh0NteHM4HGSzWVKpFG63m3A4fEJ/wNEtt4rF4gldGAYGBk5olwUn9li1/rT+/p6eHqrVKna7/ay+RmJik/A3jk2bNo1vfetbfPOb32Tv3r0YhkEikSASieD3+9F1Xc38pVIpisUit99+O42Njaf08zRNY/Xq1Rw6dIiHH34YwzDG9PgrlUrouq7Cn/WkOXpWcPbs2Xz5y1/G4/GczpdCCCHOmnw+z7p16wgEAmiahtPpVDNwdXV1BAIBdF2nXC6TyWTQNO0dQ93b22tZm+ysr7M6K+TzecrlMqFQSLXZGt1iK5fLqVnC0Wu2raPqhDhZEv7GMU3TuOSSS1i7di3f//732bNnD319fdTV1anwZ+22nTJlCl/5yleYOXPme/qZbrebr3/96+i6ziOPPEI2m1Vr/UYf32aFv9Gzgk1NTVx11VXSaV4IMaHt2bOHN998E5/Pp9bguVwuvF4vwWCQcDiM0+lU/VGtkOZwOBgeHqavr08Fvmq1qs5GDwaDtLS00NLSQmNjo9rN+9RTT/Hqq6+O6cBgGAaZTEZ1Vsjn88Bbx36m02m1oc/tdtf41RITkYS/cU7TNC644AIeeugh/vjHP7JhwwYOHTpEJpPB5/Mxe/ZsPvShD3HRRRedtjV2Xq+Xr3/965x//vn88Ic/ZMeOHWSz2TFtX0av93O5XCxcuJD58+dz5MgRkskkkUjktIxFCCHOti1btlAoFMaco26329F1XQU5a92ftfvWKsk6nU7OP/98Ojo61Fq9QqGgdvjG43ESiQRDQ0NjZgXtdrvqymD1TrWqO6VSSYW8QqFAJpMhm82Sz+eZPn36uDqCTkwMEv4miGAwyDXXXMNVV12lpvtHz8Kdbrquc8UVV7Bs2TI2bdrEiy++yO7duxkZGaFSqeD1etm3bx+RSIQFCxbQ2dmJ1+ulXC6zbds2LrnkElmALISYkI4fP67Kr6OXvow++cg63vLtX2eaJslkUi3FGb1OWtM0dd+2ZhKtTSGZTIb9+/erCov1cesUJafTCUC5XFanLQGcd955cq8VJ03C3wRj9d07W4LBIJdffjmXXnophUJBneWraRr/8A//wKZNmzAMA8MwCIfDeDwe9uzZw8KFC2X2TwgxIY0+GtPayGbN3qXTaWw2G06nU635s0qw1lo8l8uF2+0eE+6sXb2jN35Ya/1KpRJ+vx/TNNX6Pvhze61cLqea+1sfKxQKLF++XNq8iFMi4U+8Kzab7YRNHF/84hfZuHEjsViMVCpFfX09Xq+XUqnE1q1bWblypTyRCiEmnPb2dhXKRpdhrV6rxWJRbfjI5XKkUilV1i0Wi6oPoBXuRm/8GL3pw9r4Yc0YBoNBEokEwJidwVb/Pyv8lctlIpEIN954ozR4FqdE3jXilC1evJgVK1bw+uuvE41GyeVyhMNhvF4vhw8fZvHixYRCoVoPUwghTsrixYtxuVyq3Gp1OYC3yq7ZbBaHw0G1WlWbM3K5HIZhkM1m6e7uVsHRKhObpqmObKurq6O5uZnW1lba29tpaWkhGo1SrVa55557ePHFF0mlUmoWUdd1VfGpVCq0tLSwZMkS9uzZQ3t7u6z5EydNM625aCFOwaZNm7jpppvo7Oykq6uLmTNnUldXR7FYpL29nRUrVtR6iEIIcVIKhQJf+cpXeP755wmFQvj9ftXg2ePxqFOOTNM8oSxsmiahUIjm5mai0ai6WlpaVJsuj8ejAtvbqyOZTIZnnnmGxx57jAMHDqgycCAQIJfLUV9fz8qVK5k6dSqmaXLppZcybdq0GrxKYiKTmT/xnlizf5s3b6a9vZ1sNquOo+vp6SGVShEMBms9TCGEeNdcLhdf+tKX2Lx5M6lUCkAFvUKhMGYmzirD5vN5Fi5cyDe/+U2CwaBa63eyS1/8fj833HADV199Nb29vQwPD2OaJvX19Tz11FP8/Oc/Z2hoiNbWVkKhEK+++irNzc3SW1WcFJn5E++ZNft37rnnsmjRImbPnk1dXR2FQoGGhgY57UMIMeGYpskTTzzBN7/5TQA162eVga01eFZZd968eXzrW9+ipaXljI0pmUxyyy23kM/nufjii5kzZw52u522tjaWLl0qa6zFuyYLBcR7dt555/HBD36QY8eOMTw8rDrO+/1+hoeHSSQSas2LEEJMBJqmYRgG06dPJxwOk06nVY++eDxOLBZjZGSEcrnMFVdcwb/+67+e0eAHEAqF+MY3vsHQ0JDqqer1eunv72dgYOCM/mzx/iJlX/GeOZ1O7rjjDj7zmc8wMDBAe3s7zc3N+Hw+PB4PGzZsIBAI4HK5mDJlCu3t7bJDTQgxriWTSdatW8esWbNobW3l+PHj7Nu3j6amJjRNIxQKce6553LppZdyzjnnnLUWXEuXLuVjH/sY69ato729naamJsLhMNu3b6eurk71AxTir5F/gcVpsWTJEi6++GLefPNNpkyZQltbG5FIhFAoRFNTE3V1ddhsNgzDYMuWLXR2dspOYCHEuGSaJuvWraNcLtPc3EwgEKChoYFp06Zx1113qSPfarHL1m6384UvfIGNGzeyd+9e2traqKurw+PxsGXLFlpaWggGgwQCAXnIFn+RlH3FaaHrOmvWrFHnWsZiMUqlEvX19cycOVPteJs+fTqzZ8/m4MGDpNPpWg9bCCFOkEqlePrpp9VDLEA+n+eKK65Q6/5q2V6lsbGRr33taxw4cICDBw+STCYJBALk83l6eno4dOgQb7zxBt3d3bLcRrwjCX/itKhUKrzxxhs0NjZis9kIBoM0Nzerp2breCOHw0EoFGL27Nn09PRQqVRqPXQhhBjjhRdeoFqt0tLSgtfrJZ/P09TURFdXV62HpqxatYqVK1eyfft2uru7KRQKNDc3M2fOHObPn8/ChQvRdZ2DBw9KABQnkPAnTovu7m6ee+45LrroIlatWsWsWbNUyxf4cy8rq/WBz+cjEAgwODhYy2ELIcQYqVSK3//+93R0dFBfXw9ANpvl8ssvH1fr6XRd58Mf/jDDw8PEYjEKhQJ1dXXU1dXhcrlwuVy0trbi9/tJJpO1Hq4YZyT8idNi48aNGIahutFbB5dbva7eTtM0wuEwsVhMnkqFEOPGa6+9RqVSIRqNqlm/YDDIvHnzaj20McrlMuvXr+fcc88lGo3i8/nw+/3qAdu6Ghoa6OnpkfusGEPCn3jPTNNkz5492Gw2qtWqOqfyb7HZbKTTaSn9CiHGhVwux//+7/8ybdo0Ghoa0DSNTCbDqlWrcLvdtR7eGLlcjl27do3ZZfz2h21N09THyuVyLYYpxikJf+I9q1QqpFIpdcSRdRB6Pp//q0HQOhZJnkiFELVimiaZTIbDhw/z7LPPYhgG7e3t+P1+8vk8TqeTBQsW1HqYJ7DOGM7lcmSzWbLZLPl8fsz91DRNKpWKOmNYCIvsAxfvmXXuZCaTIZ1Ok06nSaVSJBKJMev+LNYNKZlMUi6XpSu9EKImcrkcL7zwAv39/USjUZxOJ0uWLFG9/BKJBBdffDEul6vWQz2B2+2moaGB4eFhhoeHGRwcZGBgALfbrcZrmibJZBLDMKTtixhD3g3iPbPZbHR1dfHb3/6WWCymOt8Hg0G8Xi8tLS14PB4V8srlMvF4nIGBAbU+UAghzqZcLsdDDz2E3W6nq6uLqVOnEolEME2TkZERDhw4gNvtHpezfgAej4cVK1bw4IMP0tPTQzgcxu12Uy6XaWxsxOFwkM1m6enpUZ0WhLDIu0GcFsuXLycSidDb26tm+6ydcfl8nkgkgsvlolqtks1mGR4epqenhw984AM1HrkQYrIxTZOnnnqKkZERli5dypQpU2hpaVHr53w+Hy0tLbS1tdHf38+MGTNqPOITaZrGDTfcwLPPPsuePXtwOp1qCU4kElHhL5VKcd1119V6uGKckfAnTospU6Zw0003cd9993HkyBF0XQegWCySTqcJBoM4nU5M0ySXyzE0NEQwGKStra3GIxdCTDaJRII//vGPLFy4kEgkQl1dHXa7fcwSFKfTSWtrK1u2bGHKlCnjcuYsGo1yzz338C//8i9s3bqVeDxOb28voVAITdPwer2sXr2aYDBY66GKcWb8vZvFhGSz2VizZg0HDx5k3bp1VKtVCoUC6XSaWCyG3+9H13UqlQqZTAaPx8M111xz1s7DFEIIy549e8jlcjidTtWe6p3WHuu6js1mo1gsjsvwBzB//nweeOAB1q1bx8svv0wmk8HtdrNs2TJWrlxJOByu9RDFODQ+381iQgoEAnz3u99lypQp/OpXv2Lnzp0MDg5SX1+Pz+dTbQcWLVrEjTfeSCAQqPWQhRCTUDweJ5/PUywWKRaLVCoVTNM8IQBanx/vbVIaGhr49Kc/zd///d9TLpdrdu6wmDgk/InTKhAIcNddd/HRj36U559/nh07dlAoFHC5XMybN48VK1bQ2dkpM35CiJrx+/1ks1ni8TgjIyPE43FaWlqAt9bSmaZJqVRiZGSEdDo9bmf93s5ms42rU0jE+DUx3tFiQrHZbHR2djJ79uwxPaesjvNCCFFLs2bNwjRNjh8/TlNTk6pChMNh7HY7xWKR4eFhDh06hKZpeDyeGo9YiNNLwp84YyTsCSHGo5aWFq644gp+//vfqz6kqVSKuro6dF2nUCgwNDREb28vV199tdzHxPuOhD8hhBCTis1m49Zbb+Xo0aNs2bKFTCZDf38/4XAYh8OBYRikUimuuuoqotForYcrxGmnmXK2lhBCiEkonU7z+OOPs379enK5HF6vF5/PxznnnMO1117LOeecI7N+4n1Jwp8QQohJyzRN1Xi+XC4TCoVU3z8h3q8k/AkhhBBCTCLSCEgIIYQQYhKR8CeEEEIIMYlI+BNCCCGEmEQk/AkhhBBCTCIS/oQQQgghJhEJf0IIIYQQk4iEPyGEEEKISUTCnxBCCCHEJCLhTwghhBBiEpHwJ4QQQggxiUj4E0IIIYSYRCT8CSGEEEJMIhL+hBBCCCEmEQl/QgghhBCTiIQ/IYQQQohJRMKfEEIIIcQkIuFPCCGEEGISkfAnhBBCCDGJSPgTQgghhJhEJPwJIYQQQkwiEv6EEEIIISYRCX9CCCGEEJOIhD8hhBBCiElEwp8QQgghxCQi4U8IIYQQYhKR8CeEEEIIMYn8f5V2Wx+25wRJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_molecule(input_string):\n",
    "    job = AMSJob.from_input(input_string)\n",
    "    return job.molecule[\"\"]\n",
    "\n",
    "\n",
    "mol1 = get_molecule(\n",
    "    \"\"\"\n",
    "System\n",
    "    Atoms\n",
    "        C      -0.75086900       1.37782400      -2.43303700\n",
    "        C      -0.05392100       2.51281000      -2.41769100\n",
    "        H      -1.78964800       1.33942600      -2.09651100\n",
    "        H      -0.30849400       0.43896500      -2.76734700\n",
    "        H      -0.49177100       3.45043100      -2.06789100\n",
