{ "cells": [ { "cell_type": "markdown", "id": "a6d5515e", "metadata": {}, "source": [ "# BigDFT for solid state systems\n", "\n", "In this notebook, a simple example of a solid state calculation is given by considering two-dimensional (2D) materials. In order to demonstrate the robustness and the flexibility of BigDFT, two case studies of graphene are presented, i.e. with cubic and linear scaling. \n", "Each calculation is initialized with the proper boundary conditions as the code natively handles dimensionality. The density of states (DoS) and the band structure are then computed within the cubic scaling approach, while the former is compared to the DoS obtained by linear scaling calculation" ] }, { "cell_type": "code", "execution_count": 27, "id": "a145be73", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "sq3 = np.sqrt(3)" ] }, { "cell_type": "markdown", "id": "32b33f2c", "metadata": {}, "source": [ "## Graphene\n", "\n", "[Graphene](https://en.wikipedia.org/wiki/Graphene) is a 2D carbon allotrope in the form of a honeycomb network that consists of a two inequivalent triangular lattices, with a C-C bond of 1.42$~$angstroem, or equivalently, a lattice parameter such that $a_0 = 2.46~$angstroem.\n", "\n", "In BigDFT, the $y$-axis is set to infinity to simulate a 2D materials (or slabs) while orthorombic cells are employed to model hexagonal lattices. Note that the yaml input format requires `float` when using numpy data types." ] }, { "cell_type": "code", "execution_count": 28, "id": "88fbeccc", "metadata": {}, "outputs": [], "source": [ "from BigDFT.UnitCells import UnitCell\n", "\n", "a0 = 2.5 # A\n", "cell = UnitCell([float(a0*sq3),float(np.inf),a0], units='angstroem')" ] }, { "cell_type": "markdown", "id": "bea42050", "metadata": {}, "source": [ "The position of the atoms are then specified in fractional coordinates, while reduced coordinates should be preferably employed when using fully periodic boundary conditions." ] }, { "cell_type": "code", "execution_count": 29, "id": "38d58500", "metadata": {}, "outputs": [], "source": [ "from BigDFT.Atoms import Atom\n", "\n", "at1 = Atom({'r':[0.000000,0.0,a0/2], 'sym':'C', 'units':'angstroem'})\n", "at2 = Atom({'r':[a0/2/sq3,0.0,0.00], 'sym':'C', 'units':'angstroem'})\n", "at3 = Atom({'r':[a0*sq3/2,0.0,0.00], 'sym':'C', 'units':'angstroem'})\n", "at4 = Atom({'r':[a0*2/sq3,0.0,a0/2], 'sym':'C', 'units':'angstroem'})" ] }, { "cell_type": "markdown", "id": "3e869782", "metadata": {}, "source": [ "The system is then constructed from the cell and the atomic coordinates, where we divide the two sublattices into a fragment each." ] }, { "cell_type": "code", "execution_count": 30, "id": "16d3d7ae", "metadata": {}, "outputs": [], "source": [ "from BigDFT.Systems import System\n", "from BigDFT.Fragments import Fragment\n", "\n", "graphene = System()\n", "graphene.cell = cell\n", "graphene['C:0'] = Fragment([at1,at3])\n", "graphene['C:1'] = Fragment([at2,at4])" ] }, { "cell_type": "markdown", "id": "952c2c1b", "metadata": {}, "source": [ "For clarity, the atomic positions are printed and displayed." ] }, { "cell_type": "code", "execution_count": 31, "id": "6c14106c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'C': [0.0, 0.0, 1.25], 'frag': ['C', '0']}\n", "{'C': [2.1650635094610964, 0.0, 0.0], 'frag': ['C', '0']}\n", "{'C': [0.7216878364870323, 0.0, 0.0], 'frag': ['C', '1']}\n", "{'C': [2.886751345948129, 0.0, 1.25], 'frag': ['C', '1']}\n" ] } ], "source": [ "[print(i) for i in graphene.get_posinp()['positions']];" ] }, { "cell_type": "code", "execution_count": 32, "id": "8c20fbdd", "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ "
\n", "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphene.display();" ] }, { "cell_type": "markdown", "id": "866390a9", "metadata": {}, "source": [ "In order to properly characterize extended systems in BigDFT, a finer grid spacing is needed for wavelet calculations while a $k$-points mesh is provided to handle periodicity. " ] }, { "cell_type": "code", "execution_count": 33, "id": "c5117d3c", "metadata": {}, "outputs": [], "source": [ "from BigDFT import Inputfiles as I\n", "\n", "inp_gr = I.Inputfile()\n", "inp_gr.set_hgrid(0.3)\n", "inp_gr['kpt'] = {'method':'mpgrid', 'ngkpt':[15,1,27]}" ] }, { "cell_type": "markdown", "id": "051d14d3", "metadata": {}, "source": [ "Additionally, mixing is required for the calculation to converge, such that the effect of abrupt changes in the density are smoothened away." ] }, { "cell_type": "code", "execution_count": 34, "id": "d2678955", "metadata": {}, "outputs": [], "source": [ "inp_gr['import'] = 'mixing'\n", "inp_gr['mix'] = {'norbsempty':10, 'rpnrm_cv':1e-10}" ] }, { "cell_type": "markdown", "id": "53a82509", "metadata": {}, "source": [ "Note that `norbsempty` has been reduced from its default value as only a few bands are relevant above the Fermi level. Similarly, a softer convergence criterion is defined. " ] }, { "cell_type": "markdown", "id": "87fac064", "metadata": {}, "source": [ "Now that both the system geometry and the input files were defined, a calculator is instantiated using OpenMP and MPI parallelisation." ] }, { "cell_type": "code", "execution_count": 35, "id": "514f6bc7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " log of the run will be written in logfile: ./log-graphene.yaml\n", " Logfile existing, renamed into: ./logfiles/log-graphene.16:22:06.851.yaml\n" ] } ], "source": [ "from BigDFT import Calculators as C\n", "\n", "calc = C.SystemCalculator(verbose=False,omp=1,mpi_run='mpirun -np 1')\n", "log_gr = calc.run(input=inp_gr,posinp=graphene.get_posinp(),name='graphene',dry_run=True)" ] }, { "cell_type": "markdown", "id": "4bf213da", "metadata": {}, "source": [ "Once the calculation is successfully performed, a log file has been produced. Here, we provide the corresponding log file, owing to the significant computational requirement of the current calculation for a regular workstation." ] }, { "cell_type": "code", "execution_count": 36, "id": "7f698386", "metadata": {}, "outputs": [], "source": [ "from BigDFT import Logfiles\n", "\n", "log_gr = Logfiles.Logfile('log-graphene_kpt.yaml')" ] }, { "cell_type": "markdown", "id": "e0a512ef", "metadata": {}, "source": [ "It is sensible to inspect atomic forces once the calculation is done, to ensure the reliability of the system modeling. A typical value for a converged calculation should be below $10^{-3}~$ Ha/bohr." ] }, { "cell_type": "code", "execution_count": 37, "id": "e68889a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'C': [3.725158813391e-05, 0.0, -1.280528231035e-18]},\n", " {'C': [3.725158813391e-05, 0.0, 2.817162108277e-18]},\n", " {'C': [-3.725158813391e-05, 0.0, -2.048845169656e-18]},\n", " {'C': [-3.725158813391e-05, 0.0, -2.048845169656e-18]}]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_gr.log['Atomic Forces (Ha/Bohr)']" ] }, { "cell_type": "markdown", "id": "d81565d4", "metadata": {}, "source": [ "Note that the `log` attribute enables to convert the `Logfile` into a dictionary." ] }, { "cell_type": "markdown", "id": "1ac01d1f", "metadata": {}, "source": [ "## The electronic properties of graphene\n", "\n", "Let us now analyze the electronic properties of graphene. Our first task will concern the computation of the Density of States (DoS)." ] }, { "cell_type": "code", "execution_count": 38, "id": "ce907872", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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OEdnR2NgYlOBU5Kp27+koyXQiIuSlJtCgRQgRzRhDe+8g6c6pJaCs5HhuOWc2z75/gkqthvNbKCcgbzvBvE6wGmMeNsasMcasyc3NneGwVKTz7GovyrSmpfJSEyJ6BPStb32Lb33rW3aHYau+QRcDQy4ykiZuQOrL5z48D2dcLF9+Zjf9Q7onyB+hnICqgZJRnxcDNTbFoqKIZwRU7E5AOSkJEd1u//XXX+f111+3OwxbtfcOAkx5Cg4gLy2Rf712GduOtvCxH2/Rsmw/hHICeg74a3c13LlAuzGm1u6gVOQ70dZLgiNm5KyXnNQEPXAswrX1Wv++GVOcgvO4fnUxP/3rNTR19XPLT7fS2TcYiPAilp1l2I8DbwOLRKRaRG4XkbtE5C73JS8CR4AK4KfA3TaFqqJMdWsPRZlJiFizwDkpCbT0DDA07LI5MjVT2nqsRJExjRGQx2VL83l4w2o6+4b4w966aT9fJAtIN2wRKcQqEPjAGNPmz2OMMbdMcL8B/m760Sk1OdWtvRSP2g2fmxKPMdDSMzBSlq0ii2cKLi0ACQhgZUkG2cnxvH24mRvXlEz8gCjl1whIRFaKyD+KSO6Y23NE5CWsarWtQL2I/MsMxKlUUBhjON7SM7L+A9YICKCpMzKn4bKzs8nOnlwDzkjT7hkBTXMKzkNEWDU7g701ug40Hn9HQHcBHwf+75jbfwZcgTVVthO4EPimiOw2xvwuQDEqFTRNXQO09QwyLzdl5LYc9+bESC1EeOaZZ+wOwXaeNaDpFCGMtTA/lc3ljQwMuYh3hPJyu338/a6cBzxnjBmpLRSRUuBaYBewzBhzA7ACOAH8baADVSoYPqi3OiIvyk8duS072SrNjdQEpKwpuNgYISUhcGd0LpqVypDLcKSpK2DPGWn8TUCFwMExt33E/ecDxph+AGNMI/BLQPu2qbB0yJ2AFuZHzwjo3nvv5d5777U7DFu19QySkRQ3UngSCPPzrJ+hww26MdUXf9N9CtA25rZzsDaG/mnM7YeBrOmFpZQ99tV0kOmMI3dUT7DUBKsdT6SWYr/99tt2h2C76bTh8aXMfU7QkcaTI6C+wWG++ft9rC7N5KaztTjB3xFQNTB/zG3nA23GmIoxtzsAHXOqsGOM4a2KJs6dm33KO2ERITclgcYIHQEp6JhGGx5fnPEOCtITOTKqNc+z75/gyR1VfPmZ3SOFD9HM3wS0A2tTaAGAiJyHtd7zmpdrl6IdC1QYOtLUTU17HxcuyDntvpyUeJoidASkTk7BBdrc3ORTRkCvH2g4+feD9QF/vXDjbwL6LyAPOCgi27ASj4vTq+IArgbeCUx4SgXPmx80AXDR/NP7CeakJNAUwf3gol37DEzBAczNSeFIU/fIOUH7atq59sxCUhMdbK8c7zCA6OBXAjLG7MIqwz6ONfI5CtxsjNky+joRuQIrUWlvdxV2/vJBEyVZSczOPv1QsuyU+IgtQiguLqa4uNjuMGzV1jNAhnPqjUh9mZubTGffEE1dA7R2D1Db3seywjRWl2ayo7I14K8XbvyuOTTGPA88P8E1LwOp412jVCgaGnax9Ugz15xZ6PX+nJQEmrsHcLkMMTGBq5QKBb/85S/tDsFWwy5DR99QwLogjDbXvZ/sSGMXwy5rFLS0MI0hl2FzefmMJb5wobujlAL2nGinq3+IC+efvv4DVgIadhnaenXhONJ4GobOyBqQpxKuqXvktNQlBWmcPccqFH7naHRPw00pAYlIjIjcJiLPiche98dzInKriGhSU2Fnm/sXwTll3ncQRPJeoHvuuYd77rnH7jBs0xbgNjyjFWUkEe+I4UhjFwfrOslJSSAnJYEzS9JJjIvh7cPNALR2D4z0o4smk972KyJJWJ2qP4S1D8hzRMJ64KNY1XLrjTF6hKQKG9srW5ibk3zK/p/RclJOdkNYmB9Zs8w7d+60OwRbtQXgLCBfYmKEsuxkjjR2U9/Zx5IC62cnwRHL2XOyeLOiiUP1nVz/wBZiY4WXvnARBelJEzxr5JjKaOUbwIeB+4BcY0yJMaYEyAG+D6wDvh6wCJWaYcYY3j3Wypo5mT6v8ZwNpKXYkccz8piJERBYhQgfNHTxQX3XKS2erlg2i4qGLi7/wRsMDLto6xnk529VzkgMoWoqCehm4CljzJeNMSNlHMaYNmPMV4CngHGPWlAqlDR09tPaM8jSgjSf13hGRg0dOrCPNG09nkakM1MMMDc3meMtPfQPuVgy6mfshtXFLC1IIz0pjic/ex4XL8rl5X3RdX7QVDrvFWONdHz5M/CxKUWjlA0O1rkbkM7ynYDSk+JITXBwvKUnWGGpIOmYwSk4sPYCeZw77+SxF4lxsbzwDxcy5DLExcZwwfwc/lTeSF17H7PSo+PcqamMgNqABePcP5/T+8YpFbLK66zqpMWzfK/tiAizs50RmYAWLlzIwoUL7Q7DNp4ihJlKQBctyCE7OZ4L5+dQlHHq+o6IEBdr/RpeW2Ylp3eONs9IHKFoKiOgV4G7ReQV976fESJyOfA54DeBCE6pYDhY10leagKZyeNPwZRmOzlY2xmkqILn4YcftjsEW7X1DpIcHztjZ/bkpSXyl69cPJJofFlSkEpyfCzvHmvlupVFMxJLqJlKAvoG1iF0L4rI+8A+9+3LgFVAE6CnoqqwUdnUfcoBdL6UZDl5bX8Dwy5DbIRtRo1mM9WGZzRn/MS/ah2xMZxZksF7x6OnQ8KkU74x5hiwBngCWAhscH8sAB4HznZfo1RYqGrtpSRr4tLX0qxkBoZd1EVYIcKdd97JnXfeaXcYtmnrGSQ9RLoRnDU7kwO1nfQMDNkdSlBM6fg/Y8xx4FNi9azPBQRoMJ6Oe0qFid6BYRo7+5mddXr/t7E857t8UN952lx+ODt06JDdIdiqvXeA9KTAnYQ6HatLMxl2GXZVtXPeqIKFSDXpEZCInC8i/yEivwH+APwY+DywdgrPdaWIlItIhYh81cv96SKySUR2icg+Ebltsq+h1HiqW62ighI/EtDSQqtKbl9Nx4zGpIKrvXeQjBkqwZ6sVbMzAKJmGs7vtC8iaVhTbFdijXjG+pqIvAB8yhgz4UqtiMRiJa/LsA682y4izxlj9o+67O+A/caYa0QkFygXkV8ZY3Q3oAqIqkkkoPSkOEqzneyraZ/psFQQtfUMztgm1MnKcMYzNzeZ945FRwKazAjoaeAq4C3gNmA11rrPavfnW7DOAnrSz+c7B6gwxhxxJ5QngOvGXGOAVPdUXwrQAkTH5KgKiuPN7gSUOXECAlhelM7O423obHNkMMbMyHHc07F6dibvHW895Wds2GU4OupcoUjhVwJyn/NzKXCfMeZDxphHjTHvG2MOu/981BhzEfA/wBUicpkfT1sEVI36vNp922g/ApZgnbC6B/iCMcblJb47RWSHiOxobGz050tSCrAKEJLiYkd6vU3k3LnZ1LT3UdkcOfuBVq5cycqVK+0Owxa9g8MMDLlC6kiEs0ozae0Z5Oioo7zveXInF39/Mxu3VNoX2AzwdwR0C3AM+PIE130Z69C6T/rxnN6m8cam9yuAnUAhsBL4kXsq8NQHGfOwMWaNMWZNbu7pp1kq5Utdex8FGYlYg+yJXeQ+ruHNiqaZDCuo7r//fu6//367w7BFq3sTalZy6IyAzp1rFR/8qdx6M733RDubdtUA8NjbkVVg7G8CWg38bqIqN/fo5HdYZdoTqQZKRn1ejDXSGe024LfGUoF1EutiP2NWakL1HX3kp/rf9qQ020lRRhJ/OaQj7UjQ2m0tJ2eG0AioLCeZZYVp/H7nCYwxPPjnw6QkOPjCJQs40tQdUf0I/U1ARUC5n9eWYyWTiWwHFohImYjEA58AnhtzzXHgEgARyQcWAUf8jEOpCdV39pGf5v0IBm9EhIsX5/JmRRN9g8MzGFnwfPrTn+bTn/603WHYosWTgCboghFsnzi7hN3V7fy/P1bw0p5aPnXubC5wj773RlARjL8JKA3wtwdJJ1bBwLiMMUNY5dsvAwewOmzvE5G7ROQu92XfAs4XkT3A68BXjDGRM/ehbGWMob6jn/y0yTV+vHzpLHoGhnkrQqbhqqurqa6utjsMW7T2hN4ICODms2ezMD+F/3n1EBnOeO64cC7LCtMQgb0nImcbgL9l2DGcvj4z0fUTMsa8iHW43ejbHhr19xrg8km8rlJ+6+gdYmDI5fMQOl/OnZtNaqKDV/bVc8mS/BmKTgWDZwouK8RGQPGOGJ648zw27arhQwtzR35GC9ISqWzunuDR4WMy23/Xi8gsP65bPdVglAqm+k5rLn2yI6B4RwwfWZzHawfqtS9cmGvpGURk5jphT0dWcjyfOX/OKbeVZDmpiqCO7JNJQJ/Ev+o2mNxoSSlb1HdMLQGBNQ33+501vHuslXPKsgIdmgqStp4B0pPiwuZNxOwsJ298EDkFMP4moItnNAqlbFDf0Q8wqSIEjw8vyiXeEcMr++rCPgGdd955dodgm5buAbJCbP1nPLOznNR39NM3OExiXKzd4UybXwnIGPPnmQ5EqWDzjIDyJlGG7ZGS4ODC+Tm8sr+er390id/7iELRf/7nf9odgm1CqQ2PP2ZnWx07qlt7mJ/n+wDFcDEzJzApFQYaOvpITXSQFD+1d5KXL83neEuPNicNYw2dfeSkTH4EbJdZ7uni2vbI2AukCUhFrYbOyZdgj3bl8lkkOGL41TvHAxhV8F1//fVcf/31dodhi7r2PmalT/1nINg8sdZpAlIqvNV3TG4T6lgZzniuW1nI794/QXvvYAAjC67m5maam5vtDiPoegeG6egbmtabkGDzxFofId0QNAGpqFXf0T+l9Z/RPnP+HHoHh3liW3iPgqKR52TbWWGUgBLjYsl0xukUnFLhzBhDQ2cfedMYAQEsK0znvLnZbNxSyeDwaY3aVQjzTGOF0xQcWKMgHQEpFcbaegYZHDbTHgEB/O2Hyqht7+OF3bUBiEwFy3T2gdlpVnriyOgt3IXGQehKBVlDp7UHKG+SbXi8Wbcwj9JsJ8+8V83HVo090ir0XXLJJXaHYAvPceyFGWGWgNISI6YfnCYgFZUa3Qlosn3gvImJEdavKODhN47Q2j0Qcp2VJ/LP//zPdodgi6NNPcxKS8QZH16/BvPTEmnu7mdgyEW8I7wnscI7eqWmqLHLswk1MHtA1i8vYNhleHV/fUCeT828o01dzMnx7yj2UFKQnogx1h6mcKcJSEWlho7AjYAAlhelUZKVxAt7wm8d6KqrruKqq66yO4ygMsZwtKmbspxku0OZtPz0yCnF1gSkolJjZz9JcbGkJARm+kVEWL+8gLcqmmhznzETLnp7e+nt7bU7jKCqae+jtWeQpQVpdocyaZHUDUETkIpKDZ395KYmBLSH2/oVBQy5DK/oNFzI213VBsCK4gxb45iKggjqhqAJSEWlRncCCqQzitMpzXby2NvHGNI9QSHllX11fPKnW3llXx0AW480k+CIYfGs8GvomZ4UR2JcjCYgpcJVY1d/wAoQPESEf7xsIXtOtLP4n//Aqn9/hdcP6GjIbm09A/yfJ3ey5XAzf/fr93j7cDOvHWjgwvk5YXmkgYhQmJ5Era4BKRWeGjr6Aj4CArhuZREPfuosbr+wjNTEOL7yzB66+4cC/jqBdPXVV3P11VfbHcaMeXFPHd0Dw/z6jrWUZDm55adbOdHWy8fPCr89Wx6z0hOpbQv/dbvwKoBXKgD6Bq0mlIEeAXlctaKAq1YUcMmSfG76ydts2lXDJ86ZPSOvFQhf+tKX7A5hRv3xYAOl2U7Om5fNI585my888T5LC9NYv7zA7tCmbFZ6IlsPh38DWU1AKuo0dQW2BNuXs+dksjA/hce3HQ/pBBTp9tW0c05ZFiLCnJxkfv/5C+0OadoK0hOp7+xn2GXC5jhxb2ydghORK0WkXEQqROSrPq5ZJyI7RWSfiOjJrGraTrbhmdkWLCLCLefMZld1O7vcVVehaN26daxbt87uMGZES/cAte19LC9MtzuUgJqVnsSwy4y8mQpXtiUgEYkFfgxcBSwFbhGRpWOuyQAeAK41xiwDbgx2nCryeDahBuMkzBtWF5OS4ODnbx2d8ddSp9tX0w7AssLw2+8znsL0yNgLZOcI6BygwhhzxBgzADwBXDfmmk8CvzXGHAcwxjQEOUYVgerarcXbgiA0oUxNjOOmNSVs2l3LlsNNM/566lSepp1LIywBnTwZNbwLEexcAyoCqkZ9Xg2sHXPNQiBORDYDqcD/Ncb8YuwTicidwJ0As2frXLsa34m2XhIcMWQHqWnoPZct4PWD9Xzyp+8Q74ghOT6WL16+iE+fWxqU149m+2raKc5MIsMZXg1iJ1KQngRATVt4j4DsTEDeVs7MmM8dwGrgEiAJeFtEthpjDp3yIGMeBh4GWLNmzdjnUOoUNW19FGUkBbQLwnjSEuP43d0X8Mx71TR29bOjspV//v1eFs9KZc2crKDEEK3213RE3PQbQKYzjnhHTNifC2RnAqoGSkZ9XgzUeLmmyRjTDXSLyBvAmcAhlJqiE229FGYkBfU1M5PjueOiuQB09Q9xxQ/e4GvP7uGFf7iIuFh7t+PddNNNtr7+TOnqH+JIU3dYntE0ERGhID1R14CmYTuwQETKRCQe+ATw3Jhrfg9cJCIOEXFiTdEdCHKcKsLUtPXaeghZSoKDb16zlEP1XTy4+TDbjrawvbLFtnjuvvtu7r77bttef6YcqLXWfyJxBARWKbauAU2RMWZIRD4PvAzEAo8YY/aJyF3u+x8yxhwQkT8AuwEX8DNjzF67Ylbhr39omIbO/qCPgMa6fNksLl2Sz/+8enIw/8ita/jI4vygx9LTY50M6nSG39k449l7wqqAW14UWSXYHgXpSWw7at8bl0CwdSOqMeZF4MUxtz005vPvAd8LZlwqcp1otd4xFtmcgADu/8RKnn2vmtTEOP7rpYM8/MYRWxLQ+vXrAdi8eXPQX3sm7avpICclfsY6XtitJMvJ73eeoH9omARH+PW0A+2EoKJMRUMXAPPzUmyOxJqK23DeHAAON3bx4z9V0Nk3SGpinL2BRYh9NR0sLUwPWrFJsJXlOHEZqGrpDYmf56nQZqQqqlQ0WgloXoj9h11blo3LwHvH2+wOJSL0Dw3zQX0nyyN0/QegNNs6zbWyqdvmSKZOE5CKKhX1XcxKSyQtxEYZq2ZnIALvH2+1O5SIcKiuiyGXYVmEteAZrcyTgJo1ASkVFvbXdrAgP7RGPwDJCQ5Ks5wcqu+0O5SIsPtEGwArIrQAAazS/vSkOI6G8QhI14BU1GjvHaS8vpOrQrQN/6JZqZTXBT8B3XrrrVN+bE1bL7f9fDu3X1TGTWtKJn5AkOyuaifDGUdJlv3FJjNpfl6KLT8zgaIjIBU1tlQ0YQycXZZpdyheLcpPpbK5h77B4aC+7q233jrlJPTcrhrK6zv5wauhtTd894l2VhRFbgGCx4qidPbVdITtEfCagFTUeOa9avJSEzgnRNvfLJyVyrDLjFTqeTPsMhgT2G5TTU1NNDVNrVHqdvc+lNr2Ptp6BgIZ1pT1DgxzqL6TM4sz7A5lxp1Zkk7v4PBIcU240QSkokJDZx9/Km/k42cV4bC59Y0vC/NTAXwmoN6BYS77nz/z6f99J6BJ6IYbbuCGG26Y0mMrm7tJTbRm8vfXdAQspunYX9vOsMtwRnHkrv94rJ5tvZl684Pw7LQemv8TlQqw379fw7DLcOPq0FmnGGtOdjKOGOGDBu9z+q8dqOdIUzdvVTRTHgLFCi6Xoaq1lw8tzAXgWEuPzRFZdlVZHRDOLMmwN5AgmJ3tZEVROr/ceozX9tfT3T9kd0iToglIRYVNu2s4ozg9pDfsxTtimJOTzKF67yOg0W1XdobAfqGGzn4GhlysLcsiPjYmZMqB95xoJy81gfw0+/r9BdM/XraQ6tZe7vjFDu54dIfd4UyKJiAV8apaethd3c5HV4Rm9dtoC/JSfE7BHajtYE1pJmmJDva6T/q0U3WrNeKZneWkOCuJ480hMgKqbuOMKFj/8bh4cR5v33sJd1xYxttHmsNqY6omIBXxNh9qBOCKZbNsjmRiC/JSONbcfVolnMtlOFDbwdLCNMpykjkWAr/sm7qsooOclARKs5xUhkBMHX2DHGns5swoWP8ZLTc1gU+utQ7jfPtIs83R+E/3AamI996xVnJTEyjNDv1uzwvyU3EZONrUzZKCk21kqlp76B4YZmlBGq09g+ysClzHhM997nNTelxLt5WAslPiKc1OZkdlK8YYW0uf3ztmfV/OKg3NUvuZVJaTTEqCg4O1oVEM4g9NQCrivXe8lbNmZ4TFnhBPl4ZD9Z2nJCDP2TZLCtKobu3lxT21DA67AnKY3c033zylx7V09wOQlRxPcWYSnf1DtPUMkjlDR53vqW4nJdFBWU6yz2vePdZKbIywMgoKEMYSEeblpYRVSbZOwamI1tTVz7HmHs6aHR7viMtykomNkdPWgfbXdBAjVqn27Gwnwy5DTVtgDiOrqqqiqqpq0o9r7h4gJcFBgiOW2VnW6LKqdWam4f5U3sA1P3qTj/7wL+N+3TsqW1lakEZyQnS+ty7LdlLZZP9UqL80AamI9r67WixcpmQSHLGUZjv5YEwl3P7aTspykkmKj6XYfZaR52yj6dqwYQMbNmyY9ONaugfIco92SjwJqGVmTuh85M2jxMUKPQPD/Pyto16vGRx2sbOqjdVh8m89E2alJ9HQ2YfLFdjNyjNFE5CKCN39Qzz8xmEO1p06//3e8VYcMRJWTSkXz0o9rcrtYF3HyJRccab1y746QCOgqfKagGZgBDQ47GJHZSufWlvKpUvyeG6XtadrrPePt9E7OMzastDsdBEMBemJDA4bmrtDoyvFRDQBqYjw7RcO8J0XD7Lhf7edshnvvWOtLCtMIzEufE6MXF2aRXVrL7XtVoLp6BukurV3JAHNSk9EJHAjoKlq7hog252AUhIcZDrjOD4Dm1H31XTQOzjMmjmZXLeyiPqOfrZXnn4U9esH63HECBcuyAl4DOHCs/epvqPP5kj8owlIBVSg+5T5o7NvkGffr2Z+XgqNnf1s2lUDwNCwi93V7awKk/Ufj7PnWPF6Np4ecLe4WepOQPGOGPJTE6m2OQGNHgGBtR+oagYSkKff3DlzsvjI4jwS42J4cU/tKdcMDbt4flct583LjuoTZQvSrQQUqPXBmaYJKMoFajG7b3CYOx7dzuJ//gMPbK4IQGT+e/twM32DLr513XIW5KXw5A5rQf1gXSe9g8Nhs/7jsbQgjazkeF7dXw+cTESjW8sUZSZxos2+xWZjDM3d/WSlnExAxVnOGUmK2ytbKM12kpeWSHKCg4sX5fHS3rqRabhhl+HHfzrMibZePuXeCxOtclMTAHQKzh8icqWIlItIhYh8dZzrzhaRYRGZWsdE5ZUxhjse3c75//VHfrn12LSe60d/rOC1Aw0smpXKf/+hnPdm4GTPPx6s508HG067feuRFhIcMayancGNa4p5/3gbRxq7RqZpzpqdEfBYZpIjNoarls/itQP1tPcO8peKppGk5FGUkcSJAL3L/eIXv8gXv/jFST2ms3+IwWEzMgUHUJLp5ERrb0AXwI0x7DjWyprSk+s661cU0NjZz7ajLdS293LjQ1v4wWuHuGJZPpcvDf3NxjMp02n9e7RoAhqfiMQCPwauApYCt4jIUh/XfRd4ObgRRr7XDzTwp3KrS8DDbxyZ8vRZ3+Awj209xvoVs3j8b88l0xnHz/5yJJChsu1oC3+zcQe3bdzOlopTO/9uPdLMWbMzSYyL5bqVRcQIPPv+Cf54sIG5uckji/bh5JNrZ9M36OKux95l29EWrlx+6i/Woswkatv6vC7GT9Y111zDNddcM6nHtLi7IGQlJ4zcVpKVxMCwi/rOwK0/HG7soqV7gHNGneF0yZI8MpxxfP3ZPVz7o7cor+vkBzefyUOfXk1MTOjv9ZpJSfGxJMXF0qoJaELnABXGmCPGmAHgCeA6L9f9PfAMcPpbXzVlxhh++McPKMlK4t+uXcbxlp4pt3d5eV8d7b2DfGptKckJDj62qohX99fT2TcYsHif2H6cxLgY0hId/HRUcmvvHeRAXQfnuCuf8tMSuXBBLg9sPsxfPmgKi/Y73iwrTOezH5rL20eaKUhPZMO5pafcX5yZxJDL0BCAX/bl5eWUl5dP6jGeKZ7sMWtAQEB7wm2vtEbSZ486w8kZ7+Dfr1tOVWsPGUlxPPt3F/DxVcVhsdE4GLKS42ntCdz/vZlk526tImD07rdqYO3oC0SkCPg48BHg7OCFFvnerGhid3U7//lXK0Y2ab53vJU54+wy9+WpHVWUZCVx3txsAC5fOoufv1XJ24ebuTxACWDb0RbWLcxjfl4KD2yuoKGjj7y0RN4/3ooxjCQggH+6fBFbDzeTkuTg1vPnBOT17fDVqxbzsVVFFGYkkZ506sJ60ai9QAXp0zt2+rOf/SwAmzdv9vsxnime0dOC83KtLg7l9Z2sdf8sTNf2oy3kpMSf1v3g2jMLuXxpPvGxMVE/6hkrMzmO1hA5HHAido6AvP3UjJ1PuB/4ijFm3DOKReROEdkhIjsaGxsDFV9E+8mfj5CXmsBfnVXEvNxk4mNjpnTGzPHmHt6qaObG1SUjvwhWl2aSHB/Lnw9N/t/C5TKnNeI80dZLdWsva+dm8bFVhbgMvOCugtpReXrrlRXF6fz5y+t4/YsfDuuW/CLCkoK005IPWCMgIGDrQJM1ug2PR0F6IjkpCSPn8UyXMYZtlS2sKc3yOrpJjIvV5ONFpjNe14D8UA2MPh2sGKgZc80a4AkRqQRuAB4QkY+NfSJjzMPGmDXGmDW5ubkzFG7k2HuinTcrmrjtgjISHLE4YmOYk+Pk8DhHQfvyxPbjxAjcuKZ45LZ4Rwxr52bz9uHJd+X90tO7WPGvL59y9s22o9bznFOWxfy8VJYUpPGcu9R6x7EWlhWe3nqlID2JnJQEIlWhewRkVyl286hGpB4iwsqSdLZXtgSkHP9oUzfVrb2cPz8wo6loYU3BaQKayHZggYiUiUg88AngudEXGGPKjDFzjDFzgKeBu40xvwt6pBHmoT8fJiXBMdK+HWD+OOfQ+DIw5OKpHVV8ZHH+adNA58/L5khTN3Xt/q9RtPUM8Nv3TjA4bHhwVCn3tqMtpCU6WDzL2gdz7ZmFvH+8jb0n2tlR2cp586LvF5Qz3kFWcrxtCaila4DEuBic8acm/kuW5HO8pYc9J6Y/CvIUyFy8KG/azxVNdATkB2PMEPB5rOq2A8BTxph9InKXiNxlV1yRbmdVG8/vruUz55eeMrUzJzuZ6tZehoZdfj/Xq/vraeoa4FPnnr734lz3GsDbR/w/q/6tCmukc9bsDN6qaB7paPDO0RbOnpNFrHu65ZozrYPlbtu4nSGXCdtCg+kqzgxcKfZktXQPkJ18+ghz/fIC0hIdfPcPB6c9Ctpc3sC83OSRNj/KP5nOeDr7hhicxP9lu9i6D8gY86IxZqExZp4x5j/ctz1kjHnIy7W3GmOeDn6UkeN4cw9feOJ98lIT+Ny6+afcV5zpZMhlqO/s9/v5fvXOMYoykvjQgtOnPZe61y4mMw23+0Qb8bEx/J/LFjIw7OLNiiYaOvs40tjN2rkniwyKM51csSyfxs5+VpZksCoKW++Dey9QAHqvfeMb3+Ab3/jGpB7TPKYLgke6M44vX7mYtyqa+c271VOOqWdgiHeOtrBORz+TluG03lh29IZ+JVx09iyPQs/tquFrv92DCGy87RxSxqyZFGWerKryVFiNp7Kpmy2Hm/nS5QtHRiajxcQIa8uyJnU6Y0V9F3Nzkzl3bjapiQ7+eKCB/iHrXdzaslOn2e67aSWX761j3aLcqC2/LcpI4k/lDdM+BO7SSy+d9GPGtuEZ7ZPnzOapHVU8/MYRblw9tfLotw83MzDkYt0iXdOdLE8CausdJDvE10G1FU+E6+4f4t7f7uYfHn+fRbNSeekLF3ltV++pqqr28x31E9uriI0RblxT4vOa8+ZlU9XS63d/sA8aupifl0JcbAzrFuXx+sF6/lzeSHpSHMvHdLNOSXBw/erikP8PNpOKMpPoG3RNe75/586d7Ny5c1KPsabgvCegmBjh5rNLqGjo4lD91A5H21zeSFJc7Cnl9co/nqn1tjDYC6QjoAjjchnqO/t4/3gbr+6vZ3N5A229g3xu3Tz+8bKFPk/QLJrEGTPGGDbtqmHdwtxxy5zPn2d1JX77SPOE8/i9A8NUtfZw/VlWNd1lS/PZtKuGZ96r5uOriryOsqLdyL9ZW++0EvE999wDTG4fUHN3v88REDAyLfvO0WYWzUqdVDzGGP5U3sAF87NJcIRPF/NQkeFux9PeG/qFCJqAwpQxhsFhQ1ys4DJWQcAv3q7k3WOtI9NWmc44Prwwlw3nzZnwkK7EuFhyUhL8WtQ+UNvJibZevnDJgnGvW5ifQnZyPFsPN3PTOCMlsFquGGM9BuDKZbNYmJ/CseYebr+wbMKYopEnqVc293BGcUbQXrd3YJi+QdcpjUjHKs5MoiA9kW1HW/jr8+ZM6vk95def/fC8aUYanTJ0BKQCqaV7gF3VbWCs6Y1tR5t5YXctlc09OONjccQIHX1DFGUk8elzSynLSWbRrFRWlWTg8DHi8aYoM8mvst5X99cjAhcvHn+BWEQ4d142mw810tU/xOGGLnoGrAPDxm4g/KDB2gQ7P89KQPGOGDb9/YV09w+P+047ms3LTSEuVjhQ28G1ZxYG7XWb3ZtQfU3BgfVvv6IonQO1HT6v8cWzB+y8AHVTiDYja0CagJQvLpeho2+Qpq4B6jv6aOrqZ2DIRXP3AHtOtFPT1kuiI5aOvkH213YwuqI1RuCC+Tl8fFUx7b2DdPcPcfHiXC5dkj+phDNWcWYS+2sm/oXxVkUTZxSlj7R+H8/fXFDGC7trOetbrzLgHpl9eGEuP9mw+pRD4g43dBMbI5Rmn2y5kuCI1SmYccQ7YliQl8o+P/7NAulkG57x//0XF6Tx2oF6+gaHJ3Ug4LbKFrKT45mXO/m2UApSE+MQsYoQQp0moBlU09bLq/vrRzr6tvYM0Nw1QHP3AC3dAz47GRdnJjEnO5mBIRcZzjj+z6ULOacsi8S4WIaGXczOss5GCbTijCRe3VePy2V8tjjpHxpmZ3Ubnzmv1Ov9Y60uzeRHn1zF5vJG1pZl0d47yLdfOMB//6Gcf7nmZPPzioYuSrOcxDu0LmYylhWmBaQSbjKaRxLQ+Ae/LZmVisvAB/VdrCj2/0j07ZUtrJmTGbXVjdMVGyOkJcbRHgbdEDQBzZCX99Xx979+n4FhF6mJDnJSEshKjqcky8nKkgyyU+LJSk4gOzme/LREclMTSHDEkJroGFlEDLaSLOdIO31fDS73VLczMOQ6pTvxRK4+o5Crzzg5RVTZ3M3GLUe56ezike4Ghxu7mOeeflP+W1aYxm/eraahs3/Kfe++853vTOp6b0cxeLPYfYLrgboOvxNQXXsfVS29fGaS60bqVBnOOB0BRauW7gH+6Te7WFKQyg9vWcXsLGdYvJsrzbYWtY819/hMQNvch7ytmUQCGuufLl/Ms++d4KHNh7n/E6sYGnZR2dzNJUvyp/yc0WqZuzx974n2KSeg888/f1LXj6wBjVOEANbxDElxsRys9b/JrefnS8uvpycjKS4s1oB0vmMGPP1uFR19Q3z3hjMozU4Oi+QDo85zGWffzr4THZRmO6dVGJDujOOWc2azaXctJ9p6OdrUzeCwGSlAUP5bUpCGCNNaB9qyZQtbtmzx+/r6jn6S4mJJTRj//WtsjLBoVir7ak72hfvNjioe3VLps03P9qMtJMfHstQ9elJTk+6M1xFQtHphTx1nFqePTC+Fi8KMJGJjZNyNowfrOliUP7l9Hd7cdmEZP99SyaNbKpnvPkdmZYn/6wTKkpLgYE528im/5Cfra1/7GuD/PqC6jj5mpSf69cZqRVE6z75/ApfLUF7fyT89vRuw9jBduvT0Ee/2yhbOKs2cVjGNskZAx5u77Q5jQvqvHGA9A0PsPdHORV76o4W6uNgYCjMSfZ6M2jc4TGVzD4snubHQm6KMJK5aPovHtx3nz4caSUt0MDdHR0BTsawwjb0nglcJ19DRR54fFZAAZxSn09U/xJGmLn7+1lHiY2NIcMTwm3erTru2vWeQ8vrOSa0vKu/CZQ1IE1CA7TzexrDLsHrO+Bs/Q9XsLKfPKbjDjV0MuwwLA5CAAG6/sIzOviFe2FPLxYvz9HCxKVpWmM6Jtl7aglT15BkB+cNzUODL++r53c4abjq7mI+vKmLL4ebTOq/vONaCMWgCCoCMpDjaewdx+ai0DRWagAJst/sclLNKwjUBJfucgiuvsxaTAzECAlg1O5NbzplNYXoid+mu9ylbXmRN9fqzh2u6jDHUd/hfcTc/L4XZWU6+93I5A0Mubj2/jAsX5NDZNzTyf8VjW2ULcbHCqtkZMxB5dEl3xmMMdPYN2R3KuDQBBdjhhi5yUxNId46/RyJUleU4ae4eoNVLg8vy+k7iY2NO2Sw6Xf/5VyvYcu8lLNFF5ylbVuiuhJvGOpC/2noGGRhy+Z2ARIQN51p7xm5cXcz8vJSRDgdbx3RK3360hRVF6ZPatKq8G2nHE+L94LQIIcCONHUzNyd8d3B7fpntq+ngwgU5p9xXXtfJPHe3ahU6spLjKUhPnHIl3P333+/3tXUd1gm3+Wn+Nz+946IyLlmSN/LGJTslgfl5KbxzpIW711nXdPcPsbu6nTsumuv38yrfRrfjKQ3hjkb6myTAjjRaZ9qEq2WF1kjE27vp8rpOFuVroUAoWlaYzt4pHoO9cuVKVq5c6de1nl6B/pwZ5SEizM1NOaWj+dqyLHZUtoysA+041sqQy3B+FB6vPhNGnwkUyjQBBVBr9wCtPYNhXc2V4YynKCPptHfT7b2D1Lb3sSjMSsujxfKiNI40ddPVP/k5/9dee43XXnvNr2uPuUt7pzsNu3ZuNt0Dw+x3NyvdcriJuFhhTZgW74Sa9CRrn16wClOmShNQAFW6/3OWhfEUHMCZJem8W9lyymbBQ/WBLUBQgbWiKB1jplaI8O1vf5tvf/vbfl17vKWH1AQHmdNc4zzX3engnSPWz9mr++o5e04WznhdFQgEzwioXUdA0aOu3ZofL8gIfKPQYLpgfg417X0caTq5ke2guwIuUCXYKrBWuFvy7JniNJy/jjX3MDt7+q2l8tISWZCXwsv76thzop0jTd1cE8QjJSJduJyKqgkogOpHFmjDOwF5TrN841DjyG0HajtIS3RQ6Of+DxVceWmJ5KclTHkdyF/HW3pGegZO1/Wri9lxrJXP/fI90hIdrF9REJDnVdam8tQEhyag8YjIlSJSLiIVIvJVL/d/SkR2uz+2iMiZdsTpr7qOfuJihSybulkHSkmWk3m5yby6v37ktgO1HSwuSAubvnbRaEVROrur22bs+YddhurWHmZnBWaKecO5pSzMT6G2vZdvfHTpyLt2FRjpzjgtw/ZFRGKBHwOXAdXAdhF5zhizf9RlR4EPG2NaReQq4GFgbfCj9U99Rx95qYkRsaP/oysK+NGfKmjs7Cc7OZ7yus4Jj9VW9lpelM7rBxvo6h8iZYJGoVNR09bL4LAJ2AgoOcHBC/9wEV19Q2TqqbcBl+GMo11HQD6dA1QYY44YYwaAJ4DrRl9gjNlijGl1f7oVKA5yjJNS1+5/i5JQ99EzCnEZ+MPeWo42d9MzMMySAl3/CWVnFmdgDOyqapvU437yk5/wk5/8ZMLrPIUoCwNYih8XG6PJZ4ZkJIV+R2w7S06KgNEdCasZf3RzO/DSjEY0TfWdfRFTJbYwP4X5eSk8v7t2ZEQ3nTOA1Mw7uywLR4zwxgeNXDA/Z+IHuC1atMiv68rdCWh+XmT8jEe6dGcctbW9docxLjtHQN7mqbx2zhORi7ES0Fd83H+niOwQkR2NjY3eLgmK+va+sC9A8BARrj2zkG2VLfz0jSPMSksM6w4P0SAlwcGaOZm8cahpUo/btGkTmzZtmvC6D+q7KEhP1LWaMOFpSBrK7ExA1cDoRYVioGbsRSJyBvAz4DpjTPPY+wGMMQ8bY9YYY9bk5tpzDEJn3yDdA8PMipAEBPDpc0tJioulsrmH61cXaQFCGPjI4jwO1HZQ0dDl92Puu+8+7rvvvgmvK6/rZGEAzoJSwZHhtE5F9XX4XyiwMwFtBxaISJmIxAOfAJ4bfYGIzAZ+C2wwxhyyIUa/1XdYxxRHyhoQWD3GfnXHWr50+UI+f/ECu8NRfvj4qmIcMcLj244H9HmHXYaKxq6Arv+omZWRFM+Qy9A9MGx3KD7ZloCMMUPA54GXgQPAU8aYfSJyl4jc5b7sX4Bs4AER2SkiO2wKd0KePUB5qZGTgMA6MuHzH1lAUrx2KA4HuakJXLl8Fk9tr6KjL3DTL8eauxkYcukIKIykjzQkDd1SbFv3ARljXjTGLDTGzDPG/If7toeMMQ+5/36HMSbTGLPS/bHGznjH4+mCEEkjIBWePvuheXT2D/HrdwI3CjpUb03pLYqQIptokBEG3RC0E0KAeNrUR9IakApPK4rTuWB+No+8eZT+ocBMvxyo7SBGrAPmVHjIcG+ID+VCBE1AAdLQ0UdaokOnqlRIuOvD82jo7OfZ905MeO1jjz3GY489Nu41u6vbWJCXqs1Cw8joM4FClSagAKnriJwSbBX+Lpyfw/KiNB556+iE15aUlFBS4rvLhTGGPSfaWVGcHsgQ1QwLh1NRNQEFSF1Hv67/qJAhIty4uoRD9V0TlmQ/+eSTPPnkkz7vr23vo6lrgDM0AYUVTxFCS5cmoIgXSZtQVWS4bGk+AC/vqxv3ugcffJAHH3zQ5/2e1j6eIx9UeEhwxJKdHE+Nu0AqFGkCCoChYReNXf1agKBCSmFGEmcUp/PKqK7mU7HlcDPJ8bEs1wQUdgoyEqltD912PJqAAqCpa4Bhl9EpOBVyrlg2i11VbZxom/ovobcqmlg7N5u4WP11EW4K0pOobdMRUETzlGAXaAJSIebqM6xD3l7YfVqXK78caeziSFM358/LDmRYKkgK0xOpmcabj5mmCSgA6txDXF0DUqGmNDuZM4rT2bSrdkqPf2zrMeJihWtX6nHZ4aggI4nO/iE6A9gVI5A0AQVAbbuOgFTouvbMQvacaGdPtffjup9++mmefvrp027v7h/i6R3VrF9REHEtpqJFYUYScPJ3VKjRBBQAdR19xMfGkKUHa6kQdPPZJaQlOvjBa4e8dkbOyckhJ+f084N++141nf1DfOb8OUGIUs2E4kwrAR1r7rE5Eu80AQVAXXsf+ekJelyBCkmpiXF8/iPz+ePBBp7x0hlh48aNbNy48ZTbjDE8+vYxVhSls6okIziBqoCbl2u1TprM8RzBpAkoAGrb+yhIS7I7DKV8uv3Cuawty+Lfnts30rndw1sCevtIMxUNXXzm/Dn6xiqMpSfFkZ+WwAcNnXaH4pUmoAA40dpLYYbOkavQFRsj/PcNZzAw7OK7Lx2c8PqntleRlugYqaJT4WtBXqqOgCJV3+AwNe29zNHjqlWIK81OZsO5pfx+V824+4I6+gZ5aW8d164sJDFOm+uGu/l5KVQ0dOFyhd7JqJqApqm6tQdjYE62JiAV+m67sAyAX2yp9HnN87tq6R9yccNq3w1KVfhYWphGz8AwhxtDbxSkCWiaKpus6pLSbKfNkSg1saKMJC5bks+TO6roG/R+VtBv36tmfl4KZ2rz0YiwpjQTgB3HWm2O5HSagKbpaFM3oCMgFT42nFdKW88gL+y2Nqe++OKLvPjiiwAcb+5hx7FW/uqsIi0+iBBlOclkJ8ezo1ITUMQ5UNtBfloCmboHSIWJ8+dlMzc3mce2HgPA6XTidFoj+N+8W4UIfGxlkZ0hqgASEc6ek8WWw01e94HZSRPQNO2taWd5oU5VqPAhImw4t5SdVW3srGrjgQce4IEHHqCtZ4BHt1Ry2ZL8kR30KjJcsiSP2vY+9p7osDuUU+j5utPQ3jNIRUMX61doqaoKL9evLuZHf6zgC0+8T/2vf4EBXho+g97BYe65dKHd4akAu2RJPjECL+6tDamTbW0dAYnIlSJSLiIVIvJVL/eLiPzQff9uETnLjjh9ebOiCZeBixac3sZEqVCWlhjHQxtW0z/oorK5m2PN3TR09vHDT6xiaWGa3eGpAMtKjucji/N4crvv4hM72JaARCQW+DFwFbAUuEVElo657CpggfvjTsD3sY02eOa9anJS4jmzOMPuUJSatLPnZLHlqx9hdWkma+ZksfXeS7hKR/MR6/YL59LSPcDGcUrwg83OEdA5QIUx5ogxZgB4ArhuzDXXAb8wlq1AhogE/X+IMYahYRf9Q8P0DQ7T1T/E49uO88eDDfz1eXNw6EFdKkzFxAhxsTE4YkSr3iLcuXOzuGJZPve9Us6v3jlGR9+g7UUJdq4BFQFVoz6vBtb6cU0RMLXDTSbw6v56vvDE+wy7DMbAsDEMj7N7+Jw5Wdz5obkzEYpSSgWUiPDfN5zJnb/Ywdef3cvXn91LjECCI5bx3nvMy01h099fOCMx2ZmAvH3JY3/b+3MNInIn1hQdQJeIlE8zNo8coMnXnceA33wuQK8UWOPGHcI07uA6Je4wGgFFxPc7XByAHPmHacVd6usOOxNQNTC610cxMPbcYH+uwRjzMPBwoAMUkR3GmDWBft6ZpnEHl8YdXBp3cM1k3HYuXmwHFohImYjEA58AnhtzzXPAX7ur4c4F2o0xMzL9ppRSKrhsGwEZY4ZE5PPAy0As8IgxZp+I3OW+/yHgRWA9UAH0ALfZFa9SSqnAsnUjqjHmRawkM/q2h0b93QB/F+y4Rgn4tF6QaNzBpXEHl8YdXDMWt9hdhqeUUio66QYWpZRSttAENIaIfE9EDrpb/zwrIhnu2+eISK+I7HR/PDTBUwWVr7jd993rbmdULiJX2BjmaUTkRhHZJyIuEVkz6vZQ/357jdt9X8h+v8cSkX8VkROjvs/r7Y7Jl4lad4UyEakUkT3u7/EOu+PxRUQeEZEGEdk76rYsEXlVRD5w/5kZqNfTBHS6V4HlxpgzgEPAvaPuO2yMWen+uMue8HzyGre7vdEngGXAlcAD7jZIoWIv8FfAG17uC+Xvt9e4w+D77c0PRn2fX5z48uDzs3VXqLvY/T0O5VLsjVg/t6N9FXjdGLMAeN39eUBoAhrDGPOKMWbI/elWrL1HIW+cuK8DnjDG9BtjjmJVFJ5jR4zeGGMOGGMCtXE4aMaJO6S/32HMn9ZdapqMMW8ALWNuvg541P33R4GPBer1NAGN72+Al0Z9XiYi74vIn0XkIruC8sPouH21MwoH4fL9Hi0cv9+fd0/dPhLI6ZUAC8fv62gGeEVE3nV3bgkn+Z79l+4/8wL1xFF5HpCIvAbM8nLX140xv3df83VgCPiV+75aYLYxpllEVgO/E5FlxpignfA0xbj9amc0k/yJ24uw+H57e5iX22wtNR3v68DqMP8trBi/BdyH9QYm1ITc93WSLjDG1IhIHvCqiBx0jzaiWlQmIGPMpePdLyKfAa4GLnHvRcIY0w/0u//+rogcBhYCQVtQnErc+NnOaCZNFLePx4T899sH27/fY/n7dYjIT4HnZzicqQq57+tkGGNq3H82iMizWFOK4ZKA6kWkwBhT6z6NoCFQT6xTcGOIyJXAV4BrjTE9o27P9Swmi8hcrDOKjtgT5el8xY3VzugTIpIgImVYcW+zI8bJCPXv9zjC6vstpx5v8nGs4opQ5E/rrpAkIskikur5O3A5oft99uY54DPuv38G8DX6n7SoHAFN4EdAAtYwGWCruwLrQ8C/i8gQMAzcZYwZu1hnJ69xu9sbPQXsx5qa+ztjTMgciSgiHwf+H5ALvCAiO40xVxDi329fcYf699uL/xaRlVjTWZXAZ22NxgdfrbtsDstf+cCz7v+XDuDXxpg/2BuSdyLyOLAOyBGRauCbwH8BT4nI7cBx4MaAvZ52QlBKKWUHnYJTSillC01ASimlbKEJSCmllC00ASmllLKFJiCllFK20ASkVARzd2E27o+KIL7urFGva0RkY7BeW4UPTUAqYojIujG/9MZ+DE38LBHpILAB+OJkHygiDhGpcbfojxvnurnuoylecd/U5n7NDVMJWEUH3YiqItHjjDnq3c0V7EBCRL0x5pdTeaB7A+gvsLpsXA086+PSW7H6tT3iflwf8EsAEXlsKq+tIp8mIBWJ3pvqL9yZIiJJwOCoIzPCySNYCeg2vCQgEYnBatHS4u1+pXzRKTgVlcQ6cdWIdSLo1SKyXUT6RKRWrNNlT3tzJiILROQx9zUD7vWV77n7e42+bqP7uXPdRxzUA924z2gSkTNE5BUR6RaRZhF5VERyRq+ViEi++zW8JlIRecA95VU6ze/DhF+TMeYQ8BfgKhHx1lX7EmA2VouZ/unEo6KLjoBUJHKKSI6X2we8HOewHrgbeAjrnf51wJeAVuA7novcR0L8EWtt4yfACeBM4B+AC0Tkw8aYwTHP/SpQh3XMQTLQJSILsH6ZxwA/dD/Pek49dwpjTL2IPAdcLyKfN8a0jYolEbgFeM0Yc8yfb4g3k/yaHgEuwlrT+d6Yp7pt1DVK+c8Yox/6EREfWE0UzTgfz4+6do77tm5gzqjbBatTce2Y596FtZifOub2j7uf59ZRt2103/ZLLzE+5b7vgjG3P+m+feOo2y5333b3mGs/5b79Jj++J5XAZh/3TeZrSgY6gP1jrk0HerGmPX3FcMrXpR/64fnQKTgViR4GLvPy8XUv1/7OGFPp+cQYY4A/AbNEJAVARFYAZwC/BhLc02U57lHWm1hJ7HIvz/390Z+4j5dYD2wzxrw15tr7vDz+VeAocPuY228HmoHfeXmMXyb7NRljurGOwV4iImtHPdUtQCLwv1ONRUUvnYJTkegDY8xrfl7r7YyhZvef2UAXsMT9+b+5P7zJ93LboTGf52KNJMq9XHvabcYYIyI/A/5DRFYaY3a6z0ZaB/xfY8yAj1j8MZWv6X+Bv8U6MfUd921/A/RhJTKlJkUTkIp2453VI2P+vA/wdY5L69gbzKkHA45+Hm98nYvyCFaCuB34e6xf+AL8bJzn8sekvyZjzDsisg/rwL17gLnA2cDjxpjTvn6lJqIJSKmJfeD+c3gSIytvGrCmthZ5uW+xtwcYY+pEZBPwKRH5Kla58ztm+oexTfVregQraf0VsGrUbUpNmq4BKTWx97EKE+5yT4Gdwt0tIGuiJzHWyagvAeeIyAVj7h6vS8FPgUysSr1ipj/6gal/TY8Bg8CdwKexihxeD0A8KgrpCEhForNE5NM+7vudMaZrMk/mXovZgFWyvFtEHgH2AU5gPtZo4F6s6reJfAO4AviDiPwIqAY+irU+BN6n4l4GjmH9wvcUA0zLVL8mY0yjpzzcfdO/ugs3lJo0TUAqEt3i/vBmATDpppzuAoBVWL+UrwXuAjqxRgAb8XMUYIwpF5EPYVXIfQFrAf954O+wCiJ6vTzGJSL/C/w78ORkE+g4sUz1a/pfrARk8C/pKuWV6JsXpezn3hS6A7jXGPNfXu7/MvBd4HxjzNuTeN5KrA2m12Gt9wSlWEBEBKuKEKAReNQYc2swXluFD10DUirI3H3hRn8uwJfdn77q5XoH8Flgz2SSzyjnYyWB7VN47FTlu1+zMYivqcKMTsEpFXw7ReSPwB6sfUHXYLW5edIY867nIhEpA87DGr3Mxfe04ng+BXgS3tiy8JnUgrX516MmiK+twoROwSkVZCLy31hJpwTrTeBR4FfAd82ofnIicivwc6AJeMAY883gR6vUzNEEpJRSyha6BqSUUsoWmoCUUkrZQhOQUkopW2gCUkopZQtNQEoppWyhCUgppZQt/j+eUS7WfAzwoQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "DoS = log_gr.get_dos() #the density of states\n", "ax = DoS.plot()\n", "ax.set_ylim([0,None]);" ] }, { "cell_type": "markdown", "id": "299a1781", "metadata": {}, "source": [ "When analysing electronic properties, the $k$-dependence is obtained by plotting the band structure. This is done along a given path, that can be specified by the user. " ] }, { "cell_type": "code", "execution_count": 39, "id": "e4e1faab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "spacegroup P6/mmm (191)\n", "Lattice found: orthorhombic\n", "irreductible k-points 112\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/sam/Software/miniconda/miniconda3/lib/python3.9/site-packages/ase/dft/kpoints.py:655: UserWarning: Please call this function with cell as the first argument\n", " warnings.warn('Please call this function with cell as the first '\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Interpolation bias 1.0852849288836878e-08\n", "('G', array([0., 0., 0.]))\n", "('R', array([0.5, 0.5, 0.5]))\n", "('S', array([0. , 0.5, 0.5]))\n", "('T', array([0.5, 0. , 0.5]))\n", "('U', array([0.5, 0.5, 0. ]))\n", "('X', array([0. , 0.5, 0. ]))\n", "('Y', array([0. , 0. , 0.5]))\n", "('Z', array([0.5, 0. , 0. ]))\n", "[['G', 'X', 'S', 'Y', 'G', 'Z', 'U', 'R', 'T', 'Z'], ['Y', 'T'], ['U', 'X'], ['S', 'R']]\n" ] } ], "source": [ "BZ_gr = log_gr.get_brillouin_zone()\n", "\n", "hsp = BZ_gr.special_points # high symmetry points\n", "[print(i) for i in hsp.items()]\n", "paths = BZ_gr.special_paths # high symmetry path list\n", "print(paths)" ] }, { "cell_type": "markdown", "id": "08e6de99", "metadata": {}, "source": [ "Here we choose half the first path, since the 2D character of our system implies no dependence in $k_x$." ] }, { "cell_type": "code", "execution_count": 40, "id": "d3de49f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['G', 'X', 'S', 'Y', 'G']\n" ] } ], "source": [ "print(paths[0][:5])" ] }, { "cell_type": "markdown", "id": "571aead8", "metadata": {}, "source": [ "Alternatively, one could also define its own path such that $k_x=0$." ] }, { "cell_type": "code", "execution_count": 41, "id": "b9e97cf2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['G', 'X', 'S', 'Y', 'G']\n" ] } ], "source": [ "path = []\n", "for p in