{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Energy volume curve\n", "## Theory \n", "Fitting the energy volume curve allows to calculate the equilibrium energy $E_0$, the equilirbium volume $V_0$, the equilibrium bulk modulus $B_0$ and its derivative $B^{'}_0$. These quantities can then be used as part of the Einstein model to get an initial prediction for the thermodynamik properties, the heat capacity $C_v$ and the free energy $F$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialisation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by importing matplotlib, numpy and the pyiron project class. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:08.037567Z", "start_time": "2019-09-04T12:58:06.149845Z" } }, "outputs": [], "source": [ "%matplotlib inline \n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from pyiron import Project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next step we create a project, by specifying the name of the project. In addition we remove all jobs which might exist in the project before to have a clean project for our example. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:09.049344Z", "start_time": "2019-09-04T12:58:08.040033Z" } }, "outputs": [], "source": [ "pr = Project(path='thermo')\n", "# pr.remove_jobs(recursive=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Atomistic structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To analyse the energy volume dependence a single super cell is sufficient, so we create an iron super cell as an example." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:09.222395Z", "start_time": "2019-09-04T12:58:09.051675Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6e07e110201e4b6385da6b4efcb68296", "version_major": 2, "version_minor": 0 }, "text/plain": [ "_ColormakerRegistry()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c9afaaee5ce4619a5d352a1bc598e0c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "NGLWidget()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basis = pr.create_structure(element='Fe', bravais_basis='bcc', lattice_constant=2.75)\n", "basis.plot3d()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculation\n", "Energy volume curves are commonly calculated with ab initio codes, so we use VASP in this example. But we focus on the generic commands so the same example works with any DFT code. We choose 'vasp' as job name prefix, select an energy cut off of $320 eV$ and assign the basis to the job. Afterwards we apply the corresponding strain." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:12.063333Z", "start_time": "2019-09-04T12:58:09.224176Z" } }, "outputs": [], "source": [ "for strain in np.linspace(0.97, 1.03, 7):\n", " strain_str = str(strain).replace('.', '_')\n", " job_vasp_strain = pr.create_job(job_type=pr.job_type.GpawJob, job_name='gpaw_' + strain_str)\n", " job_vasp_strain.set_encut(320.0)\n", " job_vasp_strain.structure = basis.copy()\n", " job_vasp_strain.structure.set_cell(cell=basis.cell * strain ** (1/3), scale_atoms=True)\n", " job_vasp_strain.run()" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-10-16T18:51:35.690791Z", "start_time": "2018-10-16T18:51:11.248Z" } }, "source": [ "As these are simple calculation, there is no need to submit them to the queuing sytem. We can confirm the status of the calculation with the job_table. If the status of each job is marked as finished, then we can continue with the next step." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:12.728534Z", "start_time": "2019-09-04T12:58:12.118542Z" } }, "outputs": [ { "data": { "text/html": [ "
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janj/pyiron/projects/2019/2019-09-04-website-examples/thermo/encut_320/ \n", "\n", " timestart timestop totalcputime computer hamilton \\\n", "6 2019-09-04 13:50:31.285688 None None janj@cmmc001#1 GpawJob \n", "38 2019-09-04 13:50:42.970024 None None janj@cmmc001#1 GpawJob \n", "45 2019-09-04 13:50:52.350144 None None janj@cmmc001#1 GpawJob \n", "8 2019-09-04 13:51:01.413156 None None janj@cmmc001#1 GpawJob \n", "39 2019-09-04 13:51:10.251511 None None janj@cmmc001#1 GpawJob \n", "0 2019-09-04 13:51:20.041600 None None janj@cmmc001#1 GpawJob \n", "4 2019-09-04 13:51:29.741836 None None janj@cmmc001#1 GpawJob \n", "37 2019-09-04 13:51:42.796905 None None janj@cmmc001#1 GpawJob \n", "5 2019-09-04 13:51:53.088836 None None janj@cmmc001#1 GpawJob \n", "44 2019-09-04 13:52:02.979283 None None janj@cmmc001#1 GpawJob \n", "2 2019-09-04 13:52:11.921097 None None janj@cmmc001#1 GpawJob \n", "7 2019-09-04 13:52:21.335934 None None janj@cmmc001#1 GpawJob \n", "3 2019-09-04 13:52:31.009130 None 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2019-09-04 13:55:00.556380 None None janj@cmmc001#1 GpawJob \n", "15 2019-09-04 13:55:10.828970 None None janj@cmmc001#1 GpawJob \n", "16 2019-09-04 13:55:19.451476 None None janj@cmmc001#1 GpawJob \n", "17 2019-09-04 13:55:28.235999 None None janj@cmmc001#1 GpawJob \n", "18 2019-09-04 13:55:36.912405 None None janj@cmmc001#1 GpawJob \n", "20 2019-09-04 13:55:46.777440 None None janj@cmmc001#1 GpawJob \n", "21 2019-09-04 13:55:55.186420 None None janj@cmmc001#1 GpawJob \n", "22 2019-09-04 13:56:05.185718 None None janj@cmmc001#1 GpawJob \n", "23 2019-09-04 13:56:14.138209 None None janj@cmmc001#1 GpawJob \n", "24 2019-09-04 13:56:22.072544 None None janj@cmmc001#1 GpawJob \n", "25 2019-09-04 13:56:30.773140 None None janj@cmmc001#1 GpawJob \n", "26 2019-09-04 13:56:38.738514 None None janj@cmmc001#1 GpawJob \n", "27 2019-09-04 13:56:46.853680 None None janj@cmmc001#1 GpawJob \n", "28 2019-09-04 13:56:55.404483 None None janj@cmmc001#1 GpawJob \n", "29 2019-09-04 13:57:03.940368 None None janj@cmmc001#1 GpawJob \n", "30 2019-09-04 13:57:13.347542 None None janj@cmmc001#1 GpawJob \n", "31 2019-09-04 13:57:21.459612 None None janj@cmmc001#1 GpawJob \n", "32 2019-09-04 13:57:29.623085 None None janj@cmmc001#1 GpawJob \n", "33 2019-09-04 13:57:37.903535 None None janj@cmmc001#1 GpawJob \n", "34 2019-09-04 13:57:46.032614 None None janj@cmmc001#1 GpawJob \n", "35 2019-09-04 13:57:54.280901 None None janj@cmmc001#1 GpawJob \n", "36 2019-09-04 13:58:02.411374 None None janj@cmmc001#1 GpawJob \n", "\n", " hamversion parentid masterid \n", "6 None None None \n", "38 None None None \n", "45 None None None \n", "8 None None None \n", "39 None None None \n", "0 None None None \n", "4 None None None \n", "37 None None None \n", "5 None None None \n", "44 None None None \n", "2 None None None \n", "7 None None None \n", "3 None None None \n", "1 None None None \n", "40 None None None \n", "42 None None None \n", "43 None None None \n", "48 None None None \n", "9 None None None \n", "10 None None None \n", "19 None None None \n", "41 None None None \n", "46 None None None \n", "47 None None None \n", "11 None None None \n", "12 None None None \n", "13 None None None \n", "14 None None None \n", "15 None None None \n", "16 None None None \n", "17 None None None \n", "18 None None None \n", "20 None None None \n", "21 None None None \n", "22 None None None \n", "23 None None None \n", "24 None None None \n", "25 None None None \n", "26 None None None \n", "27 None None None \n", "28 None None None \n", "29 None None None \n", "30 None None None \n", "31 None None None \n", "32 None None None \n", "33 None None None \n", "34 None None None \n", "35 None None None \n", "36 None None None " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pr.job_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We aggregate the data for further processing in two separated lists, one for the volumes and one for the energies. To do so we iterate over the jobs within the project, filter the job names which contain the string 'vasp' and from those extract the final volume and the final energy. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:14.448207Z", "start_time": "2019-09-04T12:58:12.730038Z" } }, "outputs": [], "source": [ "volume_lst, energy_lst = zip(*[[job['output/generic/volume'][-1], job['output/generic/energy_pot'][-1]] \n", " for job in pr.iter_jobs(convert_to_object=False) if 'gpaw' in job.job_name]) " ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-10-16T19:02:33.916818Z", "start_time": "2018-10-16T19:02:33.912978Z" } }, "source": [ "We plot the aggregated data using matplotlib. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:14.873088Z", "start_time": "2019-09-04T12:58:14.449911Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Energy (eV)')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(volume_lst, energy_lst, 'x-')\n", "plt.xlabel('Volume ($\\AA ^ 3$)')\n", "plt.ylabel('Energy (eV)')" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-10-16T19:12:50.900916Z", "start_time": "2018-10-16T19:12:50.896333Z" } }, "source": [ "## Encut Dependence\n", "To extend the complexity of our simulation protocol we can not only iterate over different strains but also different energy cutoffs. For this we use multiple sub projects to structure the data. And we summarize the previous code in multiple functions to maintain a high level of readability. The first function calculates a specific strained configuration for an specifc energy cut off, while the second function analyses the different strained calculations for a specific energy cutoff and returns the list of energy volume pairs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:14.881066Z", "start_time": "2019-09-04T12:58:14.876094Z" } }, "outputs": [], "source": [ "def vasp_calculation_for_strain(pr, basis, strain, encut):\n", " strain_str = str(strain).replace('.', '_')\n", " job_vasp_strain = pr.create_job(job_type=pr.job_type.GpawJob, job_name='gpaw_' + strain_str)\n", " job_vasp_strain.set_encut(encut)\n", " job_vasp_strain.structure = basis.copy()\n", " job_vasp_strain.structure.set_cell(cell=basis.cell * strain ** (1/3), scale_atoms=True)\n", " job_vasp_strain.run()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:14.895675Z", "start_time": "2019-09-04T12:58:14.883541Z" } }, "outputs": [], "source": [ "def energy_volume_pairs(pr):\n", " volume_lst, energy_lst = zip(*[[job['output/generic/volume'][-1], job['output/generic/energy_pot'][-1]] \n", " for job in pr.iter_jobs(convert_to_object=False) if 'gpaw' in job.job_name])\n", " return volume_lst, energy_lst" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these functions we can structure our code and implement the additional for loop to include multiple energy cutoffs." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:32.618120Z", "start_time": "2019-09-04T12:58:14.897403Z" } }, "outputs": [], "source": [ "for encut in np.linspace(270, 320, 6):\n", " encut_str = 'encut_' + str(int(encut))\n", " pr_encut = pr.open(encut_str)\n", " for strain in np.linspace(0.97, 1.03, 7):\n", " vasp_calculation_for_strain(pr=pr_encut, \n", " basis=basis, \n", " strain=strain, \n", " encut=encut)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis " ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-10-16T19:27:02.877244Z", "start_time": "2018-10-16T19:27:02.873384Z" } }, "source": [ "The analysis is structured in a similar way. Here we use iter_groups() to iterate over the existing subprojects within our project and plot the individual energy volume curves using the functions defined above. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.013569Z", "start_time": "2019-09-04T12:58:32.663233Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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e1h5MPzy9yMH4x/gz/fB0PKw9tGxZ6VRGz2X27Nn4+PgQGBjI6tWreeuttwAeKz0XyUMmMDKFxSNbFcVY+ns5YGFkwI9HrrLtfDTnbtzm+xGt8G5QuXLkEsnDZI7/HEKSQspsY2tsy+t7X8fW2Jb4zHia1G3C0vNLWXp+aYnt3a3cec/3vTLHrKl6LhcvXuT9999X34e7OxEREcTGxnLt2rXap+ciqR1M6OZyX/C+c1Mb1oxrz5+vd6RAJXhuqR8/HbkqpZUljxTmBubYGtsSkxGDrbEt5gbmlRqvJuu5eHt789dffwHg7+/P9evXiYyMrJ16LpLaT9vGVuyc0oX3NgUye2cIx64kMn+oN7ZmdbRtmkRSJg+aYcB/S2Gve73On6F/8ob3G/jW963wNWuynsvMmTN566238PHxwdPTk1atWqGnp1etei7SuUjKxMJYn6Uvteb3Uzf4fPtF+i06ysLh3nRpWnvUOyWSe7njWOZ1m4dvfV986/kWe18RRKGey9dff13s+Lx587Si5/Ljjz8WHTM3N2flypVFdjo7O+Ps7ExmZuajpeciqV0oisJLHRrx96QnsDTW5+Wf/flmVwh5BaoHd5ZIaiDBicHFHIlvfV/mdZtHcGLwA3qWTk3Wc7l9+za5ublFbbp27Yq5uXm16rlI5yLRGPd65vw9qTMjfBuy7PBVhi47wc2kTG2bJZGUm7EeY++bofjW92Wsx9gKj9miRQu+/PJL+vTpg5eXF7179yYmJqbU9osWLeLgwYN4enrSpk0bLly4gL6+Pp988gnt27dnwIABuLu7F7UfPXo0EyZMwMfHh6ysrBLHnDBhArGxsXTs2BEfHx8+//xzQB0PatmyJe7u7uzatYtFixYB6pnS4sWL6du3L82bN2fYsGFlxn7Kg9RzoYJ6Lse+BcfWxTW3w4+olew6v121BtZAdgTGMPOvQBAw+1lPnvGumqm0RFJRpJ5L9SL1XB4Wjq3Vmtuhu9Tv72hyO7bWplUPjf5e9dk5pQtN7U2ZvPYc720MJDO39LVkiUTyeCED+hXFuSv0/BTWjgCXnhBzrrgm92NAAytj1r/ekW/3XeaHQ1c5cz2JxSNb07x+5VI6JRJJ2Ug9l0edZk/DsYVwdR9YuYBd1axV1ib0dXWY0dedTi42vL0+gEFLjvNR/+a83KGRFCOTSKoJqefyqBN/CXJSwaUHJF2F79vA1YPatkorPOFqw663utDJxZpPtl7gtTX/cjszV9tmSSQSLSGdS0W5E2MZugpe3gzPfKd2NGsGw95PIP/xe7DamNbhl1Ht+Kh/cw6FxtFv0VFOXUvUtlkSiUQLSOdSUaLOFo+xtBkFI9aDYxs4vgh+6QOJj5+ao46OwvguTfjrjSeoo6fDiOUn+XbfZQpk6RiJ5LFCOpeK0vnt+4P3br3h1QMwbA0khcOyLnDud3gM0709nSzYPqULg3wc+XZfGCOWnyQmpeTcfIlE8ughnUt10GIgvHEcHFrB1jdh0zjIuq1tqx46pnX0WDjch/lDvQmOSqHfoqP8c+GWts2SSEhcsYKMk6eKHcs4eYrEwhL1NRFNSu6PGzcOb29vvLy8eP7550lPTwfUJV+mTJmCq6srXl5enD17tqiP1HOpbVg4wai/ocdHcGGLehZz49SD+z2CPNfGie2TO+NY14jX1vzLp1uDyc4r0LZZkscYQw9PoqZOLXIwGSdPETV1KoYenlq2rHQ0cS4LFy7k/PnzBAYG0rBhQxYvXgzArl27CAsLIywsjJ9++ok33ngDkHoutRcdXeg6A5y7qWcvK/tBt/eg63T1uceIJram/PVmJ+bsCuWX4+H4RyTz/YhWuNqZats0ySPIrdmzyblUtp6Lnp0dN8aPR8/Ojvy4OOq4uJCwZAkJS5aU2L5Oc3fqffBBmWNqW8/F3Fy9x0wIQVZWVtF2gK1bt/LKK6+gKAodOnTg9u3bxMTEEBERIfVcahrlkklt4AsTjoHHs3BoNqwaALdv3t/uEaeOni6fPNOCX0a3JTY1m2e+P8afp6WcskQ76Jqbqx1LdDR6dnbomj8aei5jxoyhXr16hISEMHnyZIBSdVuknksN5I5M6scdP6Z3o97FSniXiKEFPLtcvZt/53RY9oQ6fbnl4IdreA2gh7s9u97qwtvrAnh3UyBHryTw1RAPzA31tW2a5BHhQTMM+G8pzObNN0heuw6biRMx6dC+wtesKXouK1eupKCggMmTJ7N+/XrGjBlTqm5Ldeq5yJlLBfGt78sbPm/wzqF3GLdnnGZaEIoCPiPg9SPqHf0bRsHfkyE34+EZXkOwNzfkt/Htmd7HjZ1BMfT/7igBNx+/pAeJdrjjWBwXLsR2yhQcFy4sFoOpCHf0XAICAggICCA0NJRZs2ahr6//UPVc7lxn+PDhbNq0CVDPSErSbSnteFUgnUslGNBkAC4WLvjf8sdY3xhnC2fNOlq7wLh/oPNUOLsGfuwGMeer19gaiK6OwqQeTfnz9Q6oVPD8Uj+WHZZyypLqJzs4CMeFC4tmKiYd2uO4cCHZwUEVHlPbei5CCK5cuVL0+7Zt24pK9g8cOJDVq1cjhODkyZNYWFhQv359qedSU7mUeImk7CS6OXUjKj2KQVsGcSzqmGaddfWh1yx4ZSvkpsOKXnBiCagePwGuNo3Ucsq9W9jzza4QRq30Jy6tYt/YJBJNsB4//r4lMJMO7bEeP77CY2pbz+XOzMnT0xNPT09iYmL45JNPAHj66adp0qQJrq6uvPrqq/zwww+A1HOpdiqi53KvTOqWsC18euJTVELFmJZjmNxqMvq6GsYQMhLh70kQuhNce8HgpWBqV4E7qd0IIfjD/wafb7uImaEeC4b50NVNyilLNEPquVQvtULPRVGUoYqiXFAURaUoStt7znkpinKi8HyQoiiGJfSfpShKlKIoAYWvp8vTvyq4VyZ1cNPBLOmxBC8bL1ZeWMmo3aO4maZhRpiJNbzwB/SfDxHHYGknCNtXHWbXaBRF4cX2jfh7UmesTAx45Rd/vt55idz8x282J5HUdrS1LBYMPAscufugoih6wG/ABCFES6A7kFfKGAuFED6Fr50V6F8pBp1U0fJ68Vlfq0h9vot5kgXdFxCREsGwbcPYHb5bswEVBdqNh1cPgokt/P4c7H4f8nOqwfqaTbN6Zmyd2JmR7Rvy45FrDF3mx41EKacskdxhyJAh+Pj4FHvt2bNH22YVQyvORQhxSQgRWsKpPkCgEOJ8YbtEIUR5tnJXtr/GlLXDt3ej3mwYuAGXui7MODKDWX6zyMrXsK6WfQt1fTLf1+DkD7C8J8SX9FE92hgZ6DJ7iCc/vNia8IQMnv7uKFsDqib/XiKp7WzevLkoK+3OqyYJhUHNC+i7AUJRlD2KopxVFOXdMtpOUhQlUFGUXxRFsaxA/0ph0qE99b/8ghuvvkrMrFlFaY13goSOpo6sfGol4z3H81fYX4zYPoLLyZc1G1zfCJ6eCyPWQWqUOpvs31WPZQHMpz3rs/OtLjSrZ8Zb6wKYseG8lFOWSGoB1eZcFEXZpyhKcAmvQWV00wM6Ay8W/hyiKErPEtotBVwAHyAGmF/O/iiK8pqiKGcURTkTHx9foXvUs7NH0dPj9rr1GPn4YNy++B4XfR193mr9Fj/2/pHbObcZuWMkf4b+qfmO9Gb94A0/aNgetr0Ff74CmUkVsrU242RpzPrXOjDpSVc2no1kwPfHuBCdom2zJBJJGVSbcxFC9BJCeJTw2lpGt0jgsBAiQQiRCewEWpcwdqwQokAIoQKWA77l6V84xk9CiLZCiLa2thXLSFJlZKAYGKDn6Ej6wYPcGDcOVcb9GyI7OnRk48CNtLVvyxcnv2Da4Wmk5qZqdhHz+vDSZuj9uTqbbFlniDheIXtrM3q6Okzv24zfx7UnPTufIUv8WHU8XJaOkUhqKDVtWWwP4KUoinFhcL4bcF+JTkVR6t/1dgjqBAGN+1cFd2IsTosW4frPHsyHDCbT7wRXn+5Pdsj9BfNsjGz4odcPvNPmHQ7eOMjQv4cSEBeg2cV0dOCJt2DcXtCrA78OgANfQsHjtzzUqVBOuXNTG2Ztu8irq/8lOePxU/2UVI6ze64TGVq81EpkaDJn95S+6VFSPrSVijxEUZRIoCOwQ1GUPQBCiGRgAXAaCADOCiF2FPZZcVfa8v8K04wDgSeBqQ/qX9XcvcNX0dXF8euvsZs5E1V6OhHDhpO8bt1936p1FB3GeIzh136/oigKo3ePZkXQClRCw1Rbx9bq0jHeI+DIXHWV5eSIqr+5Go61aR1+HtWWjwe04PBltZzySSmnLCkHdo3N2bM8uMjBRIYms2d5MHaNK1e8sjqpjJ5LSEgIHTt2pE6dOsybV7z+YXXpuchNlFRsE2Vp5CclEf3eTDKOHsXsqaeo/8Xn6JqZ3dcuLTeNz058xp6IPXSo34Gvu3yNjZGN5hcK2gjbp6p/H7AQPJ+vEvtrG8FRKUxee47riRl0dLFmQjcXujT9b5nT72oCgZEpTOjmokUrJQ+Duzf5Hf3zMgk308tsn5OZR/KtTIwtDMhMycWynjF1jEvf+GzTwJQuw9yq1Oby0L17d+bNm0fbtiXuWQQgNTW1qOz+O++8g52dHTNnziQuLo7r16+zZcsWLC0tmT59OqAuO+Pm5sbevXtxcnKiXbt2rF27tsSS+7ViE+WjjJ6VFQ1+XIbd9Gmk7d1L+JBnyQq6v16RmYEZc7vOZVbHWQTEBfDc389xPKocsRTP59Vl/O2aq7ViNr8BOaXXHXpU8XC0YNvkzgxu5cjxK4mMWXmabefV3+78riYw6Y9zeDlZaNlKSU2kjrE+xhYGpCflYGxhUKZj0ZTffvsNX19ffHx8eP311ykoKMDU1JQPP/wQb29vOnToQGxsLACxsbEMGTIEb29vvL298fPzIyIiAg8Pj6Lx5s2bx6xZs9i4cWORnktp5V+gdD0XOzs72rVrh75+8Xv09/cv0nMxMDAo0nOpCmTJ/WpA0dHBevx4jNq0IWraNCJGvojdtHewGjWqWDlrRVF4zu05vG29mXFkBhP2TWCMR2HpGB0N/qNbNoLRO+HI/9TLZDdPwnMrwLFNNd5dzcO0jrpUTJemNry/KYgpa8+x6d9IAm7e5oeXWtPJpRwzQskjgSYzjDtLYW2fbkzwkSjaDXDGqZnlA/uVxt16Lvr6+rz55pvF9Fy++uor3n33XZYvX85HH31UpOeyefNmCgoKSE9PL7Xk/vPPP8/ixYsfOHMBtZ7Lzp07adGiBfPnzy+zbUl6LqdOVY1irpy5VCPGrVrR5K+/MO3Wlbhv5hD55kTyS/jP42rpytr+axnmNoyVwSsZvWs0kWmRml1EVw+e/ABG74D8XPi5Dxz79rEsgDmklRO73u6KnVkdDl2O53ZWHp9vu8jXOy/hdzVBlpGRFHHHsfR91YP2A5vQ91WPYjGYinC3nouPjw/79+/n2rVr9+m5REREAGo9lztyw3f0XKqClStXEh0dTfPmzVm/fn2ZbaWeSw1E02wT3bp1cfr+e+w//JCMY8cIH/IsmWfP3jeeoZ4hH3f8mPnd5hOeEs7QbUPZHaFh6RiARp3gjWPg3h/2fQprBkNq6RVZH1ViUrLIUwlGtm+Akb4uOorCL8fDGbn8FK0+/4dXV5/ht5PXiUyW5WQeZ+IiUun7qkfRTMWpmSV9X/UgLkLDLQIlUJP1XEpD63ouiqJYKorSUlGUJoqiSIdE+bJNFEXB6uWXaLR2LYqBAddffoWEH39ClDC76NO4DxsGbqBJ3SbMOFzO0jFGljD0Vxj4PUSeVhfADNlZqfusTdyJsSwe2YrZQ7z4eXRbbqVm8+PLbfjp5TYMauXIxehUPtoSTOc5B+k5/xBfbL/IkcvxZOdVS5UgSQ2ldd9G9y2BOTWzpHXfRhUesybruZRGdeq5IIQo8QVYAB8AQUAocAw4A9wENgBPlta3tr3atGkjKsL1Cwli2aSD4tjGy2LFtCPiZkjSA/vkp6WJyKlTxcVm7uL62HEiLz6+xHa5Bbli4ZmFwnOVpxi0eZC4nHS5fMbFhQqx9AkhPjUXYvs7QuRmlq9/LWTpoSvi+JXin+fxK/Fi6aErRe9VKpUIiwvDrVgAACAASURBVE0Vy49cFS+tOCmafrBTNHpvu2j20U4x+pdTYtXxcBGRkP6wTZdUARcvXtS2CWLdunXC29tbeHp6itatW4sTJ04IExOTovMbNmwQo0aNEkIIcevWLTFw4EDh4eEhvL29hZ+fnxBCiEWLFgkXFxfRq1cvMWrUKPHpp58KIYTYuHGjcHNzE97e3iIz8/6/54KCAtGpUyfh4eEhWrZsKUaOHClSUlKEEELExMQIR0dHYWZmJiwsLISjo2PRuR07doimTZuKJk2aiC+//LLUeyvp8wXOiFKeq6WmIiuKshdYDWwTQty+51wb4GUgSAjxc9W4Oe1R0VTkqMvJbFlwDgD3jvXpOUozLQkhBLc3bCD2q9nomJvhOHcuJh06lNjWL9qPD45+QHpeOu+2e5ehbkM1XxPNz4H9n8OJxWDXAp77WV0YU1JEZm4+J68lcjg0nkOX47leWH25sbUx3ZvZ0c3Nlg5NrDEy0NWypZIHIfVcqpfypiLLfS5U3LlEhiaza1kQIMjNKqBFZwe6j2yGoqPZwz879DJRU6eSGx6OzRtvYDPxTRTd+x9iCVkJfHjsQ/yi/ejTqA+fdvoUc4NybPYK2wdbJkB2KvT9Sl3av4qCdo8aEQkZHAqN4/DleE5cSyQ7T4WBng7tna2KnI2LrUmVBT0lVYd0LtVLlTkXRVEuAr8D64QQV6va0JpERZzL3dkmtg1M2bk0iOiw29g1MqP/RG+MzQ00GkeVmcmtL74kZfNmjNu1w2HeXPTt7e9vJ1SsurCK789+j72JPXO6zsHb1ltzg9PjYMubcGUvuPWDQUvUImWSUsnOK8A/PIlDofEcvhzH1Xh13TgnSyO6udnSvZkdnVysMakjM/prAo+TcxkyZAjh4eHFjs2ZM6day+5XpXPxBl4AhgEJwFrgTyFE2fUHaiEVcS5n91zHrrF5UVBQCMGxjVcIOhiJkak+vce2wMndSuPxbm/Zwq3Pv0CnTh0c5nyDadeuJbYLjA/k3SPvEpsRy6RWkxjjMQYdTXMsVCo4tUydTWZkBc/+CE26a2zj487NpEwOX47nUGg8flcTyMwtQF9XoV1jK7o3s6Wbmx1u9qZyVqMlHifnog2qZVlMUZQOwHDgOeAKsFYIsbzy5tYMqrL8S2JUOnuWB5Mcm0nbfo1p178xOrqaPfxzrl0j6u2p5Fy+jPX4cdi+9RaK/v2bKVNzU/nM7zP+uf4PHet3ZHaX2eUrHRMTqN7VnxAGjZ6ALtPAtcd/58OPQNRZ6Py25mM+ZuTmqzgTkVTkbEJj1Vk89S0MC2c1tnRytcHcsPK7viWaIZ1L9VKtMRdFUboDC4EWQog6lbCzRlGVzgUgL6eAI+tCCTlxi/quFvQZ1xJTS0ON+qqys4n9+htur1+Pkbc3jgvmo+/oeF87IQQbwzYyx38OpvqmzO4ym04OnTQ3MjcD9nygFiHT0VMvk3m/oHYsG0bD0FXgXPLsSXI/MSlZHA6N5/DleI6FJZCWk4+ejkLrRpZFzqZFfXM5q6lGpHOpXqrcuSiK0g4YgXrWEgGsAzYIIRKqwuCaQFU7lzuEnrrF4T9C0dXToeeo5jT20nx2kbpzJzEffwK6ujjM/gqzXr1KbBeWHMa7R97lyu0rjPUYy6RWkzQrHXOHi1vVdcnyMtSxmEh/6VgqSV6BinM3bhclBlyIVm/MszWrQzc3W7q52dKlqQ11jTWLy0k0QzqX6qUqYy6zUS+FJaN2KOuEEBrWJKldVJdzAbgdm8meFcEk3EzHu0cDOj7rgq6eZstkuTduEDX1HbIvXMDy5ZexmzEdHYP7H0hZ+Vn87/T/2Hh5I162XszpMgcnMyfNjUyJhF+egpSbYGoPQ34Elyc17y8pk7jUbI6EJXAoNI6jYQmkZOWho4BPg7p0b2ZH92a2eDhYoKNhlqGkZMrjXPy3bqSeixsNPbyKjt0IDuTW1cv4Dno8K4w/iKqsipwD9BNqtcZ5j6pjqW7q2hvz3Ltt8OzuxPkDN/lr7r+kxGtWesSgYUMarf0Dq1GvkLxmDddfGEFuCTt+jfSM+LTjp8zrNo9rt68xbNsw9kTs0dzIpGuQlwnNnoaMeHXpmNWD1HEXSaWxMzfk+TZOLB7ZmrMf92bTG52Y1KMpBSrBwn2XGbj4OO2+2sfU9QFsORdFYnoOyw5fxe9q8cUBv6sJLDv8SCduPjTqubix/dtvuBEcCKgdy/Zvv6Gei/ZK6j+Iyui5/P7773h5eeHl5UWnTp04f/58UR+t6bkoimIMTAMaCiFeVRSlKdBMCLG9zI61iOqcudzNtXPxHFhzCZVK8ORL7jRte3/KcWmk7d9P9AcfQn4+9T7/DIv+/UtsF5kWyXtH3iMwIZChbkN5t927GOqVEe+5N8ZyZT+sfxkUHchNgxaDoecnYC31UKqDxPQcjoYlcPhyPEcux5OYkYuiQBNrE6JSspnZz52X2jfEPyKpqLSNrPJcMnd/sz646ifirl8rs312RjpJkTcxsbQiIzkJK6cGGJqYltrerlETnhz9WpXaXB4qo+fi5+dH8+bNsbS0ZNeuXcyaNYtTp05pXc9lJepZTMfC95HAlxr0k9xDk1a2DPuwHdYOJvyz4gIHfwshL1ezmlZmPXvSZPNf1GnalOhp04n55FNUJRS1czJzYlW/VYz1GMuGyxsYsWMEV5KvlD5w1NniMRbXnjByHXSaDF3fhbC9sLidWpgs7VYF7lpSFtamdRjcypGFw304/WEv/p70BFN7uVHXxICcvAJm/X2B5h/vZtQv/vRubo+Brg45+bIOWlVgaGKKiaUVaQnxmFhalelYNKWm6rl06tQJS0v1tokOHToQGaleiKpOPZcH1t2isHYMcO6uY+cf1K82vSpaW6yi5OcXCL+/rojFr+8Xf3x2UiRGaV7LSpWbK2LnzRcXm7mLq88MFNlXrpTa9njkcdF1XVfRdk1bsSF0g1CpVOU3Ni1WiO3ThPjMSogv6wmx7zMhsm6XfxxJuUnOyBGvrzkjGr23XbT+/B/R6L3totF724XbhzvF8B/9xPx/QsWxsHiRmZOvbVNrBOWtLXY96LxYMm6EOLZ+jVgyboS4HnS+0tcfMGCAyM3NFUII8cYbb4hff/1VAOLvv/8WQggxY8YM8cUXXwghhBg2bJhYuHChEEKI/Px8cfv2bREeHi5atmxZNObcuXOLaot169ZNnD59+oF2jB49WtjZ2Ynu3buLjIyM+87PnTtXjBs3TgihrnV253chhFi9erWYOHFiqfd3L5RRW0yTmUuuoihGgABQFMUF9UxGUkF0dXXoOMSFZ6Z4k5WWy4avT3PxePQdx10mir4+dtPeocHyn8hPSCD8+aHc3rylxLadHDuxaeAmWtm14rMTnzHjyAzScsupVmlqB/3nwUR/aNYPjs6HRd7g9z3kVawcuEQzLsak4h+exJQergjgp8Lqzi93aERGTgGLD4Tx4opTeM7aw7M/HOebXSEcDIkjNTtP26bXeO7EWAa8PZMnhr3EgLdnFovBVITaoOdy8OBBfv75Z+bMmQNoX8/lU2A30EBRlN+B/cC7VXL1x5yGLawZ/pEv9VwsOLgmhL2/XCQ3q3Sth7sx7dIF582bMfL0JOb994l+byaqjIz72tkY2bCs9zLebv02+67vY+i2oQTGV+APyNoFnv8FXj8CDq3hn4/g+zZw7jdQyWWaquZu+YB3+jRj8chWzPwrCFNDPT4a0IJtkztz/tM+rBrTjte6NkFHUfj52DXGrDqNz2f/0P+7o3y+7SK7g2+RlJGr7dupcdy6epkBb88syhZr6OHFgLdncuvq5QqPKUTN1nMJDAxk/PjxbN26FWtrdemn6tRz0WjZCLAG+gMDABtN+tSm18NeFruXggKVOL0jXCyZsF+s/shPxEakaNxXlZ8v4r5fLC66NxdXnuonskJCSm17Lvac6LOhj/D51Uf8HPSzKFAVVNzoa4eF+LG7uqT/Yl8hLm0XoiLLbpIS0UQ+4F4yc/LF8SvxYuHeUDHipxOi2Uc7i5bSes0/JD74K1BsDYgSt1Kyqtt8raDtkvsXLlwQrq6uIjY2VgghRGJiooiIiCi15P7w4cOLLYulpKSI3NxcYW1tLRISEkR2drZo37590bLYgAEDxIEDB0q9vkqlEmFhYUW/T5s2TUybNk0IIcT169eFi4uLOH78eLE+eXl5wtnZWVy7dk3k5OQILy8vERwcXOL4VVlyv7EQIqI0p6SoXbGjeARSlB9WttiDiL5ym70/XyAzNZdOz7ri1cNJ4ylqxslTRM+YQUFKCvYffEDd4cNK7Juam8osv1nsvb6XTg6d+KrzV+UrHXM3QsClv9Vl/ROvQIP20GuWWhVTonVy81UERaVwKjwR//AkzkQkk56j/tbcyNoY38ZW+Dpb0aGJNU6WRrW+ekBN2ES5fv16vv76a1QqFfr6+ixZsoRevXoVpQRv3LiR7du3s2rVKmJjY3nttde4du0aurq6LF26lI4dO/Ldd9/x3Xff4ezsjKOjI40bN2bWrFls2rSJDz74ACMjI06cOIGRkVGxa6tUKrp06UJqaipCCLy9vVm6dCnm5uaMHz+eTZs20aiRWgxNT0+PO8+8nTt38vbbb1NQUMDYsWP58MMPS7y3qtxEuQH1stlW4F8gHjAEXIEngZ7Ap0KIvQ/+yGs2NcW5AGSn57F/9SUiAhNo7GVDz1eaY2iq2Y77/MREot+bScaxY5j1e4r6n3+OrpnZfe2E+K90jK6iywTvCYzxGFN03j/Gn+