    "        H       0.98633900       2.54913500      -2.74329400\n",
    "    End\n",
    "End\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "\n",
    "mol2 = get_molecule(\n",
    "    \"\"\"\n",
    "System\n",
    "    Atoms\n",
    "        C       0.14667300      -0.21503500       0.40053800\n",
    "        C       1.45297400      -0.07836900       0.12424400\n",
    "        C       2.23119700       1.15868100       0.12912100\n",
    "        C       1.78331500       2.39701500       0.38779700\n",
    "        H      -0.48348000       0.63110600       0.67664100\n",
    "        H      -0.33261900      -1.19332100       0.35411600\n",
    "        H       2.01546300      -0.97840100      -0.14506700\n",
    "        H       3.29046200       1.03872500      -0.12139700\n",
    "        H       2.45728900       3.25301000       0.35150400\n",
    "        H       0.74193400       2.60120700       0.64028800\n",
    "    End\n",
    "End\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "# Molecules could also be read from xyz files\n",
    "# mol1 = Molecule(\"ethene.xyz\")\n",
    "# mol2 = Molecule(\"butadiene.xyz\")\n",
    "\n",
    "plot_image_grid(\n",
    "    {\"1\": view(mol1, guess_bonds=True), \"2\": view(mol2, guess_bonds=True)}, show_labels=False\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439d8f35-16df-4d72-b84b-eb8f9e0057a7",
   "metadata": {},
   "source": [
    "## Setup and run the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd9005ee-f8d6-4eb9-8c5d-b36585748474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08.04|17:13:19] JOB plamsjob STARTED\n",
      "[08.04|17:13:19] JOB plamsjob RUNNING\n",
      "[08.04|17:13:19] JOB plamsjob/frag1 STARTED\n",
      "[08.04|17:13:19] JOB plamsjob/frag1 RUNNING\n",
      "[08.04|17:13:22] JOB plamsjob/frag1 FINISHED\n",
      "[08.04|17:13:22] JOB plamsjob/frag1 SUCCESSFUL\n",
      "[08.04|17:13:22] JOB plamsjob/frag2 STARTED\n",
      "[08.04|17:13:22] JOB plamsjob/frag2 RUNNING\n",
      "[08.04|17:13:26] JOB plamsjob/frag2 FINISHED\n",
      "[08.04|17:13:26] JOB plamsjob/frag2 SUCCESSFUL\n",
      "[08.04|17:13:26] JOB plamsjob/full STARTED\n",
      "[08.04|17:13:26] JOB plamsjob/full RUNNING\n",
      "[08.04|17:13:38] JOB plamsjob/full FINISHED\n",
      "[08.04|17:13:38] JOB plamsjob/full SUCCESSFUL\n",
      "[08.04|17:13:38] JOB plamsjob FINISHED\n",
      "[08.04|17:13:38] JOB plamsjob SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "common = Settings()  # common settings for all 3 jobs\n",
    "common.input.ams.Task = \"SinglePoint\"\n",
    "common.input.adf.basis.type = \"DZP\"\n",
    "common.input.adf.xc.gga = \"PBE\"\n",
    "common.input.adf.symmetry = \"NOSYM\"\n",
    "\n",
    "full = Settings()  # additional settings for full system calculation\n",
    "full.input.adf.etsnocv  # empty block\n",
    "full.input.adf.print = \"etslowdin\"\n",
    "\n",
    "job = ADFFragmentJob(fragment1=mol1, fragment2=mol2, settings=common, full_settings=full)\n",
    "results = job.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f165ff0-56e0-4c36-a3d7-10415e78e48d",
   "metadata": {},
   "source": [
    "## Energy Decomposition Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e339e4-15a7-47da-8b28-aaf24853c083",
   "metadata": {},
   "source": [
    "Energy terms contributing to the bond energy (with respect to the fragments):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76192a38-5adb-4e3f-9795-823cf6089867",
   "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>Term</th>\n",
       "      <th>Energy [eV]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electrostatic Energy</td>\n",
       "      <td>-0.159357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kinetic Energy</td>\n",
       "      <td>-0.295645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elstat Interaction</td>\n",
       "      <td>0.748751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XC Energy</td>\n",
       "      <td>-0.356182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E_int</td>\n",
       "      <td>-1.697991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E_Pauli</td>\n",
       "      <td>0.187116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E_elstat</td>\n",
       "      <td>-99.865788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E_orb</td>\n",
       "      <td>-2.454467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>E_1</td>\n",
       "      <td>-31.722875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>E_2</td>\n",
       "      <td>-56.338062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Total Bond Energy</td>\n",
       "      <td>-0.062434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Term  Energy [eV]\n",
       "0   Electrostatic Energy    -0.159357\n",
       "1         Kinetic Energy    -0.295645\n",
       "2     Elstat Interaction     0.748751\n",
       "3              XC Energy    -0.356182\n",
       "4                  E_int    -1.697991\n",
       "5                E_Pauli     0.187116\n",
       "6               E_elstat   -99.865788\n",
       "7                  E_orb    -2.454467\n",
       "8                    E_1   -31.722875\n",
       "9                    E_2   -56.338062\n",
       "10     Total Bond Energy    -0.062434"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "bond_energy = results.get_energy(unit=\"eV\")\n",
    "decom = results.get_energy_decomposition(unit=\"eV\")\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    [\n",
    "        {\"Term\": t, \"Energy [eV]\": e}\n",
    "        for t, e in list(decom.items()) + [(\"Total Bond Energy\", bond_energy)]\n",
    "    ]\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1ebfa29-8aa5-4068-bdbb-8d0338870a0a",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}