{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7fb27b941602401d91542211134fc71a",
   "metadata": {},
   "source": [
    "## Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acae54e37e7d407bbb7b55eff062a284",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scm.plams as plams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
   "metadata": {},
   "source": [
    "## Elements, coordinates, lattice vectors, and charge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd0d8092fe74a7c96281538738b07e2",
   "metadata": {},
   "source": [
    "### Manual system definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72eea5119410473aa328ad9291626812",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scm.base import ChemicalSystem\n",
    "\n",
    "system = ChemicalSystem()\n",
    "system.add_atom(\"O\", coords=(0, 0, 0))\n",
    "system.add_atom(\"H\", coords=(0.172111370, 0.975304070, 0))\n",
    "system.add_atom(\"H\", coords=(-0.987455810, -0.075969620, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8edb47106e1a46a883d545849b8ab81b",
   "metadata": {},
   "source": [
    "To see the input that will be passed to AMS, create an AMSJob and print the input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10185d26023b46108eb7d9f57d49d2b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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19Vp0KC3SM8Dw0Y4AcU5E6hxgxqkqYb5yGu855H8AFNMcJe1RRWMADgFDc7h44QaQrF5nLrc6Nj6+6bN/+7cH5JhrQuzMb7jgHToVGk2sU+He2XctrkI2M0nmSaAvkP9hs1CFBsm1h5tYCA/gMICIChgtDVKvZw0WIE5M/AgWbWhtuECy50LUID03ZKe1wch842Bf38pals02/6NSSdv8jxwkhf+Rm1fYTphXWnWYAA6ZV8TvndbWnoHKowY5A0zuoVugQVgLcrlmsFL5H03MqxAgAMOigIGJ5dpD1xQaRB91VA7qY6uJ8fHvUN3ORx7pOd+DdhOiBmnxIf6GHMiyFwMKNIiZV/geaI5cYKA8WW0GK9cgel3eMq+kQaRFKgdGRg7vOnBgE9U+rP9jh/ZiiADpxVE7fW22VYlSI5f15Q46U7y8+3An3Wew2hx0tYep3dwHaWrFVUVrrXb864MPbqept8v6On1NPr01RxPr9PK312pHkPmi8BKbwapWzf/oyzWIO+eAAy1SOOiAg2nelpPODBYfUz288emnD2fadKFn1Yf6GAEiJsRgHDBZGEiSpZrBWsn7D70pr7T5HwBFRc3EEkgwsZjaDbSHOejaliQZr9cfodYNPS5j0cRiFGOAA+agH65Wf0waYwAHXeZVJdQeDg4HiGkQaQ4C5hWzV4DjMH/NNj7+A/I3bNgA6dkQNUjPDt2UN7w1g5Vll/KNh81giZaddMDh/odrEH2Hm+gLKJvi1QxWdf+hQ9nBsbHv08JV553XyxZWnMWacjHr9Qqz7CqmdqcphuYV2sM0CKaV0v4GHVSxxERbsaNBMmmSdOTw4b36z4LHYcWau+/uWQed9tPnGCIH4IDZShL4KwGHYspqXTexitkrFTQN4m/QBRi+LMQxFzia8kdYYjK0Y/78vb28xMRFIppYzolzm6IIskVJMlNfAF6YA+SIg66ThQZRunj/IWCELwjlf2QARUveH+W7j7vXru15+Yoa5NwGhvfeALKvv3+JPpJaaDNYTPFq1so1iJlXKm1TvJhZaBCmd5WXv/8wJ51tgBQfpOJFO3ZQb0+Hnkd4T3O/expvcmAfSaVpnzRIowBG/gbdnHO1t5jByqd40SDmoLdmsPA/ktHx8R/StQ389HiIGqTHB3Aqm89HUphX0iBZ4X/oBoV5JVD4DJatwdI5+RnFFK92K6nsHx1tCiSP0q5HenwGiz5EgMCFGFpTsf6RVD6DVWgR8cc1SOh/YD8F2oO/mUoPj49ve+LQoadh6XrNYPW6jRVNLEYyBqZiNVGVtmawWOIeLC8p/A8KKZr/kTvoZmLlM1iAxT6S+rd/O2AzWFIwvc7aCJBeH8FTbz8P+dZHUklywTRN7doSE3fQdRJQ6PuQI9O7SuOgt81glT+SuuYaLuv5EE2snh/CU+4AD8nWR1LN5uzc/2hpEJ0o/A+l3f8wDSIA4X/kCxRtBovtgBS/d8ot6qIKogbposHoUFNaboJ/JFWtNsPlJWXzCpC0fSTFLJamdu0jKa3Bmmg2v0M/dp4FDjr9iBoELsTAdFTrI6lKJcP/MB9EfCk0iIDhDrppEJ0rdjGRDyL/o3JAH4HsGhnZBDt7+SMp2u8hAsQ5ce5SWyslB/0yNId8kLQfB138IOJIoDWcFg668lhikr8kZIlJRTNYO/51eLjnP5IKRSGaWCE3zqG0ppfSNZL9WzUp9WuJll7pI6nBHCBlDeLgMPPKTSyZVv4GXX6I/kha/zhVr58VH0mFYhA1SMiNszwNGNTFym1yyuV4CCPF9p9zZGKtZNd2fV5r34DwWWGfwMDsVTGDpTQaxGavdN4BYt+BtNZgtT6S2rCB+5hmEu3pEAHS08N3Qo1PV0tTbGiBAqFtrtPPmuXL5y/PshfOajZX7W82X/XQyIj2aOjL5snWAhwiSaqvCu1/PgQcVvOaecX0LkFgASAODplX/CVC6yOpVomz4jcC5KwYxkk7YcDYqG0/idIYyTtXrFg1r15/w8w0fe1Ao3HFrEplwRy9NR9UXK2/MpDXnTJt2zhwIEm1pqpPW/cMzpqVVHQOwFT879VU3j6zFUgwrbTEpCoHnX2wDCC9/pFUyE34FsNZxgF8C+2jbo/6n7344jnLx8Zu0G6JN87Mspcukgk1XYLdryhNkekb9AZ/c6MnZZXdpS3qHH6HaQzRVBqkIm3Sp/8CGRBgpinWlM70fyEj2lL0gD6S+sH+/bvf1t+/bP0//AObUydr8/uT7uUQAdLLo3d02yvyM5J10gU/KWBcfPjwzXPT9H2LtI3oLAn9NMXp0iYCRTpN8p9T28YEYGBaYVJ49I+kfHqXDRrs60HVkwk0+rdOoas/q0uz7EmSZw8eOPC2N27d+o+qAgeH/X1ze4yc3gwRIL05bke1OtQar1669MYFaXr7+dqdBGBIY9QFjMqggEEUMBIBxKJrDcDRBhCZVDZ7pXzTJqIIi/kmovYWXXWzYZxeDtru1KMyvUay7M6dlcptPz809LhAgrOe6TolezNEgPTmuLW1erWe5RulGX588eILl1Srn1hcqbx+ngRXJlV9huRbEWAkxBAcgMQ1h7/7CEHiGsQBgrQXAqP6Ceb1i2JX6Yv0pt6jVHY2m3v3J8lvv3R4+H9SBqDoup6c1Sr6S0di6D0OuOZ48dKlb1qcJJ9eUqks1sxUXT5HZZaiNAdm1THBATAcJPb+Q2XNxMo1CGmAAVgQFsytUGhMkygfTQJIJgRK2VU1/JatjcZd3+7vf/dNTz+9VyDpSZMr7Ku6F0MPcYAXfRWc8RcvWfK+8yqVjwsgyewsawgYVQHEgBGCo0175MAwE0tpNEcBEB23gUTHrj0Kgck1CNO9RNMkovroKhkXasbVLr2Zr23Nsh89KY32us2bn7hPwHllS9n0DJuL/vZMi2ND4UABjhctXfrx5ZXK+xbpXfZs5StWAIfMKtMaoWlVAMTBEVDAAVjYg5e0A4TjYwoJ4CBAg4g2EUiSMWkT+Sy1PUmy/ZkkedvqLVvu7TWQHLPvrZ7H327kwGrJ8UY9iX9syZI/WVGtvmehTKo50hoARNrDwOGaw/2OEBzmoAMIospDDSA5NYAojVlloUzJBBBOSRN5y+5pUS1ASQ5Lo+lvoqt7smx0S5q+/uotW+5br7au7ZEZrgiQ1jD3zO/qHBwrli37DW2i+4eLm82JObKOFLVnTysCDgcIwPA4TYLu4CBtAClRwMGixYqopqxaUXVY2ikgIARgaAMIQMnBgjY5zEotgWR3lu0aqtdf+jM7djypGnrCcY8AaQ11r/wyodRYvHTpWvkcdy0ROOYKMAKHaQ43rRwcbdpDwm5aRNTB4ZQNGgqwODhEC4A4UKAeQpC45nBglCiO/KhAorng6tZm87HdM2b81N8//vjB2wQx1ZijzSvuLhr0uLsa9jxboz9cbfGb+XqFrmb+SfYRP7m5aPnyS2Y2mw8uk7KYLxGeG4DDHXP3OwqAAApdPAANIqDIP7FtgUagYF9eFi0aOJyqXAGWsNHw2mMICtZrhcdKszReIKlrFrj2RKNxzxVbt75RV3f9zBa+WE8GASHV7n2Vhx9+OF21alW2Zs0ambopL6yK/niZRYsWpTt37szWrl0r3d+zoKFjaV+j8b+0fmr2gJ7IEvqKzT7pBNRf7DGoni7eZYgvmE12nFP+GAdA2B/k5GkDB8DwCD9JQ4lhcHBAAYQDg3Kkg/JcKQ1WAyQXVqtv+M6SJTenW7d+pttB0msASQWEiqJ4n2oE2pcyXHLJJdNe/epXY2Ekn/rUp/apDICgXBEEmuptt92WrVu3jnO9Ehinukyr6/XNxupBCZnAUXNwtAFCBSXOrbffElDSAMPBUaQl9JOCQ2/DC3B4GkF3kKg+E3xAQQAYDhDKODC4hrQHleHevEjUDFdzfpre8e3zz//rZNu23beq9nWqyYt2EwXYPREEiir7vXpjb7rppkXLli17yYwZM14xMDBwmeLyWq12nuLcqgZW47BdANmn8o+JPjgyMvLv999//3e+8IUvHKaO9evXV6V9egEojFG6ePFiLbqtfk+CdeFCoXyehGq2TsxSZEqXGPoeZl5JIM1BRzBJtwS0oJhXA+IVlB3dDQTQMI3Qe9T1oVY4yrwCEB61KtjSTj1fgBoTwDUmtcfr9c+s2rbtV7pZi8D8rg636i+8br/9dk2G2BOr+p73vOe6BQsWvHXWrFnXii6ao6XYg4ODybRp07R2rqaxrVrUtw2ABE2jh1wz2bdvX6JPpjeNjY39zZ49e774y7/8y9+h4wCly00vrKXGkiVLbtGuh3cskGm1QCI8R5mAY7KZqzI4AIZHA4X4UlDxa0DHfPdhwChTB4dTQOKBMSGiRYgOAkDhkbwwrWPGA6f9gAZnS5L89E9t2fLAet29G6d+g956r7uHrl69urZx40ZxO0nf9a53vX3hwoXv15P0KgHDQCGt0RQ4mgJHCkAEilQA0Xc+BhT8EYuAS/vOImjWX/kjdWmUu3bv3v0H73jHOx6ixwCxC80uLKRMmnKZnhA/lJYYXKQ+zFUMAYLmQIO0OecSZAeKaQ8JuIFE1MFh2kOA4JiPo9oA4scAA9BAiSFAdE8DBiBxcISA0G4nBThKIJEWsVktOewb5bBfoxq6ctq3WwGSSmBTBPbNb37zVXp6fnz58uVXAwyZVI2ZM2cm06dPryjqv8IGTHv0a8k1WgNwENEeRA+2A4d+9HFPU3s3me+1ffv2xv79+3//85///O0y30ZzbaJHXtcE8z2WLl16h/pyC2usFramdZPjmldoDHXBtUZBxQ+0hcUcGHwsxT/aFuAgHR4DjjJAQpCgOUKAOBCgDhCop3Mg8aHVIb1EPKTapUWulRbpyheI3QgQm6rl6f+Wt7zl1yUcH1mxYoW+05nVkDmVChwVgQSAJIDjuQCCiUXItQiahO35M5laTUWtvcvSzZs3P7Rp06YbZHb96L777qu98pWvRGt1OljD9XAYlBr8QV+SrJinbszXkzbUHmgOYuh/AAjXHpOCA5BI6D3yQDEQhOAI0yFAeOg4QAAGMTSxHCAOipD6OajGQS8Q65p6rD3WbP71VcPD13WjmSXOdFVwcPRfd911f3ThhRe+W2ZVkoOjKooGMXDgd7jvgXnlGsS1iPsfYe8cJNIiaJ6qfBL8krruc5XqeOAv//Ivf0HguLdLQIL6a2iAflpCtFKMaeaGjtmJoMdj2EfShiwXYsuwnFYx8vPIw4NVt3YM9ehao0z9fFh3qEE8vwwcBxCUMtSjMtolpar1WpnA/ZqvLFu2/HVbtgzdqrPrBLlWYzv/yyB0SzCzSoM28HM/93NfXrly5bvnzZs3IVBkilUHBprDIwDxtFM0iptYJgAIQR4BDQDiOuoDcLNnz64JHDjBcy+66KJ7ZGZd+0ppEMytDjPGpFpLNJjTzjRQAKRtVS0FPE7aVoSRSAjSzg/ySBfAcACEtAwSPw61Dnl+HKbDvLDOvC16J0Pjmgv0FbDWk72OZl7T6iLJrghdo0HkkFflc9SvvfbaT0lQXyXBnZA51Zf7G+6UF2aVm1dOEfyTCcx2cS1UwGGsmnqhqFcMzXv+/M//fLVmtv5jin2SVMaIhbV6l0NijeLDpX9hYsODhy+/nKLNO++8c9qhQ4d+XiZMivbIRb2lIahAgYJer2VI5ibL83OAQibbEcCU06EgexqhJ/qxX+Oagnx8Cw9udpHHdVC/FppfDz40aiw3YRXxdSKfvqaLtAfd6QqA+GzVS17ykjvkc9wIOPSE1x4BLT8DE8qBQNojeWgDs6HpzUkGBgjTDM2iwE/j/PPPH5TZtf6Tn/zkS/XuZdcxZrdOWNjXrF9v5oLuZYJgTczf59xtB8f+0eTVVWobM1hcawBBmCYLDgqnlPG0UfUV6mFSkLjwhsI8WToXcKvLwRCCxa85Fjg4L3PL+qL1WfoSEUH8yfX6jj598sl9aid3CJvrzT7jtOMAyV8A1i+77LLXnHfeebfIEa9L6PsQfADgYECQiX7sgHm+4Ag5TR3cT3JYlROPT3Khntwfl1Bf/7GPfWyaQNJY9cgj2fMSdoZa4fJFi2ZedP75fXMnJgbmzJixcqb2oJLgr9Cb8QVSX1XtL3XldGkyrVlaoGfuipnaI/fJkZEfu3PXLsTpCLiChpvg6/ikKEDJ2wRtAwp1c+5Y0QXfz1MeYDgl7WUmo1xHvl8vypE+ssoG03TRwrGxy1TVv+vBoez2FRB2jw78dBog6eUtc2Jg7ty5f8w0rrRGirACBAcF5lMYw/yp4hn1c0/duyYnvq6n99r1f/VXn1v7C7/wD8U9GFgFE/ZLL+2bK39pzsDAypna2UOm2YrBWg3hBmRXTq/V5INmC7Qr4QotCpSL3VwpM2JADO/T8Uz//oJ3FGzpabsXqu6qnqws7BNAkn693Gzs3Mn6qdaNdf5EwYDasrK6NLzG851yrghHbtPK4jiM5JaPy3mcL4Gg7Rq/Pr+Orik2Zmg2q69ev0rZ/75Id+F0N4SOAkTag5dzjUsvvfQ3NVt1qcBRl5DWAAACy5M9BEaYpsxUB+oEJNoArTJr9mxNsYx98pbrr/+K0v1pvT5fj7UVWhreEvZKpSXs1WpL2CUUvHATQMxuhSLsgoZ9x20mhY75plvpjD2nBIymaGbfeXNOUU9kiUvDVt++YNasyuxaLT3ItKgEKRT0Y6VDwSddjlznZfycso4OCHI5TJbnZTjnkTwvO1leeD6/Hn5pRu2K/LBrSCcBUmFtlXyNxQLGLaJNCWjVweEAwYkGKB7Jp0zuN0wpI6mTuvunTascllDOHxi46OWXX/7eaTL1bBM1hJg7ivIfGW3Crtw2YW8BIaUM10orAK3WdXK6DTjNZpV6Jovsi6tSBrgyGFywp4pS/1FwoP3Higg958phsjwv44Dx45x6LfRFtS4le2dL+eUlOks6BhA55hUtI2nKrPpFmVcz5FNo/7F+4eDIeirAkc8yGXWQQE9XoG7eLPcJKHM1xTxzwYJGlfvp3YkJu4SArf5N2JWW8Gv+y8DSEnblhQLvwArzymkHnOcz64PZxWrbOXog7NbLNnwFBwRC5ennpKoHoHk55ppIG9U5NlmQKWia7piA0Pm2c+pvoSHCcwzKsY45VwrWD7VNFD+EnVEuoIhm92hiV4TTJ2nP0T2BgzHiHcTN0iCZBFO7Wx4BB09zjw4Sjj39HNU/79Pcg3YQZ8gfqsyaVdMCL/tPPtcALsiT0bKwU6bIk+C3HXMuzAvS7H0LOM6T9vqR9shFICVAR4RbPUSKJoswNgSCH1ue6kAYLVIuT9O3QrhpV/lY9y9AIR7ZeV1vIe8j/bQYXkua4Hmto1aWfr39lNIdumEFQ9DCzk3zYnI25s+f/5PSHJfkAinZbIEid9w0Hu0v+cLzbb2YwgPuycpW7jUgLTKgF4o4nQYGCW0h7BKGNmEPzhVlPC+nll/O070KkCB4CJnKoDEkMCbomGOhwLtQhYIfpsPznjZw5fU4KJheJW3TrKLWH1ETZhd2qIMDSqBMmOY4LB+m/ZzXC82DtU3HRRsNI362O2inNIhxWibVmwQQNId9ihkCopyGXSa8CNFpDnzYw73wd6Yz/cuxCzaD72mnQV4h7JyjrVCEycuQJo9AmuBCkwsR96MEmutiLcy8XzNZiBV55YhwhXnlYzunevUBhu9+2KK6DmCEEb+nTdC97SpbBMrQ7rDtfh3lQ3B42s9Dg4i6CNtOuttCpwDCOGLGvBjtgdnkgHAGYRd3OqBJ8EWsJS7sDDrC4cLjwu6NdQGgvNIIuwtT2CNLcz4vz9QuwgUlsmXOdF3LhgoEyocCFabbhEz1aXlKS/CUdmAwjRxqDgeGdkK02TZm3IgV75e3u9w/lTHgW6vyH/I8Ut6j50F1nzAWbcn7RX8UQxaFd+hYuhMA4bEpjrEaO12FKSMhMSlAWCaLLFUP8083t7iXB4DrRybs+QnLywfdytPGXCCMSkjsPzRyYfE0fzqDABXHx0jrX2OTfgHUpj91T+7noHAKMMK0H5NnAFDbDRgcK235SjMBAAAtj3yNgTagNoDwXsaEnXaTDgP9BbD008/lPCiu4bq8j8cDireFdtLuCcXxzpn8YS/b0p0AiDVAs1f6ejSdn7dGZvYRcDggoJ7mfHiM4J6OYPfJ70V6QgPO086EehLh9vyjBF5l2/LKxwBDeXwXEZazY+XrXaX9xcD50mAO0kKo1PFjpRE2j2EZAwX9USynmWnDrER7QLUxRIu1zmP6TwQcRM/3AQAwnOe6cuQcedA8AgraQaSNopnmBZPxNH2GKs/1N+nisJZx12orRGeKN3rg6p+NxLww8t1GOXKePAT3dAHE2sA9FA9LEGr672/yEOZCkI+R5t+ZwjKh8IfnPN/ziuP8vuTXlZbAJPt0f38UILY8aUPBP16ap59pElG9jbc04HCQAAzyDRxOdV/uVzw5VV428BGAqJwBBErgPFF1WdT1k4KE8yrHZIGDw6iqoE8W03QTVcY36XBB7OIHgXfBLwMiPOaJ6pF8TLPTEaib+zQ0oBP63719Bw8aIE2YcwC4YAMGT/uTvxB2L6syoabgfFGGc/lxOR+AHJAwYSKx0yEPBRjmAHE6KUBUFvPJgADVdQYMUaZzHSSk7T2NyiDuucgr1QoGEpUxALjmcOqFoJSZDCDiQQgWxnk8jw4O9vCVaZUeVjWjjcZDVNdNoXhQnOlGSQOgOQwgJpASCBdOP3ZA6AtAE1qoR5z7qdYiCKHdk7boXvqYJxnVOwh9o1sIchsoJACFsAeAKIQ9P1+UmQwM4oGXN5ofwwtmlcZVBxtIe0DYJVC2FIW0xz6lAYuBQRTfhejHCKRpCqgiU7o8YgwYDpCcKtvAaH/TJkAwzVyYVrTFI/lWOACI6g1B4ekCHLrGQOFU14+p9r0yJMar1f+kup35w5N0p0PHAKKZKz3IxCwJH0Lpgj8ZKMLzlNenssUaralkoLfB6biEdBcLBnW/4klfBkIOguJ8CQSTCX+RF4JDaUCBIHEeDcbTfZv6ewQeLRAAECKr0VyDAAQGk2h54u2EBLnQHEqbxtD5kCL8gCW8B2LP2DRlWmESAVRMsTaQqIwFnTMNAlU9FtV+p/CFvqA5mAgwDaKyPHzy2FTpyv5mc2h/tfoYdZ7rb9LhP1O72yXsoxLGQdFMNHXBLFNA4dHB4dPDU2VqIZx+D6MChQFEGmRs7157omJOFcKt8kWafA1+cYxQhOd1rjj2dKu8tj7P/SpVQB2UU57lMxsms4Od16sSekTU2gE4AAQUzeHAcOraAw1BGlqAIk9TVxkYyrJg4JAA28tFaRAcaLQYOzCa1rGWeGlRwEFUez06MDAViUx2GEBUzjWINAd9YD/S9HCj8Y33Dw2NrleT1Tbcra4I8PRMBwOIBH2HTKS9EsZBhB6hBBhtQnoMYDg4eH/C0vhTNbUQTL5Ppx1hZFB3CyB79aKOe/Cf4PZ0V76DIRB8yUQLGEq0C3teHjCozlRUE0YmOKlNtypfx1UEyAQK2ipr2qChD8fCPgIOhAtweAQsDKaDw4AhYTQnXNTMK503YOjYqI4LwSat0AYOlbM37WqLAUQ8oD6rU2W9LgcI1wJqA7qo9VHU+3iUBlEdo6qGeKhS+T/c/+FWtSS7InQKIBVtuXNI34E/JlAskXCyw4hWl7eAEoKEPF/ZGwIDzeFCw8dTz1eTMIiTgePw6KgBdq/uv2l4uFnTB04SXgMBmoTrJrSAUfSIsLeEviXsedqfnoXgk39EaLQ9VHNEx3r0Jk9qxomPqHZL4DajMXT4Q+UfXDJ9+m9LwC/QMQ+XCj8ABBPLAcJAAhIHiGkNHZu/IaE2U0rHDgyoBW4tXiLY1Mu7HhqD9rCXecq3vXsdHDr3nADRtQ4S+moPBuWZYy5qppXqGVV/1I/qjkZjz65q9V+Uldwm7bGORJeETgCErjN+WmSa/psY+TPaxC1DSD3yTYYtO9c7AEDhAEFjEENwIDMMBtdQ1kHzXPzlOjSWg9Lv7XREABmTBtuvMt998slKH9+JKC1QtBx53dMdehP4RuOIsGfZk8o7IuyaxpbgPaKGjqhdxEcVkdHtivsUkc29ipMG9Xeh+nWH2ozpAe/MxHItcpTmyAsVIMmPuaE53FDFIqgvAINGsPLXgCHQGNVxTecBBVoETeRmW1ifayKABW8BBeNiAIHXSltU2v0PaY6GzKvagWbz/64bGtpzq5Sg6sSF6prQKYAwFgj7vXoCf2B0dLSieBRAHBgIPqDw6NxzcNjTXMILSLjGQeTlnJbLu0kHSIgFOEZGEtqjz271mO5vPqx9s7TkfZs0x5AGnCf7I6rz5IRdglEOCFiQ6zILNRDklAf6FyVsHxCdocgl9k08U6NoEAYxBMlRwNC97cERUF1iFVEZkZuYeSQA2PJ4lWXJCtPFbeaVynn9NNQaTd/yaDxWGnC4BrJ3McorNIiuEzgStb+yW3tjHahU/liHBJrRVaFTAIER+kiv/nUN3DMyqVYcPHiQbUQrrj0cHC7sZXDARRsEmSwOEEwzv85B5RqFsl4+nCkraxGAIY0GQBoHDhyoqE3f0tPwpXXVbUJwjOFDUBC0PJjcKA11YfdzUCuKWHm6RENBqW3RflEyRz8jXvym+sAT1saNBILmZpVTF2CoN8TaLmE3DSLqfbEGINDKM/NK1NZyiTowDHw6Nv9DdXr91G1R1wMM6iwAQp2KgMQ0rCgAwQxEg+jpUp+Q9tjTaKz/3aGh761XN9Z2kXOuZlroFEAYl9rWrVtHtIvJXyh9iwAiX3T6UQBxYEDD4MIOOBD40CQDHA4sBwgDR6S8XwM4yloEcKA5tCVptnfvXv5X5M+4Tp8H9+sLyFBwvTn0BUmHWrpEJ7uGS040cH1FftbvScv9otILFC2PChA4NImbPUcJr86FgixVYnwwkwqe6DyRCokINN/LAxIDiPKgZXA4+BwgKtaqlzoVHRwGEB2jRWzmTVSgzhTTPXK5nq3VPqj7pwJHV4ZOAQRmMB6S3/RTYuivafBn6ImdSYOkrgVcyAGHC7pdWNIGCDkA4Toi1/m1x7sOYIUAwcQCIGgztaX6zDPPDD/11FN3qX20k/F1ANCMMxXgU1Vbo26XFvkd9evTuRaxJwYnywApAKFzpMOA8B4LHAi1+SC6gDf4DhDzP5RX9j+sASpn96BelbH6qUdpwFEARMdM7+I3CRwNtMfORuNPf2/Llod/Sv27uwu1h5raUtUkOhBs4DEftIPIp3X/90sobdMGB0gZGDAfzeExNJUcIGgPN6/8ekBSDBy2cUmLYJq5/5Frj6Z2fq9Je/zWV7/