paths[0]:\n", " if hsp[p][0]==0.0: path.append(p)\n", "print(path)" ] }, { "cell_type": "markdown", "id": "91a31095", "metadata": {}, "source": [ "Eventually, the band structure is plotted using the `BZ` class by giving a list of special $k$-points (i.e. the `path` defined above)" ] }, { "cell_type": "code", "execution_count": 42, "id": "c00c94dd", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import BigDFT.BZ as BZ\n", "\n", "npts = 200\n", "path_bs = BZ.BZPath(BZ_gr.lattice,path,hsp,npts=npts)\n", "\n", "ax = BZ_gr.plot(path=path_bs,npts=npts)\n", "ax.set_ylim([-10,5]);" ] }, { "cell_type": "markdown", "id": "bed6c7db", "metadata": {}, "source": [ "## Linear scaling in graphene\n", "\n", "Now that we have seen how to describe graphene using first-principles simulations, let us apply the linear scaling framework to it. \n", "More information on the implementation of linear scaling in BigDFT is accessible [here](https://pubs.rsc.org/en/content/articlelanding/2015/cp/c5cp00437c/unauth)" ] }, { "cell_type": "markdown", "id": "1bb4b403", "metadata": {}, "source": [ "For that matter, one needs first to define a cell large enough so that electronic properties are well described at the $\\Gamma$ point." ] }, { "cell_type": "code", "execution_count": 43, "id": "93a1d705", "metadata": {}, "outputs": [], "source": [ "def sys_extend(sys,n1,n2):\n", " \n", " from copy import deepcopy\n", " sys_ext = deepcopy(sys)\n", " pos = sys.get_posinp()['positions'];\n", " vec = sys.cell.get_posinp(units='bohr')\n", " \n", " for i1 in range(n1):\n", " for i2 in range(n2):\n", " if i1==0 and i2==0:\n", " continue\n", " v_i = [i1*vec[0],0,i2*vec[2]]\n", " for frag in list(sys):\n", " tmp = deepcopy(sys[frag])\n", " tmp.translate(v_i)\n", " for at in tmp:\n", " sys_ext[frag].append(at)\n", " sys_ext.cell = UnitCell([n1*vec[0],float(np.inf),n2*vec[2]], units='bohr')\n", " \n", " return sys_ext" ] }, { "cell_type": "markdown", "id": "5d0edcec", "metadata": {}, "source": [ "If one desires to vizualize the supercell, it is advised that $n_x$ and $n_y$ remain small. " ] }, { "cell_type": "code", "execution_count": 44, "id": "e90ea3c7", "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ "
\n", "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gr_scell = sys_extend(graphene,5,5)\n", "gr_scell.display();" ] }, { "cell_type": "markdown", "id": "7f802ede", "metadata": {}, "source": [ "Since a $\\{k_y,k_z\\}=\\{15,27\\}$ grid was used in reciprocal space, the linear calculation requires a significant supercell to compute the spatially resolved DoS." ] }, { "cell_type": "code", "execution_count": 45, "id": "6c8bcbda", "metadata": {}, "outputs": [], "source": [ "gr_scell = sys_extend(graphene,11,19)" ] }, { "cell_type": "markdown", "id": "37e2f783", "metadata": {}, "source": [ "The linear scaling input file is then written down simply by doing" ] }, { "cell_type": "code", "execution_count": 46, "id": "c5714456", "metadata": {}, "outputs": [], "source": [ "from BigDFT import Inputfiles as I\n", "\n", "inp_scell = I.Inputfile()\n", "inp_scell['import'] = 'linear'" ] }, { "cell_type": "markdown", "id": "7344b8f1", "metadata": {}, "source": [ "The spatially resolved DoS is computed when" ] }, { "cell_type": "code", "execution_count": 47, "id": "1f7c475a", "metadata": {}, "outputs": [], "source": [ "inp_scell['lin_general'] = {'subspace_diag': 'Yes'}" ] }, { "cell_type": "markdown", "id": "3e8740ee", "metadata": {}, "source": [ "In order to run `bigdft` on the input file `gr_scell.yaml`, sizeable computational ressources are required." ] }, { "cell_type": "markdown", "id": "1616b38e", "metadata": {}, "source": [ "For convenience, the resulting logile is provided." ] }, { "cell_type": "code", "execution_count": 48, "id": "f28d9049", "metadata": {}, "outputs": [], "source": [ "# log_scell = calc.run(input=inp_scell,posinp=gr_scell.get_posinp(),name='gr_scell',dry_run=True)\n", "log_scell = Logfiles.Logfile('log-gr_scell.yaml')" ] }, { "cell_type": "markdown", "id": "e79f6597", "metadata": {}, "source": [ "Finally, the DoS is plotted using our path defined previously." ] }, { "cell_type": "code", "execution_count": 49, "id": "3bef301f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "DoS = log_scell.get_dos() #the density of states\n", "ax = DoS.plot()\n", "ax.set_ylim([0,None]);" ] }, { "cell_type": "markdown", "id": "76a026a2", "metadata": {}, "source": [ "As one can see, the DoS obtained by linear and cubic scaling are quite comparable. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }