{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "830c3f88",
   "metadata": {},
   "source": [
    "## Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34ee099c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scm.plams import *\n",
    "from scm.plams.interfaces.adfsuite.ase_calculator import AMSCalculator\n",
    "from ase.optimize import BFGS\n",
    "from ase.build import molecule as ase_build_molecule\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# In this example AMS runs in AMSWorker mode, so we have no use for the PLAMS working directory\n",
    "# Let's delete it after the calculations are done\n",
    "config.erase_workdir = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599f038b",
   "metadata": {},
   "source": [
    "## Construct an initial system\n",
    "Here, we use the ``molecule()`` from ``ase.build`` to construct an ASE Atoms object.\n",
    "\n",
    "You could also convert a PLAMS Molecule to the ASE format using ``toASE()``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c0705df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lNXRMZFGdVGfNw1I6862c9iBfoLyU3Yh2/Bl+xw1UYYe0h8g5QGSyoKo52AjihAQBWo1pEsNMieKPdPQESQBlZoXmisweCSCC78gkkiCcEQlhosWPS5gRVM2mnQrpptLD9FW6i7fT4/UChHqWVn7lj370o82MsyyBGF/hvFY53WyLRAqZRCovUabb0ur7zJ9yJpfyiBQKyeRg7Mb7zne+4+q3atUq7+2335b/Q/WC/WidmdzbOncSvXBGjgQSx7AZjASJILRvMJ/pDwjwhe+9955acjmvTthkz2vcQHOcQlMmkRbHrApEDLRAhOMRBD4qwZYQo5XiCL7GUiL0do3Bt/kM5XwGbXSYPG/jdJ+t45Sp4BOdlpLW0rUS01s8EfbZZ591RJVDHpJDTrc0h+qm8RoWynN5lHb++ed7fOVXX4st4ZPYD4scaI0SiKF1gU1zHAMxwlMGG0F0385sQaCvR+j+GztfLxTF0BT6oq0bmUbAXcsuIUYA45grPiZP5LnnnnPmF+TSNsCPiGH3+wmyhOaSc5Ix1TQHSgOP5ZhDZ4uAlCcXw5lxKlsaKpkg0iYiiEw8Cb5CSAwRRSYe4yTepz71KY+v+zofhXICjskhL0GLrMcvWqyesgQ5uu1Vcxexn7QIDEaCyF6JImCv4JB/BeF6SMKGeRRDAANaZ1/OLsKqHitfxJFzzhjEAl5A2gSxLkEj/C3kmEjeIgbynAMtsogQIgvHfWJUBIAgAedKUB05RBRFEYWynJZCKzlCyjfROZdcconHLGJn6olAIq7MPHq2PAYs1ZkQIy2gjiJ7FALLIV+HhpvLPTTQU6YeKyMHIGQbBiNBhJlIUoRz/X0EbiOt9VKE7izNaxJZQnNHrTct86scu4Vu1t9IsCHUL8lXRjz7/fffvxQhnY1gn0svU1SmjzSAyEKaHGaRJULL7ssMC7VB+NCSiSKyiBzKo3lhMqHuu+8+b8eOHc4Uu+yyyzymxnhy3jGjoiKTtBD1aYAsj+OQ30K5e4nWnRsCnIPtYCWIoNP4RRQT6/dsP4aj/SkE7Xw0yUekMQh/xpF/EfPoKfZjiQE+pUsDNLFZBbFWyT8hnoZzfiGa4h8gxHn4DsNEFBEGAXbdx/ofEkTbMOp4cpRpJaLSHezx4VJv7dq1TtOIPJpigoZqQav9CiKvpw7rSFvBdgtRwcjRjkPOfgczQQSiNImEKobA/YKtYqegVv7Tn/60mzwYHkCIZdroPG01zqEeo3UQ6iFMnDHEC9Eun0SYpV0mqKcLErjxjJAM6h3TIJ96zWRCiWgigQgi8wuzyZli0hoihnrQIJWmxpRccMEF/5uesbCXKhydlzNuDjkg5DIMdoIISwlVssCHQiYCIK9BXKPfypgUAvY70sijvIp6kWk725+S9lP8hnJIIk0zA0K4joBkYogEIobML5lL5HNkEUGUT2M10iYa5RexlKYIYYZzjSLIE8Xkak1RPw5byAUCRpB2FDsJfALYkCg9wVl5XX6EPJlshxH2Rgm3tEVIjmTNIWLI/xA5lC6HXQTROSKQSCISaV/ahq2IdpDrtTHargHF3tSzJ/dieZIQMIIkgZGj3ZBsmliFQvHXI/znItyBBFyECEkhYogMCiKPTC0RhHOcjyLCiBhKJwb89yHLHhz3DToHzWHkEBB9GIwgfQeuNInI8mviVRJuCXtIDhFDRJDmCDWMiKDjOpacLmIRY5RRRJ4/0GN1MJwV3HfVt5KFgBGk7+RAPorGQ57FTNLU29EIdxytgfwfIUcyEeSkJ2sV9XSF5IEgvgiEBnmw76psJXdFwAjSFZHc/Zf2KMJH2M/I+90I/v1ogFYEXCP37ioih0gQ+ibJ2kUZQvJwXKGILueVL7/88vMcctPmXSH206cIGEH6FF7X0xVl5u+DvLQ0F5LMwczS/Ch9f9FpB2kIhN918Up7JGuQRB6yxIrozTpAr9iXObedXX1bbys9gYARpG9FQVokrjcBWRziCmR8FQSYCknaEHot3eOW75FjLu0R+h8JDaNzpTmKMdFQPI1//84772hw0HUn9221rfQQgfYulPCfbfsCAQm6z3STAxDgfLTCU5CkCNNKX/J14xzuxS3GO/ZptRIiRAoO7N/vM+BYzPsl7xL/DnI8L8ecsqznqi+eUpoyTYOkASbHyRLqCKPre9lezrSWK9kugCxnteJ8Y2v5LF7lxdud96CF/23x+O54LPYjzKrFEKWB/J1G8/lvoR8QMIL0A8iJS4gkzn9gWstj7D9WVl6+8KSqqoUfGTOmbfLYsUXDq6piTYcORf+yZcsvn3733S9Bjt2Jc6U5OkbuE2m26QcEzMTqB5CTLiFzS7FMaU2HD7/HC+i+/lThoFfzbgfL1nl1ZWVbRY5/ZtUTDolURg5AyEcwguQD9faZxHoppNSnN8unq5cXTzoiI4JaecHnEweacSxCWcgTAkaQPAHPZZ02EUFYpMdFPHePD5NoP9Q0+audXdkhYATJsyDIfnIEETkUGReJsrVQGAgYQfL8HKRBIglyiBgiiREkzw8l6fJGkCQw8rHrCCKzKkGO0MzKR13smkcjYAQ5GpN+TXE+SEJrmInVr9D36GJGkB7B1HeZ9ABkYkmDsBivmVh9B/UxlWwEOSbYcndS2IvltEeCKIyN5O4CVlJWCBhBsoIv+5NdL5aZWNkD2UclGEH6CNieFpvSSYcwFgoDASNInp9D6KSHJpYbB8EXsVAYCBhB8vwckgcKo0w3kbMup11hlvu1n3wiYATJJ/pcO3TSk8dBNLJuoTAQMILk+Tk4HyTZSU/s57ladvkEAkaQPItC2IslHyRZi7hq8aquhfwiYC9M5Rd/Z2Jp3CMkhxZxMBMrzw8l6fKmQZLA6M/deUzipXXSFzvbR9HRIOrBSmiSCF5I9J3EG4j9WS+7VmcETIN0xqPP/y2EFE8j+E/opal4/ECbHHKim26SIAtLnjRiesXkqtdAlPn2RmGfP5d0FzCCpEOmD9KlNRa1C3v8K+PGzeQtwqvGQ4+TWG2RJdy9PSwqF4nFginFxbP+X13dp/2tW39JNdqkTUSYPqiSFdkNAkaQbgDK1WGRA60R+8QJJ0w+PQjum+R5l05mPawatEYVGqUCE2sIa2UVs+piWTQ6HTY8sbGu7lXWLF0CUX4GSSKQxIbYc/VAeliOEaSHQGWTLSTH7Lq6iybF40+eGolUj2ZhrCrIUc1CvTUs9zOMWEp0TiEfytGapENZFmhUEDyxZuzYe/1t2/4toUlEEllfFvoBAXPS+xjkhbT80hxT6+pmjwmCpyf4fnVVPN421PcjrGASYSUTr5YP6OgLt3zQ0OMba55XWemToI8Wxit8v21GUdGCP48b9x8ys5ZRXh9X2YpPQsDATgKjD3Yjt9HaV0ycOLo2CJaN9f0h5UEQG8Ki1uVoiwrIUYWZVQI5IIXHJ3E9j2+juy1f2oUsqJVSrYnVOjUSuea/x42bL4ddmoQ0C/2AgBGkD0HGtNI67sHo5uZvjYpERpQFQVspwl0CORTLIEgZ2sOT5uAT0h0Eqalp31eaPvVcXBwt8f34yCC4/5HJk2GOhw1mi1j34aPrKNoI0gFFznecaVU1btwprP72P0rxKWj2iwR4FNrwlZD2xarRIB7fQnckkRaR5gijzC1pl9JSfVAkPj4aHXtSY+MXRLqVpkVy/sBSFWgESYVKbtIctmiNfyiLRNAVfOBT5UIOWn8vgCAME3q+jvDhHEjQThQRIowijtKVJxr1+Q0gGlaW58+yHi1g6PtgBOk7jMUD+mb9mdqq64muW/dx9jZIoiUTteWzuO2GWPI2QR5I4UgkIhEjLeRk77SHTjllOGeZmQWGfR1olCz0EQLiBG1+UKfRcgk3UVuvmf9NxEZiM+Mf5bwHwidu26OIoqA0vThFHkX5HE3s+XQRR/kWOzl2//aTnyxZVlXVNr/9Y56OkO7cFD8LFy6MzJo1q1ODyH99trq9ninOsSSUt4HQtwgg7m0iRWNSPMz+YYT+IAQ4ADHK+EhnhO8POpdeRFHQVmkcE1H4HIIrg39+aU3NdG/HjjcvefbZ5vbMnrdi4cKiXW++GcyDLFyzgywixm233RaICIsWLTqKDKxyKtLoeMc5YZm2NYL0uQwgrEWIuXeQeEhbEYN4QCPnaAmRo1gru3OMaSbt/gb7HQThy7h8GN3l5xx/P4J8/rRpj2/+q7+6eU8s9uudsdhTf/eTn7w8e9EiWW0uBMuWadRen4n2IEWM6F1//fUf4VNwHxs2bNhZVfSOVVZWvrRr165VEOMdnSSimDZx8HX6MQ3SCY6c/lHLHEPotrb6/scakMD9JFQSGQvxyiBICYJflPA9pCGGojVKIIuzgzgW1+efIchB0vdzzm7lHz3anzBliog0dQKfc9vX0nLT5htvfOOA7z+95cCBFfe++OIf/PnzxUUX5s6de+ZHP/rRpZBjDp+B88aMGUNv8jB9E/F/8mGelg0bNvzX2rVrb6CeO4wkIWpHtkaQI1jkes85E7wxuIqCL2/gZy+xnEjPllcMIcLRvhj/mxH+g/RWaQCRL3y6TyK0ktYMOQ7rGPm3E8cMH+6+RBVhlB01EB9WUhIdVlY2FYd+6qTm5htPmzLlPTTUc/ubmn66aMWKEojxs2nTplXU1dUFo0aNio0YMYJe5TJq4fls6WP2Po9G+ZvVq1fPgSSbjCSC5kgwghzBItd7zt5H2P+rJR5fhJlVvAtLhk5cX1Kpl6L0XRBlamW/CfNKmqIMjaKHounvMWIrsZFj0iD1xcWa2Rt5dfNmv6amJlLDR0DLGVQsKS/H0y+Pl9fWRk4qK5uAevjy4UOHvnz21q1ebV2dzKlYdXV1dPjw4UUJcrh7hQy4PX7ryJEjT2Z/+XPPPXceB3bLb0nlr7iTBtmPEaTvHrhkP7p5+/b3xo0b9wQDg1fVMwdLoxqh5ogjoCKAerUOsy2HHGrbHUFIc8fZHiIehFxv+n7Rnm3bvMk473W1tW2jGxr8kZACrRCpIVagXUpLS51miRUVReMlJX5VeXlAWrSCQUfN90oOkEN/GYTxWtEuE/l24k2kLVixYkWREaQdKSNIOw599aueIb+ttPS64ubmT9JVNAJDX8MhUac52BE51N1bibBKfCGQk1iJrghCN1VAD1h8ZyQS/f2BAz95Z+fOqeMqK6eNHzq0aGJtrTdh5Ehv/MiRbaNHjPBHQpJa4nimzb+yZYvXwFd0SyGJiKBPTCcIQclHBclBHE3zL8uWLVs8e/bsejSKzhv0PVtGkKNkJacJ4kFk56ZNO/AB5tNV9Bu89iHbmZNFd63r3UL4XQ9XJWQQQWR+hQ+Fk+MQJN7ASPyGWOy+tfv2LdDhzQ0NpxIv/dP27ZfXVlSce2JtbdHEUaO8CTjgdaNHxybW1/vPvP02syErPPkxfGudTrFW9VSlI4n4GECQavyRmez/hq5hKbqOnjH2B2UIn8WgvPl+umlnam3dunUFJLkUIX2SaSbVu5ibRVdTvAENUw2JqqhMgiABkokoMwTi+9F6ju0OgiUvb9t2y0dRLn/GHOLQG4qHW1ruJk7dsnfvpX/54IPLayorzzlx1KiisTji2yHEtBkzeFHxkIt8e91rpkesq5lFOWGgRnHcovh4JcyaNUtdxOGxQbs1gvTPo3dmFSR5Hjv/HEiyhEGH+U28E4I28SCBN4St/A/eKFRwLMD0erXB827hvN8shCiIq8ihoJ5gxRhNvyNLI2RprK8//cP6+vOHVlb+W9348VMgRUBXboTo1dfXe4x7ePhDztxSIclBBGpoaPCU18IRBIwgR7Do6z1HEr6R/i4X+gyCej8rmlyOermQkfXJjKwzydcP6BbeSSu+JhaJPLlr69afkzc2D58Fcuj8MHBa++u3aJojZPH9t9A9bw2ly+rw4cN37969W926kaHMDi5nSj1jH3pR0aPXSs68pncxSE/v2cGDHnkjEJG3e7d+oIusXLlSm0EfjCD9KwIScgm09+GHH65io+hNmjRpGH6CS/+X99/fBxlEgDC4d9nDPym2R8gSBMUcx7cPXmhhkJFrROCKG/eQky4fRGaWtIR6tZQm3wSCBGgQf926dQ1EVyd8EDcCn+J6gyrJCNL/jzsUflwN99JTbOPGjR12DeRQ0DEF5U3WHC4xw49MMGmCl8eOHbty//79szZt2tSG5nDPWWSQT7J3717ni4gg0iiQphXSlKxfv/5hQj09WdJmvbluhioN7ENGkPw9v2QB9LtUI/lYl0M9+xuNRv8XGuOl7du3M7zit6JRijG7nPaQySVnnTwBBGlramoqeeedd9a/9NJLizVIOH/+/JDEPbvYcZzLCFIYDzeX4w0S7gi+zlp6zS6DJE9u27atWn7Gnj17YvgfgQjCiDq88aNoj+IdO3asQ9PMff311xvWrFkjUy+X9SkMhI+xFkaQYwSuwE8TSaKYWq7XDC1xGz1U8zCvSvBLnPaQw46D3oD2ePytt966hfx7iSKHaQ9ACIMRJETi+NvKTJMmUa/ZVfSa3Y4Pcg4aYwJkaaJTYDNdvy9xbEvi1o0cCSCSN0aQZDSOv31nbnFbPprjbbaKncK8efOivDeifKY5OiHT/scIkgKU4ywpFHxpCMUwyM8IIEfWHQJhgcfj1ghyPD7V1PdkWiI1LhlTk1uUjBntoCEwGBEwggzGp2733GMEjCA9hsoyDkYEjCCD8anbPfcYASNIj6GyjIMRASPIYHzqds89RuC4IAhTKXiNIvCZaOe22s8UQSecHBhuewxYioy5KCNFsZZUCAgM9HEQTbgLmJnK2mxugYGgl6+J5mJSXlhGTokisheCgKSrA++LhPfdKQvpPtFbsmSJq78aqjCtU0b+MEjpGrLbb7+dRe4LUxQLs1ZdkUz9Xw8gzos+JcxUHbl06dJRTOn2S0pKUj64sAjlYZp3UyMhTMt2O3Xq1BhTxHM6Ig3RM95HtnXO9vwMDZFrpO655x634EM3DZfD7Fvf+hZrS7SFI/7ZVi2n5w9IgqhVIsQfeOCBys2bN1fzuuiZTMbTSz7FTMjrzmxUq9XKK6esuJP90jaqCC8atd15550HunsyXE9Cn1EQZC4S9FPfXV5dj9dmmZSbmZuuwHi8noYhfKddp6YMujbldUdOLhvZT115Fb5zoxT+55nU6gKZGi7giLCYXYy3GEdTt8oTTzwxZZ3ymTjgCBIKNSZIJW/MfZ4Fz+p5e+4bkhIeGKvnBFrdM6MQSmDIo8WaM2JPkVpoZJiEJl1GybzK08NOl0dCj0CJjXoltoa8aQVQ+cij1UVGITTpinTpFKPrDuWP7iVtmcpMue6VW3diih+V1X4bWvxRH8HKXB71EyvjTJsXiB3XRoPrRaw4L2B9TJeBAHyH1C/S24ypAgtJNLI+8NksN7T/4osvbk6VJ59pA4ogkMKtQC5y8CA+x0MNeMfh56j7/fkEMZ/Xfuihh4qZqZuZ6b2oIJq1iuxFCH6H0Kc6XZpC6Qh/p8OhBmFNYD6BErD+dstzkLOCTF0bmQjXauElrgtp4A5MmDDh0Tlz5uwLidqp0Dz+GTAEETkgQjwkhzAD/EdIO6hjOIKBnME8YtmjS1PfjILXo0KOZAquvvrq1E3zkTy93dvT2xNS5ccHqVf6DTfcsCHVcaUtXrz4PDTHiNGjRz9+7bXXrio0cqiOA4IgITkAdCyt0RUAGQvJkQBVH4fR/eRS+FTeQAlqGLK9d1eG8MzmphM9VgG9WE62VB4dGJEzzjjD1Y8ODf2PcXw217mY48shxx8SzzHbe8im6inPTWs3p8ydn0SnOe6+++5x2LZXUYW2LuQoOFDzAFMuMHBlyPfIJkpD6nz5Uora1wtZpDsTS+SgW3cWGM3Gj/n9zTffvEINoPLlAbduL1noGsQH2DaRAzCv5G728cro46TxCYx2p7LbO7QMBYFAaAWIHBDnIp7n87feeuvKML0gKpmiEoVMEGm34K677joBMP+J/X3Yq49//etfN3KkeJCFnAQJ1NDFBxo5hGmhmljq3G/ETh3J9ioIsp9W5yfXXHONc8gT6jgrW7mQBep4qRvPTc/IkUM+B6PlA0ZzhM+g4DQI/oVPF2MjhDgJM+oUKloPsI/xEcpDqrRaokTlQ5s1Z0Thel6h94RRv/C+EzBkt6GxUQEqUzvZlt3xLEQOnqHKCxhEvQhsz8eH/DkWwKsJ8zh8jmQp3NBxQ/muYgI0PtC6bCQLJ18HUcomT568FqB/jt+xr7a2NspiZ3H1+TNqHmgLWfjMhoUCQ8DJ1B133HEVY1V8g7T5Q4hyMUR88qabbnq90H2OrlgWnAZhjdgY86tqWbupjk8FbGEw6bPEYgYE48y58tkPGFzS6uQR1PYhwO/J6KsWdO6udVRPCousB3xtuaP164qX+89x9RBkbAE5riAC701ZSFKiyuNvxvIwNTXPLM6cpXrKzZhXRZO3u/vVOJI+5MmEhP0aFc8qdG20qK++oHsez3AX9R2Q5BAgBaNBEk9H9QmYv3MGRJnEhyoP0QLxHczUwk0y/IikvYdQ0HlAVeTt8RSURF3Sbii3lDJTjQ6nPSfVAepEMZot4j59oM+DdBvIzxejHaHS5hUmlNmjxo98fKbEj7F1dUlbaM8O8EWH4JBM5FdeeeViVpBvnTFjxn0LFix4Y6BpjvB20wpXmMG2qRHggZeErWbqHD1PZa3cKEuADu1pq99dyQhpBL+tFg0Rwe7vTpNkbGSSrlVDuSXEjOUlru2x3m/l9OnTX/7CF77wwUAlh+69IAkCyO4dgqSHk9WuHFuax6zK6HJyRiHpknew/xXwAxavnErNIJOEXDz4sAyZRDmFrxB64xINU25vLKcoWWGGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAgMPgf8PmNgQA4RtF/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=200x200>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atoms = ase_build_molecule(\"CH3OH\")\n",
    "# alternatively:\n",
    "# atoms = toASE(from_smiles('CO'))\n",
    "\n",
    "atoms.set_pbc((True, True, True))  # 3D periodic\n",
    "atoms.set_cell([4.0, 4.0, 4.0])  # cubic box\n",
    "\n",
    "view(fromASE(atoms), height=200, width=200, guess_bonds=True, direction=\"tilt_z\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6222f4",
   "metadata": {},
   "source": [
    "## Set the AMS settings\n",
    "\n",
    "First, set the AMS settings as you normally would do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7000580b",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Settings()\n",
    "s.input.ams.Task = \"SinglePoint\"  # the geometry optimization is handled by ASE\n",
    "s.input.ams.Properties.Gradients = \"Yes\"  # ensures the forces are returned\n",
    "s.input.ams.Properties.StressTensor = \"Yes\"  # ensures the stress tensor is returned\n",
    "\n",
    "# Engine definition, could also be used to set up ADF, ReaxFF, ...\n",
    "s.input.ForceField.Type = \"UFF\"\n",
    "\n",
    "# run in serial\n",
    "s.runscript.nproc = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f02f2e",
   "metadata": {},
   "source": [
    "## Run the ASE optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04129c60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial coordinates:\n",
      "[[-0.047131  0.664389  0.      ]\n",
      " [-0.047131 -0.758551  0.      ]\n",
      " [-1.092995  0.969785  0.      ]\n",
      " [ 0.878534 -1.048458  0.      ]\n",
      " [ 0.437145  1.080376  0.891772]\n",
      " [ 0.437145  1.080376 -0.891772]]\n",
      "      Step     Time          Energy         fmax\n",
      "BFGS:    0 10:46:19        0.424475        3.0437\n",
      "BFGS:    1 10:46:19        0.354817        2.8239\n",
      "BFGS:    2 10:46:19        0.270256        0.9678\n",
      "BFGS:    3 10:46:19        0.223897        0.6128\n",
      "BFGS:    4 10:46:19        0.200223        0.5503\n",
      "BFGS:    5 10:46:19        0.196200        0.1861\n",
      "Optimized energy (eV): 0.19620006656661387\n",
      "Optimized coordinates:\n",
      "[[-7.36222829e-02  6.46660224e-01  1.80595127e-17]\n",
      " [-4.27710560e-02 -7.22615924e-01 -7.74061622e-18]\n",
      " [-1.12651815e+00  9.85598502e-01 -2.86803391e-18]\n",
      " [ 9.22587449e-01 -9.45309675e-01 -3.18423479e-18]\n",
      " [ 4.42945518e-01  1.01179194e+00  9.09790370e-01]\n",
      " [ 4.42945518e-01  1.01179194e+00 -9.09790370e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Initial coordinates:\")\n",
    "print(atoms.get_positions())\n",
    "\n",
    "with AMSCalculator(settings=s, amsworker=True) as calc:\n",
    "    atoms.calc = calc\n",
    "    optimizer = BFGS(atoms)\n",
    "    optimizer.run(fmax=0.27)  # optimize until forces are smaller than 0.27 eV/ang\n",
    "\n",
    "print(f\"Optimized energy (eV): {atoms.get_potential_energy()}\")\n",
    "print(\"Optimized coordinates:\")\n",
    "print(atoms.get_positions())"
   ]
  }
 ],
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