61X3aRI4HdCfn5rl3VRr3M9K4b1W/XiMeWB7j5qZWWXtwrhwKIYYnOumgsCe+jqm0AIf4bdpD1Mw3XeP3gDowHITGY+XbOOka6jKAiLp5JTA3x/ReZ1uz+dTTM2b891uFuzWth6VKdV/opAaBG4CED6Y+qCf52yS458usaepYMtDSGqEGCAESmkmheRVqEK6lHg8AizocIGgP1yCB9sD3qD399NP/9KUvfekv8n27GOtOB+SZaeVfFf1PxZmKlidqphaCGkbyPVCwLYoPML9wyJU200r5CEURdVyYV0qXAWLgUD7B6lcaCjCo3xdLmu+hYwCyX9XvzLJfvfPxx/cLHLwYpGhXhk4DBF5WJIx7pUVuVvoeCWrj2WefLQAC1+zJBMMnefo7ONy8Qnscy8RygJUBwgxWPmuFacVu7/tlXv2qygtjxSaKNKWTASGq6v3RU9IiN6tdmH6YfYyhySnmiwNEybZgwqscqAHDqfgafmBlwNCDxbSIypjmUJkCJMrjHq5BfNpYWQVAvP5Ce+gcbRM4JsYqlb7hev1Dn9q27Z9uVdvXddm0Lv0IQ6cBQltMq8vU+rIG/kMS7ltk5owLIf2cPBY43H8IAQI4ThQgfj3mFQDRPTP5HpkEsDo0NPR2bSrxVBeYVrAgDMar4eHh9fJHLhGv7tBDA5Aw22sBf4QQAsJB4bQQYJVzM8iojs3vECAQDMBhFE0MSHTswHCqAVJuK/g9Q3ONxhHld0zonUffkP7h9tPbtv1OPmuFddjVwZ48XdJCxoIdDe/S03GtnvK2ebW2BGIH9sQ3sdZMly0vYTqYN+jHAgimFVF1Fd1zDRL6LoBDjnkTgOzatQtwvHfDhg2f9P2Ci4u7K2G8Ekj+RCB5TxkkNHVAcXpOSXvkqTNNEUoEWWGk4iKKd6ZBlAcgQoDAVY9Kth5koiH4kP7ctJqQU963o9m896lG402v2L59dN0RDHN51wZ40S2Bh1hFwvtL0gL6b5fqm5j6VWApB+9MzF9wnwFw8PQ/UYC4JlJd5oNQD+DApBM4qnLIEznB7/3GN77R7eBgvOAVTvt7BRJMSkCCPCLD9kRAk7iwhtQ1hVMuCmMBDirLNUkIDtcc0DAU2kOZ1E2daA4BpADHo5XKGx4YHh795xbejqgelevWYMzsosbRHmOczK2PSwO8D4EWYOr8jwf/54EmYYEiACGWXyy6ieUaxPvm2oP6cpCxvKUh7VHbtm3bIQHkXd/97nfv6nLN4d2BwivktCGte4soS1FEWuAhQUDgB/OI5gijaxCnlEWbQEGaU9cc3MwBws1deEJwAEbAIWDgIDXlc1R3Npv/+PUsewvvvW5VFeta2FWp7g/ex25qKW2ygRdIfknpj8pMmqvBbwoQ/JkOf8GWYmqVARLOYLl5hYkVag+Bo0kUOGpyyBOZVf8qm/598oH+s4fA4eMV8up69fXPdGKG+ouMFtoEZgIMN7McJJOZWYAijA4OrywEiIpaACCupXJtVNfnwrVDyt+bZf/jy8PD71eSYlxO0Z4J3QgQZx5j0tCfd16iAb9D6bWcQOClNeoCCCDhDXMamlkyN8ovCXW50IWhrm9OZJZVeRmo6eQ9Mq0++Oijj/6Rqs16EBywwwMyjf/24+rqJ/RwuFrd5RzWDny0QKEQJA4Q1yCuPRwgXOgRyQ7BgeAg8YQAHDZjIGe88myzuXckSd69YXj4LpVzT9AvaV3YA7/dDBDYZyAhIVv7NQLHzUq+WVRT9nIgBQaBg7fvDUwtf8GImcV5CQv94+8IbDmKtAYO+ZOa0v3Sjh07/lTHz+g8ZYg99WRTe8vBQKLMVLz6LfX/A5LTOTlQCo1CRwGCaxEox4CECkKQlMERAkRFfaYsE+MARqrp4soh5Suu352mv/PI0NDja8T/u1u87Tlw0Ef41e2BcSGYAOsfoF6EAy/hf63iFRIE/cXdkZW+nm5dosdqXV9iNBrPSFC+JnqP/I2v6JwebknS41rDuxhSeIUgZuLTBeLF7Uq/TVQr1o19aBQK8Po0BRAAA5BAQ3BwrgwQf5LoFPdo6of19zVW/DIpcCjLvnU4y275/tat8sPt09muXUJC+04k9AJAvB+MF8EGmYTMr/kCyQs0+FeI6n9p0iuUjTA8JaHYpbw9Mqu+r2UjTyufMbSQAwOJManJs88mUmhe+XFXqWM3ijdvV1xEJ1nWLuFGwJsSgBRUCRAVAAJQAEcJIOzsByiIvDmtsp7LgKElL3LI79XykY89uWXLP+p8/VbVtU4JhZ7nby8BpMXylinMmAIUBuxEgwOMQTuZ6060/m4rx9g6nxJplEXSvGjd65X/EsCiaG1WXosholzgEYZZOi9HeZin5esNLVH5pgbgq3qje9f2zZsftor0s6ZlUhUPMc/vVdqLAAl57UJwrH4AhDCG154raWQc/hRCm4PlJcq7SuB4segFihcp7Q8RHebqQqAQA7ergq3Kekh+xrf1fcy3dm/d+kMr1CrHcvVKL/sa3pcyPZZglcvF497nAGMNWHhgHGX6XKx/mJW/xvkiSGMIM1mqdWn7lFm+Jl0tbbGxlV8+V9TR64kIkF4fwefXfgcL1AEDPV4waysvACDOWlCETIgACblxbqefSxaeC0DnNvdi7yMHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgciByIHIgcm58D/BzZe+2pIwagtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(system, guess_bonds=True, width=200, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8763a12b2bbd4a93a75aff182afb95dc",
   "metadata": {},
   "source": [
    "### Lattice vectors: 1D-periodic\n",
    "\n",
    "For periodic systems in 1 dimension, the lattice vector must be along the x direction (with 0 components along y and z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7623eae2785240b9bd12b16a66d81610",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "   Lattice\n",
      "      10 0 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=600x300>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scm.base import Lattice\n",
    "\n",
    "system.lattice = Lattice([[10, 0, 0]])\n",
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(\n",
    "    system,\n",
    "    guess_bonds=True,\n",
    "    show_lattice_vectors=True,\n",
    "    show_unit_cell_edges=False,\n",
    "    padding=-3,\n",
    "    width=600,\n",
    "    height=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cdc8c89c7104fffa095e18ddfef8986",
   "metadata": {},
   "source": [
    "### Lattice vectors: 2D-periodic\n",
    "\n",
    "For 2 dimensions, the two lattice vectors must lie in the xy plane (with 0 component along z)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b118ea5561624da68c537baed56e602f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "   Lattice\n",
      "      10 0 0\n",
      "      0 11 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAYAAAB5fY51AAAW20lEQVR4Ae3df2xd5X3H8eecc+/179+JHRMShyQU6gS2ZumACUiaoraaxtZOM6Wq1Kn8sU6alm5Vpa1aqXGnTaLrtK1TUdtNVdWJPxKv/NGWMo0BQaMEUKswJXYyHFichJjajh3/uvb9dc6+z7WdpmnYCHy/IffmfdDVda7v/dxzX9f+8JznHp/jHAsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUj0BQJS8lcMkFr+QX/3XBN/gSAQQQQAABBBBA4P8R6HORv0fHhzo+ettTt714f3L/jz+ZfPLFPZN7ev3t/Ul/6K9ZEECgOgRSFf0yxt3yJm3i1qfqUr8eSX+l5L9gKWjxr2t4cLhaNnkr+m1i5RHQEqjswlpRCIIgX1wslkquVJCb0i50RS0gchBA4OoRqIrCEs4gcIHfPIzlEiVBwsjq6vkZY00QUBNgjkeNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqCwrIXJRwABNQEKS42SIAQQsBagsKyFyUcAATUBCkuNkiAEELAWoLCshclHAAE1AQpLjZIgBBCwFqjmwgp61/YG1oDkI4DAlROovsJKnEsvpSMhTAY+MFAMgsAlSRL19/dX32u9cj8nPBMCV4VA6qpYC6WVCJyUU5C4o5uOzvS63kzfV/uaBz4/MCmlVfJPIcUVytex0tMRgwACV1igegpLaih2cZAZz8SfffyzX9/2o20b165b2/qpVz91UorqqeHh4a9JWZ3cv39/dN9995UL7Apb83QIIPAOBaqisMJC6JLGxKVH027nD3eGd+y4Y/emGze5jvYOF4Zhuxj96po1ax44duzY7918881PM9J6hz81PByBd0mgKuZ1SmtLQXAocBu/s9H1dPW41s7WUltrWyxlJTNaMvByrtDS0tK2YcOGH7z00kvvl9tjP9J6l8x5WgQQeJsCVVFY8c/iUuMPG11bTZurba51jfWNUSqV8q/Nf0ror9NyKdbL0tHR8Tcywgr6+vp8mbEggEAFCVR0Ye1yu8rU6aF0U21QW66luHjpOXUpKb/5G0tn3fnoo4/e5Cff/aZhBb1XrCoC17xARf/CPvvss+U3MIiC9+byOZddyLqF7IKbm5tzcXzJ4vIjrqitra3VP3BwcJD9tDwECwIVIlDRhbVq7Mvp3Llz7uzZs+XL2NiYm5yc9LsxrN7FyYjKLS4ulm/392NBAIHKE6iKTwmljI76Enr9zOtOJttdQ0ODi6KoXFAymvKfFLqlpSU3NTXljh8/nj9y5Mgb/q0aGhr6eaNV3nvHGiNwzQlUdmH5KSzZKsz8WmZucXjRvXLkFdfY2FguqHw+Xx51yaeDLp1Ou0KhUFxYWEgdPnz4xw8//PCo3/N9YGDgktuN19xPAS8YgQoRqOzCWkEuthejrr1dbvIfJ92hFw85X1aLi1k3LSMqGW0l6UymKLelT5w4kR0ZGdkrIzI/smL+qkJ+SFlNBFYFqqKwcqO5pOlzTa777m53pO+wO/j8C+7Y2hHX0bHGre3oCGozmbRsDk6Pj4/3HTx48Aijq9W3n2sEKkugKgorqo9cYaLg2j+8xt31xzvcrT/pch03rElOjU+6/zx8ePrkxMQ3XnvttW/KW3OSsqqsH1DWFoELBaqisJz8ZWCYDlxe/uuYCN3t79nidt+1s+QSl3p8w4Z9v9Xf/xeRTLx/8cEHmbe68N3nawQqTKA6Csv/kU15RipxDXOhTLIX3ezkhJubz7o33njjyK5du1K7Ozt9WeUr7P1hdRFA4AKBqtgPy4+wXCSNtRS75mnnmtOhSy3lwjnZ1WF2evqY7GBa3ObK97rgpfMlAghUmkB1FJbfOSElR8OaKLqWhcDVyyGvgmw2nJqYWDo+Ovqqf1OGBgfZ56rSfjpZXwQuEqiKwvIvIpHNwtRUybUWAlebxHHg/0xndvbkiwcPnvF7uQ8sH7XhopfPPxFAoJIEqqGwgrCYBC4MXM3rRddadK6mWEhi+XvCxdnZ4z+VQ8vEX/pSNbzOSvq5Yl0RMBGo5F/kYNcJvyHoktl0kluqC1xmKnYtMvueyS0lhZkZNzc7OyJqwYEDByr5dZq88YQiUIkClfqLLBt5Lnl21C35iamds6meDYcWk+ufOefqXcGFS7lgRkZYE8XiAf/9pvl5uTsLAghUukAl/iL7kvXT7OGfXH/9JzbF8ec2yCGQuwtJ0Czz7nV1GVcvf46zmMkEo9PTf/qB0dG/9y9SHlAuuUp/w1h/BK5lgStaWP5In3IMqvKozh/xc+Vv+i7n07ty6azr7q7/SBA8tjMMP7xVDiHTKe/g2ih0bbJzaI1PK8l+DqVSHERR+D+l0r6nnHvgD8bGFv0bLS/4cp7PP4QFAQSuEoErteNo4MtKFj8ykjZZXvynd/v27XurZ7EJ+mRUNdjdXSNF9f33BMEH18ZxoSkIonY5HPKaTMbVyMWl5CX542AV5MwUS0vFLWH4cVcstj/k3EdkX6yVwRaltfIWcIVARQlciRHW6iac27t3b9eOHTt61q1b5w/3krv33nv/y2u9lVNvSVlFg1J227q7v3FXFH1mexzne4Ig0yMFtbG21rXU1zvnLzU1y2+AHP8qmZ93wfx8Xp4s83Kx+OX3jY31S5X5XUzPl2ZFvVusLALXuIBpYa3+ofGePXu67rzzzq/ccsstv7N58+aWrq6u8jGqSqXSITky6IO33nrr488880xq4pFHyptra3t/8RTzB2Qz8qGhoUL7hg3bb4rjl39DxlA3ymjrhigKNsmoqkcO2Ffb3OycHPuqXFp+w1GOLioHxHJuejqRYyYnpwuFxZdSqRt/99Of/pkbHg6kJf1ojwUBBJQFVqZ6lFOX4ywLq3yW5e3bt2++++67//2ee+7ZsnXrVrdx48ak2ZfLyvPLSMudOnXqj7Zs2fLI6o1vdt22bt1f70ilvvC+OC5uCYKUFJa7QUZXm5qaXEaOLOr8RQ7gJ0fwWy4sOR6Wm5jwxVWUA2Klns/l/nDX+Lg/agMLAghUoIDZHJbMWflJq3RnZ+djMqraIkcCzbe2tqalrMolKd/2x1wvydFAAymxr//b/v2LR594Yri1qakxWFjY2hJFSSmb7WmI4zVRLhc15vPv/dtXX73p9NRUEsvxj2X/UNmBwcnxGeT8XTKiyvhR1erFF5aU2QWXaL5YTNZ0dv7V/O7dD8gmYgW+VawyAlexQBCU5NcyikuloYbHHntANpX8b2N5i0lzrU0Kyx8dQcqqKHNVvy0l9StSSkV5LRk5xdb5dfcT7nKJ5PQ2iZxDMNm2efO3b7j9dtcgZVNXLLp0Lid/wLzkUnLtslmXltt6ZLQ0LJecP167JGXlsiDFJ2Xk6n0J+fv6SXdfVvLYcjH5s+dId87LdZOclrChpaVDDkm6zHl+bfgCAQTekYD8Hso8j9+yWZlEfkdpb/pgk8KSUVW5WaWIPiqllBSlUPymn7/+pUXuICcIdFJscef69UGSzyfJwoL8MWAgeyaUXEFKKCfXTf6xQRBKSYVzEjInj5n1Z8uR752V7HoptUY/svL389e+lBYWyiW2JPeZk7ys3Kf+zBkXL2f90qpwAwIIvD0B/ztcJ4OFGT9oMFxMCkv2tSpPaMvpt3rnZS9zOU9gMDs7K/Pf03LY4o7yGW3OvyY/cJRlfHo6yJ4+XSrKyKgge6nn5bI0Px/l5JM+uQ4y/na5LEkZnZX7+1mwJkGql/KpESSZMHNdUmCNch8Z3pWLqyRfL8plUjYHF+Rxz8kKfHt+/rj8f0B/rCqZLAhcswKySSjTMlEujoe8wfJvtb6GSWGtrK8fWU3K6bcSf47ACdmUO336tHyIV++6u7tly235qf0oakbK7JUzZ4Knn3wyVZCRUVZKyo+G/HkEs1I4colL+fzMZBzXyAPrx6SomuRJ6uQ6LSUVygirJF9npbxk7svV+RWQ2/2obkEus/Ikk2GYktHYFx45ceJb+owkIoDARQLq81c+36qwZJvMyVZd8LSU1YdGR0eTJv9JnuyC4Cfb/ZmZ/em3/PkC5dRbybxchkZGpr/1xBPfC8LwZL5QmMgWCrHMb/m29vtMzctlLNXcvKWzsfEF+dzPNciGn4yUglCKyQ9HpdnLc1l+s9BvREdymy/DrMyRZeWU9EfjeGo4iv41kdN7DcpuDX3s1iBKLAgoC8jWjcVk++paWhWW3yQMZBT1qIySviin1qqvq6uTQVAS+ROa+pOe+vMHykS8HLkqzssZbWrGxse/PJPNfm35s4Xlcj4/rFxGcKXZ2Z/IA78ah+GfnZLHSStm/BPJrJcvpvKcVoPc1xdW6AvLF5lMvZ8Nw8x/l0p/OXj69NRDAwOpAdlgLD/PqgLXCCBQEQLnO8FgbeWjOle67rrrfl+uv+NHVLKvVVF2YSjPY/kRlyyyx0IueGVk5Ml/+e53f7O3tzesGx5OfvrzKabVYeXqdSD7cqUWFxYel7b7YEr2x1onxdgt/dQh135ey38OKYWVSJnFMv2ezEZR6nixuP/7Y2P398n9BtnL3buzIFCRApaF5UFWS+vjMpL6Sm1t7UZ/6vj29nYnX/s5JtnVKvtPzz333Odl9JWVTUj/mNVy8l9fvPg7JDIHVi/3/WfZfPyE/JGia5RLm9zeIiW1uqmYyKbhrNw+nST/8EI6/ef9o6P5geXs/yv/4ufj3wggcBUJlBvCeH3KpSUjo2b5xPBjUlzvl7JJyfzVyZmZmR9JYb288vzlMnoL63L+fjJ6u0/u/xnZLLwjCoI6mdMqT8pJiZ2T256fc+7vxs+c+Y/LzH8Lq8BdEECgmgV8aV1y8X9vKN+43OL09/ePKy89XV2b1q9ff1tnd/fH2uW6obOza/V7shnon/ty81cfzjUCCFyjAr40/CR/+eL3hpevz5fO2zR50zLqX85+06J8m8/HwxBA4F0UqJaRx8WjNP/hIXNV7+IPFk+NAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIXFLgfwHOpRykmSu6lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=300x300>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scm.base import Lattice\n",
    "\n",
    "system.lattice = Lattice(\n",
    "    [\n",
    "        [10, 0, 0],\n",
    "        [0, 11, 0],\n",
    "    ]\n",
    ")\n",
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(\n",
    "    system,\n",
    "    guess_bonds=True,\n",
    "    show_lattice_vectors=True,\n",
    "    show_unit_cell_edges=False,\n",
    "    padding=-1,\n",
    "    width=300,\n",
    "    height=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938c804e27f84196a10c8828c723f798",
   "metadata": {},
   "source": [
    "### Lattice vectors: 3D-periodic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "504fb2a444614c0babb325280ed9130a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "   Lattice\n",
      "      10 0 0\n",
      "      0 11 0\n",
      "      -1 0 11.999999999999998\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=300x300>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scm.base import Lattice\n",
    "\n",
    "system.lattice = Lattice([[10, 0, 0], [0, 11, 0], [-1, 0, 12]])\n",
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(\n",
    "    system,\n",
    "    guess_bonds=True,\n",
    "    show_lattice_vectors=True,\n",
    "    show_unit_cell_edges=False,\n",
    "    padding=-2,\n",
    "    width=300,\n",
    "    height=300,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bbdb311c014d738909a11f9e486628",
   "metadata": {},
   "source": [
    "### Delete lattice vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b43b363d81ae4b689946ece5c682cd59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scm.base import Lattice\n",
    "\n",
    "system.lattice = Lattice()\n",
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(system, guess_bonds=True, width=200, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a65eabff63a45729fe45fb5ade58bdc",
   "metadata": {},
   "source": [
    "### Charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3933fab20d04ec698c2621248eb3be0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "   Charge -1\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "system.charge = -1\n",
    "print(plams.AMSJob(molecule=system).get_input())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd4641cc4064e0191573fe9c69df29b",
   "metadata": {},
   "source": [
    "To get the charge of a system, use ``system.charge``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8309879909854d7188b41380fd92a7c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The charge is -1.0\n"
     ]
    }
   ],
   "source": [
    "my_charge = system.charge\n",
    "print(f\"The charge is {my_charge}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed186c9a28b402fb0bc4494df01f08d",
   "metadata": {},
   "source": [
    "Unset the charge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb1e1581032b452c9409d6c6813c49d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The charge is 0.0\n"
     ]
    }
   ],
   "source": [
    "system.charge = 0\n",
    "\n",
    "my_charge = system.charge\n",
    "print(f\"The charge is {my_charge}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379cbbc1e968416e875cc15c1202d7eb",
   "metadata": {},
   "source": [
    "## Atomic properties: masses, regions, force field types ...\n",
    "\n",
    "In the AMS system block most atomic properties are given as a suffix at the end of the line.\n",
    "\n",
    "To access an individual atom, use for example ``system.atoms[0]``, which corresponds to the first atom. The ``atoms`` array uses normal Python indexing, so the first atom has index 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277c27b1587741f2af2001be3712ef0d",
   "metadata": {},
   "source": [
    "### Isotopes (atomic masses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db7b79bc585a40fcaf58bf750017e135",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0 mass=2.014\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "system.atoms[1].mass = 2.014\n",
    "print(plams.AMSJob(molecule=system).get_input())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916684f9a58a4a2aa5f864670399430d",
   "metadata": {},
   "source": [
    "### Regions\n",
    "\n",
    "Regions are used for example to  \n",
    "\n",
    "* set special basis sets on a subset of atoms, or \n",
    "* apply a thermostat in molecular dynamics to only a subset of atoms, \n",
    "* visualize atoms easily in the AMS GUI,\n",
    "* and much more!\n",
    "\n",
    "Use the ChemicalSystem region methods to assign atoms to regions. In this way, one atom can belong to multiple regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1671c31a24314836a5b85d7ef7fbf015",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0 region=region1\n",
      "      H 0.17211137 0.97530407 0 mass=2.014 region=region1\n",
      "      H -0.98745581 -0.07596962 0 region=region1,region2\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system.add_atoms_to_region([0, 1, 2], \"region1\")\n",
    "system.add_atom_to_region(2, \"region2\")\n",
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(system, guess_bonds=True, width=200, height=200, show_regions=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b0902fd34d4ace834912fa1002cf8e",
   "metadata": {},
   "source": [
    "### Force field types\n",
    "\n",
    "Some force fields need to know the specific atom type and not just the chemical element. Use ``forcefield.type`` for this when you use the ForceField engine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6fa52606d8c4a75a9b52967216f8f3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0 forcefield.type=OW region=region1\n",
      "      H 0.17211137 0.97530407 0 mass=2.014 forcefield.type=HW region=region1\n",
      "      H -0.98745581 -0.07596962 0 forcefield.type=HW region=region1,region2\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "system.enable_atom_attributes(\"forcefield\")\n",
    "# these types would depend on what type of force field you use!\n",
    "system.atoms[0].forcefield.type = \"OW\"\n",
    "system.atoms[1].forcefield.type = \"HW\"\n",
    "system.atoms[2].forcefield.type = \"HW\"\n",
    "print(plams.AMSJob(molecule=system).get_input())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a1fa73e5044315a093ec459c9be902",
   "metadata": {},
   "source": [
    "### Delete all atom-specific options\n",
    "Reset the modified mass, remove all regions, and disable the forcefield atom attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cdf66aed5cc84ca1b48e60bad68798a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for atom in system:\n",
    "    atom.mass = atom.element.mass\n",
    "\n",
    "system.remove_all_regions()\n",
    "system.disable_atom_attributes(\"forcefield\")\n",
    "\n",
    "print(plams.AMSJob(molecule=system).get_input())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d086ab-bdb8-4300-85eb-5b6b20a0d1e2",
   "metadata": {},
   "source": [
    "## Bonds\n",
    "\n",
    "Most methods (DFT, DFTB, ML Potential, ReaxFF) ignore any specified bonds. \n",
    "\n",
    "When using force fields, you sometimes need to specify the bonds that connect atoms. Some force fields (UFF, GAFF) can automatically guess the correct types of bonds. \n",
    "\n",
    "So **most of the time you do not manually need to specify bonds**.\n",
    "\n",
    "If you **need** to specify bonds, it is easiest \n",
    "\n",
    "* to handle in the AMS GUI: use File -> Export Coordinates -> .in, and then load the file with ``system = ChemicalSystem.from_in(\"my_file.in\")``\n",
    "* to use the ``from_smiles`` function to generate a system from SMILES code, for example ``system = ChemicalSystem.from_smiles(\"O\")`` for water."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170db934-08d5-4a24-abb1-22bbbd7a1f64",
   "metadata": {},
   "source": [
    "The water molecule without bonds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "020e6076-0f5d-4c44-9c73-6daf13696055",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "# in previous pictures in this notebook, we passed guess_bonds=True to the view() function to see the bonds\n",
    "# without that argument, we see that there are in fact no bonds\n",
    "plams.view(system, width=200, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13d219c-f2d9-4028-a279-1a9755e39570",
   "metadata": {},
   "source": [
    "If you need to add bonds manually with ChemicalSystem you can do it as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f9bc0b9dd2c44919cc8dcca39b469f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 0.17211137 0.97530407 0\n",
      "      H -0.98745581 -0.07596962 0\n",
      "   End\n",
      "   BondOrders\n",
      "      1 2 1\n",
      "      1 3 1\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scm.base import Bond\n",
    "\n",
    "system.bonds.add_bond(0, 1, Bond(1.0))  # bond order 1.0\n",
    "system.bonds.add_bond(0, 2, Bond(1.0))\n",
    "print(plams.AMSJob(molecule=system).get_input())\n",
    "plams.view(system, width=200, height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e382214b5f147d187d36a2058b9c724",
   "metadata": {},
   "source": [
    "## Multiple systems\n",
    "\n",
    "Some tasks like NEB (nudged elastic band) require more than 1 system in the input file. This can be accomplished by using a Python dictionary.\n",
    "\n",
    "In AMS, \n",
    "\n",
    "* the \"main system\" has no name. It should have the key ``\"\"`` (empty string) in the dictionary.\n",
    "\n",
    "* every additional system needs to have a name, that is used as the key in the dictionary.\n",
    "\n",
    "Let's first define two ``ChemicalSystem`` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b09d5ef5b5e4bb6ab9b829b10b6a29f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scm.base import ChemicalSystem\n",
    "\n",
    "system1 = ChemicalSystem()\n",
    "system1.add_atom(\"O\", coords=(0, 0, 0))\n",
    "system1.add_atom(\"H\", coords=(1, 0, 0))\n",
    "system1.add_atom(\"H\", coords=(0, 1, 0))\n",
    "\n",
    "system2 = ChemicalSystem()\n",
    "system2.add_atom(\"O\", coords=(0, 0, 0))\n",
    "system2.add_atom(\"H\", coords=(3.33333, 0, 0))\n",
    "system2.add_atom(\"H\", coords=(0, 5.555555, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a50416e276a0479cbe66534ed1713a40",
   "metadata": {},
   "source": [
    "Then create the ``system_dict`` dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46a27a456b804aa2a380d5edf15a5daf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "system_dict = {\n",
    "    \"\": system1,  # main system, empty key (no name)\n",
    "    \"final\": system2,  # for NEB, use \"final\" as the name for the other endpoint\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1944c39560714e6e80c856f20744a8e5",
   "metadata": {},
   "source": [
    "Pass the ``system_dict`` as the ``molecule`` argument to ``AMSJob``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6ca27006b894b04b6fc8b79396e2797",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 1 0 0\n",
      "      H 0 1 0\n",
      "   End\n",
      "End\n",
      "System final\n",
      "   Atoms\n",
      "      O 0 0 0\n",
      "      H 3.33333 0 0\n",
      "      H 0 5.555555 0\n",
      "   End\n",
      "End\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(plams.AMSJob(molecule=system_dict).get_input())"
   ]
  },
  {
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   "id": "f61877af4e7f4313ad8234302950b331",
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   "source": [
    "Above we see that the main system is printed just as before. A second system block \"system final\" is also added with ``system2``."
   ]
  }
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