DEYMZ6jNXM6IJ8OLcGDn0D6begaV/o9SnYt9Ssv+ShUKASXIpJ5VR4EqeuJXI6IonkTHV8pr6FIb7OamfT3tkKF9vaV4CzJjiXR5kqLf+iKEoL4EXgCaA+kAlcAnYCG4UQj0REtyY5F1A//AMPROL31xWMzQ3oPa4lDq51NeurUpH488/Ef7sIfQcHHBcswMjTo8S2YclhTNo/ieiMaPo17sdXXb7iXOw5ph+ezrxu8/Ct71s+w3Mz1VWXj30LOangNRye/AAsKy4dK6k+VCrBlfh0ToUn4V/ocOLS1Lk61iYGRc7G19kK93rm6NbwCgLSuVQvUiysAtQ053KHuOup7FlxgbTEbHwHONP6qUYalwjJPHuOqGnTyE9IwH7GdCxffrnEb6JZ+VlMOzSNo1FHsTOyI6sgi2+7f1t+x1Ls4klwbCH4/wRCpRYn6zJd6sfUcIQQXE/MVDua8CT8IxK5maTeT2FmqEe7xupZja+zFR6OFuhrWO37YfE4OZdarefyOFFTnQtAblY+h34PIexMHE7ulvQa0wITC80KUucnJxPzwYekHzyIaY8eOMz+Ct26Jc+Aph6cyr4b+wB43u15JreajJWh5no0JZISBYe+hoDfQd8EnpgCHd6EOpXfrCZ5OETfzvrP2YQnFgmmGenr0qaRZdEymneDuhjqa1cK+tKlS7i7u9e65bzagBCCkJAQ6VzKS012LqD+h710PIaj6y+jb6hL7zEtadBCswe/EILk1auJnTcfPRsbHOfPx7h1q2Jt/GP8mX54OoNcB7E2ZC15BXmYGJgw0Wciw5oNK1+V5ZKID1UH/UO2g4mteud/m9GgJ6sC1zbi03I4E6F2NqfCkwi5lYoQYKCrg0+DukXLaG0aWWJSR49lh6/i5WRRrGSN39UEAiNTmNCtaksKhYeHY2ZmhrW1tXQwVYgQgsTERNLS0nB2di52TjqXB1AR56KNqqqJ0ensWX6B5FsZtO7bCN9nnNHVcGkiKyiYqHfeIS86Gtu338J63DgUHZ0ix3InxuIf48/UQ1NxMnXiYtJFXCxceM/3PTo6dHzwRR7EzdOwbxZcPwaWjaHHx9DyWdCpWcsrEs1JyczjzPWkotlNUFQKBSqBro6Ch6MFDSyNOBQaz8JhPvRuaV9s/05V10jLy8sjMjKywntDJKVjaGiIk5MT+veIF1ZWz2UT8AuwSwihKrNxLaUizuXuHb4NPbzue19d5OUWcGz9ZS4ej6FeEwv6jG+JmZVmQmQFaWnEfPwJabt3Y9K5Mw5zvmF1zFY8rD2KxVj8Y/wJSgjC2cKZuafnEpkeSc+GPZnednr5SvmXhBBwZZ/aycQGQz0vdWaZS0+Q3zZrPRk5+Zy9kVzkbAJu3iY3X/3YsDE1ICOngG+e9WRQq/sF8CS1j8o6l17AGKADsAFYJYQIqXIrtUhFl8WuBwWwdd6XtHrqGYL276l2x3I3YadjOfh7CDo6Cj1eaU4TH1uN+gkhuL3+T2Jnz0bXwgKHefMwaV968D6nIIfVF1azPGg5BaoCRnuMZpzHOIz1jSt3AyoVBG2Ag1/C7RvQuAv0+gyc2lRuXEmNIjuvgPM3b7Nw72VOhicVHW/TyJLBPg7093LAykQuj9ZWKlUVWQixTwjxItAatRLlXkVR/BRFGaMoyuMtEC4gLzsb/y0bcOvY+aE5FoCm7ewZ9kE7zG2M2LUsiCPrL1OQ9+CJpaIoWL4wnMZ/rkfH1JQbo0cTNfN9RMF/JVwyTp4iccUKAOro1uFVr1f5e/Df9GrUi58Cf2LgloHsCt+lUS20UtHRAe/hMOkM9PsfxF2CFT3U5f4Twio+rqRGYaivS4EQXI5LZ0oPV+oa6TO8XQPSs/P5eOsFfL/ax9hVp9kaEEWWhhXCJbUDjWIuiqJYAy8BLwPRwO9AZ8BTCNG9Og18GFR05nIjOJCt876kID+fgrxc2gwYQreXxj7UYGJBnooTm69y/sBNbBqY0ne8B3XtNZtVqDIyiJw8hQw/P+q4u9Pgxx/JDQ8naupUHBcuxKRD+/v6nI09yzf+33Ap6RKt7Vrzfvv3cbdyr/yN5KTBiSWFBTGzoNVL0H0mmFdRnSOJVrg3xnL3e0tjA7YERPF3QDQxKdkYG+jSt2U9Bvk40NnVBr0aluosuZ/KLov9BbgDa1AvicXcde5MaQPXJiobc7F0cGDLnM+Ji7iGk3tLBs34GEPTh5tuGx6YwP5fL6LKF3Qb2Yxm7etp3Dd23nySVqxAqVMHdHVxWvw9pp1KL+FSoCpg85XNfHf2O1JyU3i+6fNMajUJS0PLyt9IejwcmQtnfgEdXWg/ATq/DUZVMLbkoaNJtphKJfCPSGJrQBQ7AmNIzc7HxtSAAV4ODPJxwKdBXZn9VUOprHPpIYQ4UC2W1RCqIltMpSpg7/IlXDi4F1MrG56ePA2n5iXvjK8u0pKy2fvLBWKupODeqT5dh7uhX0ezvQcxsz7j9rp1ABi4uGD/7gxMunYt8486JSeFZeeXsTZkLcb6xkz0mcjwZsPR09Gr/M0khcPB2eq4jKEFdJ4K7V8HfaMH95XUWnLyCzgYEs/WgCj2h8SRm6+isbUxA30cGezjQBNbuUeqJlFZ5/JsCYdTgCAhRFwFDRoKzAKaA75CiDN3nfMCfgTMARXQ7t4yM4qizAJeRV3vDOADIcTOwhjQCtTxIT1gtRDi6wfZU5X7XGKuhLLzu3mkxMXS/tnhdHzuBXR0H97mMlWBitM7IjizKwJLe2P6vuqBtWPZf5AZJ08RNXUqdV94geQ1a1BMjCmIjcPkiSewe+9dDN3K1hW/knyFOafncDLmJK51XZnpO5P29e9fUqsQt4Jg32dwZS+YOcCT74P3SNCtAgcmqdGkZuexO+gWWwKiOHEtESHAy8mCQT6OPONdHzszzbIkJfdTVfuPKutcdqCWOD5YeKg7cBJwAz4XQqzR2JL/xmyO2nH8CEy/41wURdEDzgIvCyHOF8Z6bgshCu7pPwtIF0LMu+f4SGCgEOIFRVGMgYtA97KqO0PVb6LMzcpk/y/LuHjkAA5uzXl68nQs7OyrbHxNuBmSVKQP03loU1p2cShxFnLHsdyJsdx5b97vKVK270CVnk7doUOxnTIZPevSy7cIIThw8wBzT88lKj2K3o16M63tNBxNqyjlNOIY7P0Uos6AjRv0/ATcB8j05ceEWynZbDsfzZaAKC5Ep6KjwBOuNgz2caSvRz1M68gvG+WhrFhYefYfVda5bAPGCyFiC9/bA0uB8cARIUSF134URTlEcefyNDBSCPHSA/rNomTnMgIYCQwBLIATQAchRNJ9g9xFde3Qv3TsEPtW/ICiKPR6dSLunbpW+TXKIjM1l32rLnLzYhIure148mV36hgV/yNMXLECQw/PYsH7jJOnyA4OwuK550hY8gPJa9eiY2SEzYTXsXz5ZXTqlF5+Jqcgh18v/MqKoBWohIoxHmMY6zEWI70qWM4SQr3Lf//nkHAZnNqpS/w37lz5sSW1hitxaWw5p3Y0kclZGOrr0Ku5PYN9HOnqZouBnkwEKA0hBLdSswmKTGFX8C22B0bTqoElV+LTK7SxtbLOJUgI4XnXewX1kpiHoijnhBCtyuj+oLEPUdy5vA20AewAW2CdEOJ/JfSbBYwGUoEzwDQhRHLhstga1HIAxsBUIcRPpVz7NeA1gIYNG7a5fv16RW+jTFLibrHju7nEhIXSslsveox9HQPDhxc3ECrBub03OLn1GmZWdegz3gP7xublGiPn2jXi/jeX9EOH0Hdywm76NMz69i0zHnMr4xYL/l3ArvBd1DOpx7Q20+jbuOw+GlOQD+f/gINfQ1o0uPZSO5l6ng/qKXmEEEJw9kYyW85Fsz0wmuTMPOoa69Pfsz6DWznSpqGlxoVeH0WEEEQmZ3EhOoWgqBSCo1IJjkohsVCZVEeBusb6JGXkMaWHK+/0aVbua1TWufwANES9gRLgOSASmAFsF0I8WUq/fUBJKUsfCiG2FrY5RHHnMh2YCLRDXd5/P/CREGL/PWPbAwmAAL4A6gshxiqK8gTwJmrHYwkcBfoJIa6VdY/VXVusID+fk5vWcnLzn1jWq8/Tk2dQz6VptV2vJGKupvDPz8Fk3s6lwxAXfHo2QCnnH16Gnx+x38wh5/JljFq3xv79mRh5lv1A/zf2X77x/4aQpBDa2Lfhfd/3aWZV/v/EJZKXpa68fHQBZKeonUunyeA17L824Ucg6qw640zyyJJXoOJoWDxbzkXzz8VbZOepcKxrxCAfBwa3csTN/n5do0cJIQQ3kjKLOZHg6BRuF+r16OkoNLU3w8PBHE8nC1o6WJCWlcc7G87zUvuG/HbqhlZmLgrwLOp9LQpwDNgkqqAoWQnO5QXgKSHE6ML3HwPZQoi5ZYzRGLWT81AUZQlw8k4cSFGUX4DdQog/y7LjYRWuvHkxiJ2L55N5+zadX3iZtgOGoDzEulrZGXkcWH2J8PMJNPKwpufo5hiZlm93tCgo4PamTcQv+o6CxETMBz6D3TvvoF+v9NTnAlUBm8I28f2570nNTWWo21Am+UyirqFmGjUPJCsZji8Cv8WgylPHYgYshPgQ2DAahq4C54e7JCnRHhk5+fxz8Rabz0VzLCwelYDm9c0Z7OPAQB8H6lvU7oxDlUoQnpihdiBR6lnJhehU0rLVKqP6ugru9czxcDSnpYMFno4WNKtnVqxqtdZjLoqi6AJ7hBC9NL5aOSjBuViinq10BnKB3cBCIcSOe/rVv7PfRlGUqUD7wiD+e6j35IxFvSx2GnhBCBFYlh0PsypyVnoae3/8njB/Pxp6+tBv4juYWlaytH05EEIQdCiK45vCMDLRp/e4lji6lX8PSUF6Ook/LSdp1SrQ0cF67Bisx41Dx8Sk1D4pOSn8EPAD60PXY6JvwqRWkxjqNrRqUpcBUqNh+9tweQ8oOqCjD0/NVuvJSB5L4tNy2B4YzZaAaM7fvI2iQHtnKwb7ONLPsz4WRjW7yEiBSnA1Pv0/JxKVyoXoFDIKqxkY6OnQvL45no7meDhY4OFogZu92QPjTjUlW+xv1NlbKRpf8cEGDQG+Rx1XuQ0ECCH6Fp57CXgf9ZLXTiHEu4XHVwDLhBBnFEVZA/gUtokAXhdCxCiKYgqsBFqgnmWtLGvWc4eHXXJfCEHQgT0cXLUc/Tp16PvG27i0qYQ4VwWIv5HGnhXBpMRl4d6xHk++3LxofToyNJm4iFRa932wgmRuZBTxC+aTunMXera22E6disXgQWXOyMKSw5jjP4dTt07R1LIpM9vNrJw42b3smA6nl6udiyoPHNuC72vQcjDoaaaFI3n0CE/IYGtAFFsDoglPyMBAV4cn3W0Z0sqR7s3stK5Hk1eg4kpceqETUTuTSzFpZOWpHYmRvi4tHMzxcDDHw1HtSFztTLUq2lZZ5/In6qKVe4GMO8eFEFOq0khtoi09l8TIm+z47n/EXw+n1VPP0PXFMegZPLwifrnZ+exaFkRkSDI2TiYMmORDcmwme5YH0/dVD5yaaT6jyTx3jthvviH7fCB1WjTHfuZMTHxLdxhCCPbf2M+8M/OKUpent52Og2kly72EH1EvhbUdB6dXgOfzcPUAJF5Ra8m0GQNtx8iyMo8xQggCI1PYEhDFtvMxJKTnYGaox9Me9RnUyoEOztbVngiQm6/icmxaYYxE/bp0K62ogrSJgS4tC2ciHo7meDpa0MTWtMZJTVfWuYwq6bgQ4tcqsK1GoE2xsPzcXI6u/ZWzO7di07Ax/afMwKbBw9OcF0JwYvNVzv1zA119BR0dHfpN8KRB8/Iv1QmVitSdu4ibP5/8mBjMevfCbvp0DBqVfj/Z+dmsurCKn4N+RiAY6zGWMR5jKpa6fMex3Imx3Hn/3EoQ+erg/+U96rIyzZ9Rz2YadpR7ZR5j8gtU+F1NZEtAFHuCb5GRW0A9c0MG+qhLz7Sob17pDMfsvAJCbqUVOZHg6BRCb6WRV6B+9poZ6hUuaf03I3G2NqkVmW6VFgtTFMUIaCiECK1q42oCNUGJ8tq50+z+4VvysrLo9sp4vHv3e6j1lA7/EUrwkSgAbBua0fUFN+o1sajQWKrsbJJW/UriTz+hysvD6sUXsXljAroWpY8Xkx7Dgn8XsDtiN/VN6jOt7TT6NOpTvs/g2Lfg2Lp4EFnj5gAAIABJREFU8P7ebLGkcPWM5twadYaZvSf4vgqeQ8GgkjICklpNVm4B+y7FsjUgikOh8eSrBE3tTBncypGB3g7sCIp5YJwiK7eAizGpxYLtYXHpFKjUz9m6xvpFsZE7M5KGVsa1tnZaZWcuzwDzAAMhhLOiKD6od+YPrHpTtUNNcC4AGbeT2bVkAdcDz+HargN9Xp+CkVn59qRUhMjQZPYsD8ajqwPnD0Siq6tDdkYe7h3q0WGICyYWFYtT5MfHE7doESmb/kLXwgKbSZOwHD4MRb/0IOqZW2f4xv8bQpNDaVevHe+1e6/qUpfvJjcTgv6EUz9B3AUwrAutX4F249QqmZLHmqSMXHYExbD1XBRnricD4GZvSmRyFguH+9C3ZT0OhMTy1roABvs4kpGTT3B0Clfi0in0I1ibGODhqM7WujMrcaxrVGsdSUlU1rn8C/QADt3ZMHnvxsraTk1xLqBeWvp351aO/vErxubm/2/vzeOjLM/9//edmcmeTDaSkEAIAQKEhFUWEVBQARWBWrRa7YYe7eKxtlqPPe35fdv+zu9U257ar7bfb+2i9bTWDRVcUFAQAdn3bBCWJED2bbJNZjLL/fvjmUkmIWGSySSZhPv9es1rnuV+nrlvhiefue7ruq+L2x59clDrxLiFxe1juXymgW1/ymN8Vhznj1ej0wexYM1EcpaP63NJ5e5YTp+m6plnMR84QHBGBolP/YjIG2/s9SFzhy4/f/x5mtub/R+67ImUULoPDr0IhR+AdMLU2zRrJmO5mjJTcKnezHsny9l8vIyz1S0AGMP0NLbZO9okRoWQk2pkhoeYJEeHjioh6YmBistBKeVCz9X4QohTUsqhq4w1yASSuLipKj7Ph8//moaKMhas28Diu+9Hp/d//qRj20pJTI/u4rx3R4tlzB7D3k1nKc2tIzY5nKVfyfTJFwOab6fls8+ofvZXtJeWErF4MYn/9m+ETu09KWajtZE/nPgDb5x5g6jgKB6d/SgbMjf4L3T5ig8s01L9H/0bmGu1HGYLHoZZ90LI6F6Ep/COlJKCiib+Y3Mexy6aWDgxjkduzCA7xUhi9LWZRHOg4vJXtLUnT6Otzn8MMEgpv+3vjg4XgSguoFW53Pm3P5H32XaSJ03hjseeIiZ57JD3o+RULXveOktTTRuT5oxh8YbJRMf7thBNtrfT8Prr1Pzh/+BsbiZmwwYtKWZC7wu3ihqKePbQsxyqPERmbCZPL3ia+cnzfR2Od+xWyH8XDr4I5ccgOApmf1WzZhKGNrOCIrBwLzYcyKr20cRAxSUc+AmwEm3tyDbg/+2eBn8kE6ji4ubM/r188ucXcDqc3PLgd8hatmLI+2C3OTjx6SWOflQCEuaunsCcW9PQB/u2NsBhMlHjTooZEkL8I48Q942v95oUU0rJpxc/5deHf01FawWr0lfxxLwnGBs5yGJ7+ag2ZZb3jrZmZtIKWPAITLlVizpTXDP4a1X7aGLA0WKjnUAXF4Cm2mq2vvAbyk4XMH3JTdz84HcJCR/66Kbmegv73j7HuaPVRMWHsuTuKUycleDz3LL1QjHVv/41LZ99hiE1VUuKuXp1r/ez2C28nP8yL+W+BMDGnI18a8a3CNUP8rRESzUcfQWO/BWaKzSn//yHtHLMqkrmNYG/VrWPJgZquWQCTwLpaAW4AJBSDv3P50FiJIgLgNPh4OC7b7J/02tEjxnD7f/6I1Iy/VC/3gcun2lgzxtF1Je3kpYVx5J7phCb3HvqF2+07t+vJcU8c4awOXO0pJgze3frVbRU8N9H/5ttJdtIiUjhieue4NYJtw6+A9Vh09L+H/wTXNwH+jAtUeaChyF5aCuPKhTDzUDF5STwR+Ao0FG0S0p51J+dHE5Giri4KTtdwNbf/4bmuloW330/C9ZvIGgYpmicDie5n5dx6P1i7O0OZq0Yz3V3pBMc6pvDXTocmN55R0uKWVtL9J13kvjDH2AY2/vU1+HKwzxz6BmKGooYHzmeB2c+yJenfLnj/KGKQ+TV5bExe6NPfboqlbnawsxTb4G9DSbcoInMtDWqUqbimmDAochSynmD0rMAYaSJC4CltYVP//wHzuzfw/isHG579Ami4odn3tfc1M6BLecp/KKCcGMwi++aTOaCJJ+tCEdLK3V//jP1L78MQhC38VskPPRQr0kx7U47bxe9zXPHnqPV1sqK8Sv4+eKfU9RQxJOfP8lvbvyNf3OXdcdcD8f/oeUzM12E6FQtxczcb0LkmMH7XIVimBmouPwMqAbeBazu496qO44kRqK4gObkzv98Bztf+iM6vZ6V336MKQsWD1t/qoqb2P36GapLmxk7ycjSezMZM973EF5bWRnVv32Opg8/1JJiPv64lhRT17OVZrKY+I99/8GuS7vQCR06oeOJ657gvmn3Dc16A6cDzm7XoswufAa6YJhxFyx8GFJH9e8zxTXKQMWluIfDUkqZ4Y/OBQIjVVzc1JeXsfWFX1N14Rwzb1nNTV9/CEPI8MTdS6ekcH8FBzafx9JiY8bSVBauyyA0wvfU5m0nTlD1y2doO3mSkOmupJgLe7dEfrH/F7xV9BZ6occu7UyJncKGKRtYM2kN0cGDn/EAgJoizZI58U9ob1GZmRWjEhUt5oWRLi4ADruNva//nSPvv0Nc6njueOxHJKYPn/5bzTYOvV9M7udlBIfpWLRuEllLUnxOxielpGnrVi0pZnkFkbfcTNKTTxKcnt6l3aGKQzz5+ZPcM/Ue3jjzBmsnreVo1VHy6/IJ1YWyKn0VGzI3MGvMrKGxZixNcPJ1zTdTd9aVmfmbcN1GlZlZMeLxSVyEEE+569cLIe6WUr7lce6/pJT/Pii9HQZGg7i4KTl1nI//8FssLc0se2Ajc1bfOawpKOrKWtjzRhFlRSYSxkey7N6pjJ3kW0JMcCXFfOV/qHvxRS0p5le/SsJ3v4POaOwQFrePxXM/MjiSTUWb+PDCh5jt5qG3ZpxOKN6lRZkVfawyMytGBb6KyzEp5dzu2z3tj3RGk7gAmJsa2fZ/f8eFY4eZOOc6Vn/nccKNg5CXq49IKTl3tJp9b5+jpcHK1IXJXH+X7wkxQUuKWfP885g2vY0uOpqERx/lUPFuxlx3A/Nu76wScXTrK9Qc28fqn74IgNlmZmvxVjYVbeqwZlamr+TuzLuHzpqpL9bWyxz7O1hMKjOzYsTiq7h45hLr2O5pf6Qz2sQFtD/oxz/+gN2vvkRIeAS3fe+HpM8a3t8DNquDox+VcPzTi+j0Qcy/fSIzV4xD56Uk69WwnD5N1bPPYt5/AH1yMs6WFlJ//wKRixbReuAgZT/4AanPPUfEooVXXFtQV9DFmpkcM5kNmRu4c9KdQ2PNtJsh9y1tyqwqr2tm5vzN3ssHKBTDjLJcvDAaxcVNTWkxHz7/a+ouX2Temi+x9L6vo9MPb91wU7WZL946S4k7IeY9mYzP8i0hJriTYu6i+le/or2kBPR6jOvW0rLzs16FxZNht2Y6MjP/CQrf1zIzj18A1YXwlX9Axo1XFkJTKAIAX8XFgVbWWABhgNl9CgiVUg7vXyg/MprFBcBmtfD53//KyU8+InHiJO547EfEpYwb7m5RklvL3jfP0ljTRsbsMdywYTLRCb4lxASQNhsNr71O1X//N1itBMXGEr/xWxjvvBNDcnKf7lFQV8DbRW/zYfGHtNpah96aaSyDoy/DkZe1zMxCBymzoeYM3PIzlaFZEVCoaDEvjHZxcXP28H62//F57LZ2VnzrEbJvGoJ0KV5w2Jyc2HGRI1tLkBLmrprA3JW+J8RsPXCQsscfJ3TWLFq/+ALsdhCCiOuvx7h+HVG33EJQH3KymW1mPir+iLeK3hoea8Zu1abGdv4CGi93PReTBokzIHE6JGZp7wmZoA8e3D4pFN1Q4uKFa0VcAJrra/no97/lUv4pMq9fyq3/8j1CIyKHu1taQsx3znHuiCsh5oYpTJzdv4SY3X0srQcOcvn73yfypptoO3IEW1kZQeHhRK1ahXHdOsIXzEcEeff3DJs1454Km7dRK818/fe0qLLqAm3KrLYInK6CVUF6iJ/cVXASs7QEmyp7s2KQUOLihWtJXACcTgeH33uHfW/+A31IKIvvuZ95t3VWrb6Yd4rK80UsWLdhyPtWdqaB3a6EmOOnx7L0K5l9TohZ95e/EJqd08XH0nrgIJa8XOI2bqTt6FFMW7bQ/NHHOFtb0aeMxbh2LcZ16wiZONHr/YfUmunuY+nJ52Jvh/rzUJWviU11oVayuaGk8z76MBgzVROaJA/RiRqrwp8VA0aJixeuNXFxU3HuDFt+85+0NjSQtWwFq779fS4X5vPB755hzeNPD2p55avhdDjJ213GwfeKsVsdzLx5PPNvTyc4zD/JIJ1tbTTv2Enj5s207tsHTidhs2Zh/NJ6olevRhfjPWx70K2Zvb/zPVqsvRVqTmtiU1XQaem0VHa2CTW6LJysrtZOuO+BFYprDyUuXrhWxQXAajbz/m//i9LcE0TGxWOzWFj7xE+GTVg86UiIua+C8KhgFt81icyFyX61EGxV1TR98D6NmzdjPXsOYTAQuXw5xvXriVy6BGG4etyK25rZVLSJvLq84Vk301fM9Z1C436vKgBrY2ebqLFXTq2NmQrBvpdTUIxelLh44VoWFzdbfvOfnDt8AIBpN9zIknu/jjExaZh7pVFV0sTu14uoLmkiOcPIsnszGZPm34gpKSWWggIat2yh6YMPcdTXo4uLI3rNHRjXrSM0K8urUBTWFWrrZoYr0swXpISmcg/Bcb1qzoDdXWxWaL6b7lNr8ZNBN2qCRhU+oMTFC9e6uFzMO8UHv3uG7OW3cvzjD3A6HAgBc25by8L19xAaOfwOf+mUnD5Qwf53z9PmSoi5aG0GoZH+/+MmbTZa9uylccsWWnbuRNpshEyZgnH9OqLX3IkhKfGq148oa6Y3nA7Nd9Phz3FZOnXnQLrKOgUZtCi1xOldrZ2YCeAOlBjI9J4i4FHi4oVrWVzcwuL2sVzMO8X7z/2S5IwplOQeJzQ8gkVfvpdZK+9A72WKaCiwmm0c/qCEU7suawkx12aQtTTV54SY3nCYTDR9/DGN726m7eRJCAoiYvFijOvWEXXLzQSFXX1dTm/WzJqMNRhDfM+xNmzYLFoCTrfgVLlEp/FiZxtDBCRO04RGF6JlIVj7e8haCyV71GLQQMBPoq/ExQvXsrgc2rKJ5EmZXXws7mix9Flz2f3qy5SeOo4xMYkl932DqdcvDYhf3nVlLex5s4iyM66EmF/JZOzkwc2fZi0upvG992jcsgV7eQVBERFErV5FzPr1hM2bd9Ww5u7WTIguhFXpq0aWNXM1LE3aVFp1flfhMdd2ttGFaKHTKXMgOUfLCh2dovl5olO17dAAnT4cbfQlGrEPKHHxwrUsLn2h5OQxdv/jJWoulpA8OZMbH9jIuOnDXy++e0LMhPGRzLttApPndvqKLp9poLqkibmrJvjvc51OzIeP0Lh5M83btuE0mzGkpmJctw7jurUET7j6Z406a+ZqtNRoQvPF/4bzOyAuQwsOaCoHc92V7YMjrxScaNe2+1h4fOe0m6J3pARLI7TWQmuN9jLXdu5XF8DFAzBuvrZmygdrUomLF5S4eMfpdFCw+zO+eOPvtNTXMXn+IpZ+9ZsBkUbGZnVw9OMSjm0rRToha2kKy76SScX5Rrb9OY9V/5LNuKmxg/LZTrOZ5h07aHx3M63794OUhM2Zg3H9eqJvW40uuvdf4mabmY9LPuatM29dYc0crTpKTkJOl/LMhyoOkVeXx8bsjYMylkHD/av4uge1bNDuP2I2CzRXaK+mcmgqg6YK7d19rLmy08fjJsjQTXBSPF6uY1HJozPYoN3sEopeBKPj5TrmtPV8n9AYrbaQrQ2aLsOyp2DFT/rdHSUuXlDi0ndsVgvHtr7HoS1vYbNamXnLbSzecN+wpvR301hj5pOXC6i60IQhVId0SlZ8bTpT5g9N1Jutqoqm99/HtHkz7efOI4KDibx5BcZ164hcsgSh732dTndrJiUihQZrA88ufZblacuvqFUzYhjo9IvTAS3VLqEp7yZCHsc6ItvcCIhM7Co4PYlQf0ocDEZwgr1ds+A8RcFc200k3Nt1YGvt+T6GCIhI0AQjYoxru/v+GAhP0Cw/fXDvot8PlLh4QYlL/zE3mti36TVOffoRhpAQ5q/dwLw71g1beWVPtv81n7OHq7QdAWlZ8cxYmsKEnHh0usGfTpFSYskvoHHzZpo++ACHyYQuPh7jmjUY168jdPr0Xq/tbs0ApEamUtdWx4PZD7J+ynqSwpNGjo9mKKLFpIS2BpfweIpQeddjlsYrrw2N6dn343ksLFbLZtAXoXQ6tb50tyJ6E4ye+gSadeYpCle8dxOM/tYBUj6XoUGJi+/Ul19mzz//xrnDB4iMi+eGex4g68YVBA1TPqvLZxrY9uc8spelkrvrMhNy4ik7Y6LVZCU8Opjpi8eStSRlQNmX+4Nsb6dlzx4aN2+hedcusNkImTpV88/cuQb9mDG9XltYV8gvDvyCvNo8BAKJ9qwmhCWQnZBNdny29p6QPfp8NYNBe2u3abcyl/h4HGupBrr9TdSHdYpNkA4uHoTUeVB2RPNXCNEpGOY6rWTCFQjNYriaVeEWiogELYPCYP6AUNFiQ4MSl4FzuTCPz//xEpXnihiTls6yBzYOeXEyt7C4fSzu/ZUPZmG3SQr2llOaW4uUMH56LFlLUpk4K2FAxcr6g72hgaaPPqJxyxYsJ09pYc1LbiBm/XoiV6wgKLSr1eeeCrtn6j28ceYNvjv7uzilk/zafPLq8ihuLO5oOz5qfBexmRY3jXCDqmrZb+ztWpqc7r4fz6m4xsuAU4t+M47zPg0VMUZLqzMKE4gGnLgIIe4GfgZMBxZIKY94nJsJvAhEA05gvpSy+4QqQoh/BR4F7MCHUsqnXMd/DDwIOIDHpJTbvPVHiYt/kFJSdGAve157hcaqSibMnMOy+79FYnrGkHz+sW2lJKZHd3Hed48Wa2mwULivgoIvymmptxIWZWDaIs2aiUkauj/G1gsXaNzyHo3vvYe9ooKgyEiib1uNcf16wubO5eRzP+P31m08/M3nWTB2AYcqDvGnvz3GoyGrmP3DnwPQ3N5MQV0BebV55Nflk1ubS2Wrlj8sSAQxOWYy2QnZzIifQU5CDpNjJ2MIGoVO7qHEPX009xtw7JVrfr1OIIrLdDTheBF40i0uQgg9cAz4mpTypBAiHjBJ2TVcRAixHPgJcIeU0iqESJRSVgshsoDXgAVACvApkNn9+u4ocfEvdpuNk9u3cuDt17CYW5mx7GZu+MoDRMUnDHfXOnA6JZcK6ynYW07JyVqcTklqZgxZS1LImDMGvWFofmVKpxPzoUM0vruZpk8+QZrNGMaPp2xSNGOOljDhhT90lA8o+f6/UviD29lw7896vV9tWy35tZrQ5NXlkV+bj8lqAiBEF8LUuKnkJOQwI34G2QnZTIieQJBQYb19wk9+itFEwIlLx4cLsYuu4nI78FUp5QNernsT+JOU8tNux38MIKX8pWt/G/AzKeX+q91PicvgYGlp4eDmNzn+0XuIIB3z7ljH/LUbCOlDsa6hpLXRyun9FRTsLaep1kJIhJ5pCzVrJi5l6BI2Oltbaf70U0ybN2M+cFBzVOv1hM+di6WggJRnnyHq5pv7dU8pJZdbLmtTabV55NbmUlhfSJu9DYAoQxRZCVlkx2dropMwY2QFDAwlKpXNFYwkcXkcmAckAmOA16WUv+rhuhPAFmA1YHHd47AQ4vfAASnlP1zt/gp8JKXc1MM9HgYeBkhLS5tXWlo6CCNUADRWV7H39f/h9BefExZt5PoN9zHz5tXorhKaOxxIp+TymQYK9pZz4UQNTodk7CQjWUtTmDQ3EYOP1TF9wVZRQeN771P/yis46us7jhvS0gjLziY0J4ewnGxCs7L6VFnTE4fTwYXGC+TV5mmvujyK6ouwS63wmAoYUPSVYREXIcSnQE+Fy38ipdziarOLruLyJPA9YD5gBnYAP5VS7uh27zxgJ/B9V9s3gAzg98D+buKyVUr59tX6qiyXoaHy/Fl2/+MlLhXkEjs2laX3f5PJ1y0KyF/Jbc3tnN5fScEX5ZiqzASH6Zm6MJmsJSkkjBuaRJ7uyprGu+7C9OYbRK1ejdPUSFteHvaKCq1RUBAhkyZ1ik12DqFTMxHB/St5bHVYOVN/pov/pqSxpCNCzR0wMCNB89+ogAEFjCzL5V5gtZTym679/wAsUspfd7vuY+AZKeUu1/55YBHwEKhpsUBGSsmFY4fZ/erL1JddInVaFjc+8CBjp0wd7q71iJSS8rMmCvaWc/5YDQ67k8T0aGYsTWHyvESCQwfH+uqpZLPnvr22lrbcXCy5ebTlae+OhgYAhMFAyLRphOXkdIhO8MSJCF3/LK+W9hYK6grIrc0lv06bVqto1UQtSAQxKWZSF//NlNgpVwQMvJT3Etnx2aMj08Aowl/fy0gSl1g0a2UJ0A58DDwnpfyw23XfBlKklP+PECLTdU0akAX8k06H/g5ginLoBx5Oh4PcndvZ99armBtNZF6/lKX3fYOYpJ6M3cDA0mrjzIFK8veW01DRiiFUR+b8JGYsTfV7fZmrlWyOf+ihK9pLKbGVlWPJy6XtVC6W3Fws+fk4zWYAgsLDCZ0xo9PCycnBkJrab6vRHTCQV6f5b3oKGPCcTqsyV/HU5091ZBYYsZkGRhndvwdfv5eAExchxJeAF9D8KibghJRylevcA8CP0VYybfUIMf4L8Ecp5REhRDDwEjAbTYSelFLudLX7CbARLUT5cSnlR976o8Rl+GhvM3P4/Xc58sE7OO0OZq+6g0V3fYWwqMDNjiulpPJCEwV7yzh3pBq7zcmYtCiylqSQOT/Jb+WYB4p0OGgvLqYtNw9Lbi5teXlYCwuRNi3flC42ltDsbJeFo73rE/oX0SelpKylrIv/pqCuoEvAQGpUKiWNJSxJXcLBioP8aP6PWJK6BGOIkWBd/6bvFP3HYrdgspposDTQYG3AZDFhsprIr8tnW8k2FiQvIK82zyfBDzhxCTSUuAw/LfV17HvrVfI++5TgsDAWfuke5qy+E30/fQdDjdVso+hQFfl7y6m73II+OIgp1yWRtTSFpPTogPMnyfZ2LEVnseSe6hAd6/nzWtoSQD92bNeAgexsdFH9s8p6ChgorCvs8N94EqYPwxhiJCYkBmOwkeiQ6C77xhCPV7CRmFDtuGE0JqXsA1aHlQZLAyarJhAmi6lDMBqsDR3HTNbO4xbHFcsEOwgOCqbd2c4jMx/h0TmP9rs/Sly8oMQlcKi9WMLuf/6N4uNHiB6TyJJ7v860xcuuWislEJBSUl3aTMGeMoqOVGO3OohPjSBrSSpTFyYREh64fwydra1YCgu7WDi2i53Fv4LT011io1k4odOnX5FN4GocqjjEE58/wc1pN7OtZBv3T7+fxPBETFYTjdbGzle79m6ymmiyNnVEr/VEmD5MEyCX6HiKUExIDNHB0V323e0GIkr+9h+1O9o7LApPMXALR8dxSwON1kYarA0dFmFPRAVHERsSS0xoDLEhsRhDjF32Y0JjiAnp3D5Tf4Z/2/1v3DP1Ht4886ayXAYDJS6Bx8W8k3z+95eoLjlPUsZkbnxgI+NnzPR+YQDQbrFz9nAVBXvLqS5tRmcIYvK8RLKWpDB2kjHgrJmecJhMtOXld7Fw7DU12km9npApU1wWjjadFjJ5MqKHSqW+zu1LKTHbzVcKUA8i1Nje2NHOmyiF68OvsIY8BSg6OLpTjDzaGHSGq45lTuKcqwtEd6vC0oDZbu61n1GGqA4xiAmJITY0tuO9QzQ8jhtDjOiD+j4dO2p9LoGGEpfARDqdFH7xOXtf/x+aa2vImDufZfd/i/hxacPdtT5Tc7GZgr3lFB2qpN3iIDY5nKwlKUxbNJbQyMC1ZnrCVlWlWTYeFo6zqQkAERJC6PTpXQIGgidMYNt/fYcxcxcz7/ZvdNzn6NZXqDm2j9U/fdHvfZRS0mprvVKAXNvu492FqtHaiOMqcT9uUTIEGShvLSc5IpmKlgpiQ2KxOCy02Fp6vTbCENHFYuiwKkK7CkSHeAzBtN/H//mIX74XJS5eUOIS2NjarRz/6H0OvvsmNouFnBUrWXzP/UTEDE4BsMHAZnVw7mgV+XvKqSpuIkgvmDRHs2ZSM2NGhDXTHSkltosXtei0PJfoFBQgLdocf1BUFIbx42i/UEz8Qw9hXL+e9pJiyn/0VEdIdaDgKUqeVlCH5eQhQqfrT1NlrmJc5DhmJc7qYkVcMRUVEhOQ/iFvoe59RYmLF5S4jAzMTY0ceOd1Tm7fik5v4Lo772L+nXdh6Mf8fyBQV9ZC/t5yig5WYjXbMSaGkXVDCtOuH0t4dGAHMHhD2u1Yz5/vYuFYTp/uCBgAEKGh6MeMQRcXiz4mFl1cnLYdG4su1mM7Lg5dXBxBEREBI76emap99VMMJU6rFYfJhMPUiKPRhKOxEYfJhLOxEUthIc2ffEr4woVY8vJ8EnwlLl5Q4jKyaKgsZ+8/X6Ho4BdExMax+O77yV5+y7DVkPEVe7uD88eqyd9bTsW5RoJ0gomzEpixJJWai00kTjReNcPzSMFptVL581/Q+M47hC9YQMi0qTjqG3DU12NvaMDRoG3L9vYerxcGAzqX2OjjYtF5E6SYmH4vGO0L/vJT+MLVRMK9rZ3z2G9s7LAie8RgQBj0SHMbCd/9DmMee6zf/VLi4gUlLiOT8qJCPv/7S5QXFRI/Lo3kyZlMX7qcCdmzOtpczDtF5fkiFqzbMIw99U59RSsFX5RzZn8lllYb4dEG2i0Obv7mdCbPTbqiVs1Iwj3lEnvfvTS89nqPv5CllDhbzThMLtGpr8fRYMJRX48g3fKMAAAW8klEQVSjwSVCbkEyadvO5uaeP1AIdNHRHZaPLjYGfazHdlzcFYLUl+g3f/gprhAJlwj0KBKuc30RCV2MEX1MDEFGIzpjDDqjEV2M6929HWPssm8+dYryH/zwqt+LN5S4eEGJy8hFSsnZQ/vY88+/YaqsIEinY8W3vs2sW2/jYt4pPvjdM6x5/GnSskdGpJnD5uTCiRry95ZTdkZL5xJuDMZqtjP7lvFMX5xCdEJowEwTecNfc/s9IdvbsZtMHZaPo6EBu0uAHCaP7YaGDgsJR89OexEejj4mpgeLqFOQbOUV1LzwAim//CURNyym+bNdVP6v/0XCd7+LIWXs4IhEjIdYdBcJl3iIsLB+/39QPpchQonLyMdht3Hyk4/Z+8bfsbWZiUsdR0t9PWuf+Hcm5Mwe7u75hKnazM7/KaTiXCMiqLN6bliUgaT0aJImRpM00UhiejQhAZIVoDv9TWMzmEinE2dTU5epOE+LqCdBkm29ryvpDWEwEDREIuEr/vpelLh4QYnL6MFqbuXdZ35O2ZkCAKLHJJJ9063MuOkWohN6r1cfiLinwrKXpZK3u4xF6zKQEqqKG6kqbqKh0rVOQkBscgRJE6NJnqiJTtzYCIJ0gb3wdCTgbGvrtIgaNNExbd6Ced8+IpYtI+auu4ZVJIabq4lLYP7cUSh8pOrCeerLL7Ng/d2c2PYBYVHR7HvrVfZt+icTZ80lZ8UqMubNR6cPvPBQT7r7WFKnxnbsZy9LBbTUM9UlzVQWN1JV0kTJyVpO79OyFutDdCRNiOqwbpImRhNhDBnOIY1IgsLCCAoLw5CSAmi/7q2FhSR89zs0vPY6uo0bAyqkOpBQlgvKchktdPexuPeXf/Nh6ssvk/fZJ7TU1xFujCFr2QpyVqwkLmXccHe7R45tKyUxPbpf0WJSShpr2qgqbnK9Gqm93ILToT3jkXEhJKUbSc6IJik9mjFpUeiHsADaSGcw/UcjFTUt5gUlLqODQ1s2kTwps4vz3jNazOl0UHLyGLk7tnPh2CGcDgep02aQs2IlmYtuwBAystbL9AW7zUHtpRYqL2jWTdWFJprrXYscgwTx4yK1qbQMI0np0RgTr50pnf4SSP6jQEGJixeUuFx7tJoayP98B3mfbaehopzgsHCmL7mJnBUrScqYPNzdG1RaG62aZVOiWTdVJc3YrVoUVUiEvot1k5geTWhEYE8hKvqPL5ZxTyhx8YISl2sXKSVlhfnk7txG0YEvsNvaSUyfRM6KlUxbciOhEUNT0ng4cTolDRWtndZNcRP1Fa24M+THJIV3BAokTTQSn6qCBUY63X16vq6jUuLiBSUuCgBLawun937OqZ3bqCm5gN4QTOaiG8hZsYrU6TOuqemi9jY7VaVNXfw3bc1akTG9IYgxE6JImmjsEJ3I2NE3pThakU6Jtc1OSW4te944y+R5iVw4UePTAl0lLl5Q4qLoTtWFc+Tu3E7h3l20t5mJHZtC9vKVzLjx5hGVMNNfSClprrNQVdykRacVN1FzqRmnXfv7ERET4rJsokmeaGTMhCgMwTq/Tb8oesfhcGJpsWFpsdHW3E5bi422ZhuWlh62Xe2ks+vf/etuT2fh2ox+f7YSFy8ocVH0hs1qoejAF+Tu3E7Z6XyCdDoy5i4g5+aVpM+aO+LymfkTh81J7eWWDrGpKm6kqVYLFhBBgvjUCCJjQyk708Cye6cwdeFYys6aRmwam6HCZnXQ1tLuEgvbFduaWHQet5p7qV8jIDTcQGikgbAoA2GRwYRGGQiL1LbNze3k7bpM5sJkzh2tVpbLYKDERdEX6soukffZJ+R/voO2pkYi4+LJXn4r2TfdijExabi7FxC0Nbd3sW6qS5pot2jBAkInkE5JeHQwoREG9IYg9ME69IYgdB7bekMQOvd2cBB6g851XtvWzndu64Nd1xt0rjZBg+IT8ik83DUF5WlVuIWhJ+GwNNuw25w93itIJzShiAx2iYWB0O7bkQaXgAQTGqHv9d9B+VyGCCUuiv7gsNu4cPQwuTu3UXzyGAATcmaTs2Ilk65bhL6HiozXKtIpaag0s+/dc5Tm1pEwLpLYsRE4bE7sNgf2did2mxOHx7bd5sDh2vaVoCDhEqBO0ekuQDqPbb1B13N7DxFrqGzl0Acl3LBhMknp0VwsqOPwhyVMv34sIeH6HoSj5ykoN/oQncuK6CoSYVHBLhHx2I4KJjhU5ze/n4oWGyKUuCh8pam2mvxdO8j9bDvNtTWERkUzY9lyspevJGG88inAlWls+vrrWEqJw+7E3u70KkYdbdpd+zanS6C0be1857bd5uixvbMXIegLIRF6zaqINHQIgqdIdBeO0bCAVYmLF5S4KAaK0+ngYu5Jcndu59zhAzgddsZmTiNnxUqmXr+U4NCw4e7isOCv6Zehwulw9iJAnRZV4f4KLhyvIXNhMnNXpmmCcZUpqNGMEhcvKHFR+BNzUyMFu3eSu3M79WWXMISGMe2GZeSsWEnypMxrKqR5tEWL+WqFjVaUuHhBiYtiMJBSUl50mtyd2zizfw92q5WEtHRyVqxk+tLlhEVGDXcXFf1gpFlhQ4ESFy8ocVEMNlazmTP7dpO7cxuV58+iMxiYsmAxOStWMj4rBxF07U2pjDRGmxXmD5S4eEGJi2IoqS65QN5nn1CwZyfW1laMScnkuBZoRsbFD3f3FIo+o8TFC0pcFMOBrd3KuUP7yd25nUv5pxAiiIlzryNnxSrqLl9k7OSpvWZ4VigCAVUsTKEIQAzBIUxfchPTl9xEQ2W5tkBz16dcOHqI0Mgo7O1Wbn34UbKWruhSq0ahGAkoywVluSgCB4fdTvGJo+Tu+JgLx44AknBjDO1tZq7f8FXmrF4zKuvOKEYmalrMC0pcFIFIc30tH/3hOS7lnUQIgZQSncFA6tTppOXMYULObBInZlzT+c0Uw4uaFlMoRiAN5eXUlhaz6Mv3cmL7VubfeRfmpkYunjrO3tdeYe9rrxAaGUVa9iwm5MxmwszZGBOTh7vbCgWgxEWhCEg8fSxp2TMZnzWzY/+mrz1Iq6mBi7knKM09SWnucYoO7AUgJmksaTmzmDBzDmkzZhEaOfqLnSkCEzUthpoWUwQeh7ZsInlSZp+ixaSU1JddpjT3BKW5x7mUn4vN0oYQQSRlTGLCTG0KbWzmdJVUU+FXAs7nIoS4G/gZMB1YIKU84nFuJvAiEA04gflSSksP9/hX4FHADnwopXxKCHEr8AwQDLQDP5JS7vTWHyUuitGEw26n8lwRpbnHKT11gopzZ5BOJ/qQEMZNz9am0HJmk5CWfk2lolH4n0AUl+lowvEi8KRbXIQQeuAY8DUp5UkhRDxgklI6ul2/HPgJcIeU0iqESJRSVgsh5gBVUspyIUQ2sE1KmeqtP0pcFKMZq7mVSwV5lJ46TmnuCRrKLwMQboxhQs5s0lz+mqi4hGHuqWKkEXAOfSllIdDTr6aVwCkp5UlXu7pebvEd4BkppdXVrtr1ftyjTT4QKoQIcbdTKK5FQsIjmHzdQiZftxCAptoal79GexXu3QVAXOr4jsCA8Vk5BIeFD2OvFYNJf6ZdfSXQHPqZgBRCbAPGAK9LKX/VS7ulQoj/D7CgWT+Hu7X5MnBcCYtC0ZXohDFaBc3ltyKdTmoullCae4KLuSfI3bmd4x+/T5BOR/LkqS6xmUPypCno9IH250LhK8mTMvngd8+w+tEfkpwxhdqLpX5fpDto02JCiE+BnuIifyKl3OJqs4uu02JPAt8D5gNmYAfwUynljm73zgN2At93tX0DyJCuwQghZgDvASullOd76d/DwMMAaWlp80pLSwc0XoViNGBvb6e8qFCzak6doKr4HEhJcFgY42fM7JhGi0sZp/w1IwRbu5XGygoaKssxud8ryqm5XIqlqYmE8RNoNTV0RCb2h2GZFpNS3uLDZZeBz6WUtQBCiK3AXDSR6d7uHZeYHBJCOIEEoEYIMQ54F/h6b8Li6t+fgD+B5nPxoa8KxahDHxxMWvYs0rJnsfS+b9DW3MSl/FOUntIi0c4fOQhAZHxCR2DAhJzZhBtjhrnn1zY2q4XGqkoaKstpqCjHVFWBqaKchqoKWupqu7QNizYSkzyWjNnX0VRbzeWCPBZ9+d5+C4s3As3O3QY8JYQIR4v2uhF4rod2m4EVwC4hRCZadFitECIG+BD4sZTyiyHqs0IxagmLiiZz0RIyFy0BwFRV6QoMOM75wwfI3/UpAGPS0kmbOYf0nNmkTp/RkaJmKOb2rxVsVgumqkpM/RCQtBkziUkeS2xyCrFjUzEmJRMaoa19cq+lWvTlezm5fSvjs2b6VWCGK1rsS8ALaH4VE3BCSrnKde4B4MeABLZKKZ9yHf8L8Ecp5REhRDDwEjAbTYSelFLuFEL81HXtWY+PW+l2+PeGihZTKPqP0+mg+sL5jsCA8jMFOOx2dHo9KVOzmJAzm5CICPa9+WrHlEv3xaGKrvgiILHJKb0KSG90/x58/V4CLhQ50FDiolAMHJvFwuXT+VpwwKnj1FwsAcAQGobTbmP8jJmUFxWycP09pEzLIjg0jOCwcIJDQzGEhaE3BF8TfpxeBaSynJb6rgGyngLSISJ9FJCr4S+LUomLF5S4KBT+xzNFTdHBL7BZ2q7aXgQFERwWRnBouOs9DENYGMGhoa7tzuMd5z22g8O0l8ElWv7ORtCfP8hXCIiHM72vAhKTPJaQ8Ai/jsHfBNw6F4VCMfqJiIll+tLlRMTGc+HYIXKW30r+7p3ccM/9xI5NxWax0G5po73NTHtbm2u7DZulc7vd0obZ1NC539aG02Hv0+cH6fQdVlGn+IR3bBtCXcIVFt4pUr2JV1hYR/iue+rowvEjbH3hN8y7fS2Htmzqk4CkZc8acQLiK8pyQVkuCsVg4a+5fU8cdluH0HQIUptZ27ZYOgXKfaytrUchs7nenQ6H9w8FdHo9QYZg7JY29CGhV1hiI9kC8RVluSgUimGh8nxRFyFJy57JmsefpvJ8kc/iotMbCIsyEBYVPeD+SSlx2O20t5ldgtR2VSuqva2NssI8ai+VMi4rh1m3rB71AuIrSlwUCsWg0ZNzOC3bvyGvA0EIgd5gQG8wQrTRa/uLeaco2r+nI3w33BhLUsbkIejpyCNouDugUCgUIwHPKb0b7nmANY8/zQe/e4aLeaeGu2sBiRIXhUKh6ANXm+JTXIly6KMc+gqFQuELV3PoK8tFoVAoFH5HiYtCoVAo/I4SF4VCoVD4HSUuCoVCofA7SlwUCoVC4XdUtBgghKgBBqMUZQJQ67VVYKPGMPyM9P6DGkOg4O8xTJBSjunphBKXQUQIcaS3ML2RghrD8DPS+w9qDIHCUI5BTYspFAqFwu8ocVEoFAqF31HiMrj8abg74AfUGIafkd5/UGMIFIZsDMrnolAoFAq/oywXhUKhUPgdJS4KhUKh8DtKXHxACDFeCPGZEKJQCJEvhPi+63icEOITIcRZ13tsX68dKf33uIdOCHFcCPHB0PW8y+cPaAxCiBghxCYhxGnXPa4f2hH4ZQw/cF2XJ4R4TQgROrQjuOoY7nbtO4UQvYa+CiFWCyHOCCHOCSGeHrqed+mDz2MI8Oe5T9+Bq63/n2cppXr18wWMBea6tqOAIiAL+BXwtOv408Czfb12pPTf4x4/BP4JfDDSvgPXuVeAh1zbwUDMSBoDkAoUA2Gu/TeBbwbQGKYDU4FdwHW9XKsDzgMZru/g5FA/C34YQyA/z17773EPvz/PynLxASllhZTymGu7GShEe9jXof3RwvW+vh/XDhkD6T+AEGIccAfwl8Hvbc8MZAxCiGhgGfBX1/XtUkrTUPTbk4F+D2hlysOEEHogHCgf3B5fSW9jkFIWSinPeLl8AXBOSnlBStkOvI429iFlIGMI5Oe5j9/BoD3PSlwGiBAiHZgDHASSpJQVoH3hQGI/rh0WfOz/74CnAOcQdNErPowhA6gBXnZNBfxFCBExRN3tkf6OQUpZBvwGuAhUAI1Syu1D1d+e8OH/cypwyWP/MkP8h7k7A3kmA/B57iuD8jwrcRkAQohI4G3gcSll01Bd6y986YMQYg1QLaU8Oqid6yM+/jvqgbnA/5VSzgFa0aafhgUfv4dYtF/5E4EUIEII8cDg9dJrf3z5HkQPx4ZtbYR6nv2LEhcfEUIY0L7IV6WU77gOVwkhxrrOjwWq+3HtkDKA/t8ArBVClKBNY6wQQvxjCLp8BQMYw2XgspTS/etuE5rYDDkDGMMtQLGUskZKaQPeARYPRZ+7M4D/z5eB8R774xiGqT0Y2DMZwM9zXxi051mJiw8IIQTafH2hlPK3HqfeA77h2v4GsKUf1w4ZA+m/lPLHUspxUsp04F5gp5RyyH8xD3AMlcAlIcRU16GbgYJB7G6PDGQMaNNhi4QQ4a773Iw21z6kDPD/82FgihBiohAiGO3/03v+7qM3BjKGAH+evTKoz7O/IgOupRewBM18PwWccL1uB+KBHcBZ13ucq30KsPVq146U/ne7z00MX7TYgMYAzAaOuK7fDMSOwDH8HDgN5AF/B0ICaAxfQrNMrEAVsK2XMdyOFt10HvhJgP1f8jqGAH+e+/QdeNzHr8+zSv+iUCgUCr+jpsUUCoVC4XeUuCgUCoXC7yhxUSgUCoXfUeKiUCgUCr+jxEWhUCgUfkeJi0KhUCj8jhIXhUKhUPgdJS4KxTWAEOIFIcQxIcR81/50IcQfhVbT5jvD3T/F6EOJi0IxynFlfE4EHgHWAEgtHfu3gXuAqxaSUih8QYmLQtEHhBC7hBCruh17XAjxf65yTcvg9+yKzwwTQnwuhNC5j0kpW9EKSu0CnvdouxbYi5ZiBiFEsBBit6s+jEIxIJS4KBR94zW0xH6e3Os6HkhsBN6RUjrcB4QQ8WjFxJqBjuNSyveklIuB+1377WhC85Uh7bFiVKLERaHoG5uANUKIEOgoypQC7BVC/FBodezzhBCPd79QCJEuhMjz2H9SCPEzj3OnXQXL8oQQrwohbhFCfCGEOCuEWOBq94AQ4pAQ4oQQ4kVPy6Qb93NlFuWfohUWy0crf4sQ4iYhxPNCiBeBrR5tN7vuoVAMCCUuCkUfkFLWAYeA1a5D9wJvoNWB+RawEFgE/IsQYk4/bz8Z+N/ATGAa8FW0TLdPAv8uhJiOZk3cIKWcjWZ9XCEArrT1GVLKEo9j6Wh1Xt5AS8k/wzWeXVLKx6SUj0gp/+Bxmzxgfj/7r1BcgRIXhaLveE6NuafElgDvSilbpZQtaEW7lvbzvsVSylwppRPNutghtXTluUA6Wq2WecBhIcQJ135GD/dJAEzdjv0n8AvX/TrEpTdc02ntQoiofo5BoeiCctwpFH1nM/BbIcRcIExKeUwIsawP19np+kMutNt5q8e202PfifaMCuAVKeWPvXxOm+e9hRCzgbuAJUKIP7jO5fahvyGApQ/tFIpeUZaLQtFHXJbJLuAlOh35u4H1roqQEWgFmvZ0u7QKSBRCxLt8Nmv6+dE7gA1CiEQAIUScEGJCD/1rAHRCCLfAPAvcKaVMl1qlwVl4sVxczn936WSFwmeU5aJQ9I/X0Ka+7gVwWS9/Q/PHAPxFSnnc8wIppU0I8QvgIFCMVj2yz0gpC4QQPwW2CyGCABvwPaC0h+bb0SwVJxAhpdzhcZ8qIUSEECJOSlnfy8ctp6uDX6HwCVWJUqEYRbiCCX4opfyaj9e/A/xYSnnGvz1TXGuoaTGFYhThspo+u0qocq+4os02K2FR+ANluSgUCoXC7yjLRaFQKBR+R4mLQqFQKPyOEheFQqFQ+B0lLgqFQqHwO0pcFAqFQuF3lLgoFAqFwu/8/xvWSx4VBzxDAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for pr_encut in pr.iter_groups():\n", " volume_lst, energy_lst = energy_volume_pairs(pr_encut)\n", " plt.plot(volume_lst, energy_lst, 'x-', label=pr_encut.base_name)\n", "plt.xlabel('Volume ($\\AA ^ 3$)')\n", "plt.ylabel('Energy (eV)')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fitting\n", "After we created multiple datasets we can now start to fit the converged results. While it is possible to fit the results using a simple polynomial fit we prefer to use the phyiscally motivated birch murnaghan equation or the vinet equation. For this we create the Murnaghan object and use it is fitting functionality:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.264136Z", "start_time": "2019-09-04T12:58:37.015213Z" } }, "outputs": [], "source": [ "murn = pr.create_job(job_type=pr.job_type.Murnaghan, job_name='murn')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Birch Marnaghan" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.310070Z", "start_time": "2019-09-04T12:58:37.302889Z" } }, "outputs": [ { "data": { "text/plain": [ "[-10938046483227.81, -160.21766207685127, 4.0, -21876092966476.383]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[e0, b0, bP, v0], [e0_error, b0_error, bP_error, v0_error] = murn._fit_leastsq(volume_lst=volume_lst,\n", " energy_lst=energy_lst,\n", " fittype='birchmurnaghan')\n", "[e0, b0, bP, v0] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vinet" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.322375Z", "start_time": "2019-09-04T12:58:37.311758Z" } }, "outputs": [ { "data": { "text/plain": [ "[-16.62393775939236,\n", " 361.18208413366904,\n", " -8.873197550150648,\n", " 21.173041264923626]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[e0, b0, bP, v0], [e0_error, b0_error, bP_error, v0_error] = murn._fit_leastsq(volume_lst=volume_lst,\n", " energy_lst=energy_lst,\n", " fittype='vinet')\n", "[e0, b0, bP, v0] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that both equation of states give slightly different results, with overall good agreement. To validate the agreement we plot the with with the original data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.530517Z", "start_time": "2019-09-04T12:58:37.324021Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "vol_lst = np.linspace(np.min(volume_lst), np.max(volume_lst), 1000)\n", "plt.plot(volume_lst, energy_lst, label='dft')\n", "plt.plot(vol_lst, murn.fit_module.vinet_energy(vol_lst, e0, b0/ 160.21766208, bP, v0), label='vinet')\n", "plt.xlabel('Volume ($\\AA ^ 3$)')\n", "plt.ylabel('Energy (eV)')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Murnaghan Module \n", "Besides the fitting capabilities the Murnaghan module can also be used to run a set of calculations. For this we define a reference job, which can be either a Vasp calculation or any other pyiron job type and then specify the input parameters for the Murnaghan job." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.644025Z", "start_time": "2019-09-04T12:58:37.532255Z" } }, "outputs": [], "source": [ "job_vasp_strain = pr.create_job(job_type=pr.job_type.GpawJob, job_name='gpaw')\n", "job_vasp_strain.set_encut(320)\n", "job_vasp_strain.structure = basis.copy()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.895833Z", "start_time": "2019-09-04T12:58:37.675200Z" } }, "outputs": [ { "data": { "text/html": [ "
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ParameterValueComment
0num_points11number of sample points
1fit_typepolynomial['polynomial', 'birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet']
2fit_order3order of the fit polynom
3vol_range0.1relative volume variation around volume defined by ref_ham
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" ], "text/plain": [ " Parameter Value \\\n", "0 num_points 11 \n", "1 fit_type polynomial \n", "2 fit_order 3 \n", "3 vol_range 0.1 \n", "\n", " Comment \n", "0 number of sample points \n", "1 ['polynomial', 'birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet'] \n", "2 order of the fit polynom \n", "3 relative volume variation around volume defined by ref_ham " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "murn = pr.create_job(job_type=pr.job_type.Murnaghan, job_name='murn')\n", "murn.ref_job = job_vasp_strain\n", "murn.input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We modify the input parameters to agree with the settings used in the examples above and execute the simulation by calling the run command on the murnaghan job object." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.933789Z", "start_time": "2019-09-04T12:58:37.931262Z" } }, "outputs": [], "source": [ "murn.input['num_points'] = 7\n", "murn.input['vol_range'] = 0.03" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:37.983849Z", "start_time": "2019-09-04T12:58:37.935426Z" } }, "outputs": [ { "data": { "text/plain": [ "ase.atoms.Atoms" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(murn.structure)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:58:38.465443Z", "start_time": "2019-09-04T12:58:37.985315Z" } }, "outputs": [ { "data": { "text/html": [ "
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} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The job murn was saved and received the ID: 3606074\n", "The job strain_0_97 was saved and received the ID: 3606075\n", "The job strain_0_98 was saved and received the ID: 3606084\n", "The job strain_0_99 was saved and received the ID: 3606089\n", "The job strain_1_0 was saved and received the ID: 3606092\n", "The job strain_1_01 was saved and received the ID: 3606099\n", "The job strain_1_02 was saved and received the ID: 3606106\n", "The job strain_1_03 was saved and received the ID: 3606112\n", "job_id: 3606075 finished\n", "job_id: 3606084 finished\n", "job_id: 3606089 finished\n", "job_id: 3606092 finished\n", "job_id: 3606099 finished\n", "job_id: 3606106 finished\n", "job_id: 3606112 finished\n" ] } ], "source": [ "murn.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afterwards we can use the build in capabilites to plot the resulting energy volume curve and fit different equations of state to the calculated energy volume pairs." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:39.624624Z", "start_time": "2019-09-04T12:59:39.574891Z" } }, "outputs": [ { "data": { "text/html": [ "
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volumeenergyerroridequilibrium_b_primeequilibrium_bulk_modulusequilibrium_energyequilibrium_volume
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" ], "text/plain": [ " volume energy error id equilibrium_b_prime \\\n", "0 20.172969 -16.576797 0.0 3606075 -8.102283 \n", "1 20.380937 -16.593942 0.0 3606084 -8.102283 \n", "2 20.588906 -16.607049 0.0 3606089 -8.102283 \n", "3 20.796875 -16.616336 0.0 3606092 -8.102283 \n", "4 21.004844 -16.622714 0.0 3606099 -8.102283 \n", "5 21.212813 -16.623909 0.0 3606106 -8.102283 \n", "6 21.420781 -16.620513 0.0 3606112 -8.102283 \n", "\n", " equilibrium_bulk_modulus equilibrium_energy equilibrium_volume \n", "0 359.180621 -16.623924 21.172823 \n", "1 359.180621 -16.623924 21.172823 \n", "2 359.180621 -16.623924 21.172823 \n", "3 359.180621 -16.623924 21.172823 \n", "4 359.180621 -16.623924 21.172823 \n", "5 359.180621 -16.623924 21.172823 \n", "6 359.180621 -16.623924 21.172823 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "murn.output_to_pandas()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:39.850020Z", "start_time": "2019-09-04T12:59:39.628014Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "murn.plot()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:39.919616Z", "start_time": "2019-09-04T12:59:39.851944Z" } }, "outputs": [ { "data": { "text/plain": [ "{'fit_type': 'vinet',\n", " 'volume_eq': 21.173041264923626,\n", " 'energy_eq': -16.62393775939236,\n", " 'bulkmodul_eq': 361.18208413366904,\n", " 'b_prime_eq': -8.873197550150648,\n", " 'least_square_error': array([2.35206844e-04, 2.03161020e+01, 3.15362473e+00, 7.39621643e-03])}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "murn.fit_vinet()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Common mistakes " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Not copying the basis " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important to copy the basis before applying the strain, as the strain has to be applied on the initial structure, not the previous structure:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:39.960916Z", "start_time": "2019-09-04T12:59:39.921187Z" } }, "outputs": [], "source": [ "volume_lst_with_copy = []\n", "for strain in np.linspace(0.97, 1.03, 7):\n", " basis_copy = basis.copy()\n", " basis_copy.set_cell(cell=basis.cell * strain ** (1/3), scale_atoms=True)\n", " volume_lst_with_copy.append(basis_copy.get_volume())" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:39.972769Z", "start_time": "2019-09-04T12:59:39.962410Z" } }, "outputs": [], "source": [ "basis_copy = basis.copy()\n", "volume_lst_without_copy = []\n", "for strain in np.linspace(0.97, 1.03, 7):\n", " basis_copy.set_cell(cell=basis_copy.cell * strain ** (1/3), scale_atoms=True)\n", " volume_lst_without_copy.append(basis_copy.get_volume())" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:39.998610Z", "start_time": "2019-09-04T12:59:39.974290Z" } }, "outputs": [ { "data": { "text/plain": [ "([20.17296874999999,\n", " 20.380937499999995,\n", " 20.588906250000004,\n", " 20.796874999999996,\n", " 21.004843749999992,\n", " 21.212812500000016,\n", " 21.42078124999999],\n", " [20.17296874999999,\n", " 19.769509374999995,\n", " 19.571814281250003,\n", " 19.571814281250003,\n", " 19.76753242406251,\n", " 20.162883072543767,\n", " 20.767769564720073])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "volume_lst_with_copy, volume_lst_without_copy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rescaling the cell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another common issue is the rescaling of the supercell, there are multiple options to choose from. We used the option to scale the atoms with the supercell." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:40.083034Z", "start_time": "2019-09-04T12:59:40.000103Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a84b0c508d5e406e87b2ee37e998843b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "NGLWidget()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basis_copy = basis.copy()\n", "strain = 0.5\n", "basis_copy.set_cell(cell=basis_copy.cell * strain ** (1/3), scale_atoms=True)\n", "basis_copy.plot3d()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A nother typical case is rescaling the cell to increase the distance between the atoms or add vacuum. But that is not what we want to fit an energy volume curve." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:40.152204Z", "start_time": "2019-09-04T12:59:40.084706Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c5bdfdfffd644104bc69b1e9a3a82c3f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "NGLWidget()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basis_copy = basis.copy()\n", "strain = 0.5\n", "basis_copy.set_cell(cell=basis_copy.cell * strain ** (1/3), scale_atoms=False)\n", "basis_copy.plot3d()" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-10-16T19:33:52.089315Z", "start_time": "2018-10-16T19:33:52.085882Z" } }, "source": [ "The same can be achieved by setting the basis to relative coordinates." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:40.221411Z", "start_time": "2019-09-04T12:59:40.153745Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a9163f8a6ac6431691cf03e4a3c602ed", "version_major": 2, "version_minor": 0 }, "text/plain": [ "NGLWidget()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basis_copy = basis.copy()\n", "strain = 0.5\n", "basis_copy.set_relative()\n", "basis_copy.cell *= strain ** (1/3)\n", "basis_copy.plot3d()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2019-09-04T12:59:40.287442Z", "start_time": "2019-09-04T12:59:40.222918Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "59f0aa755e874b4491c33c49d796547c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "NGLWidget()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basis_copy = basis.copy()\n", "strain = 0.5\n", "basis_copy.cell *= strain ** (1/3)\n", "basis_copy.plot3d()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }