{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Molecular Dynamics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tutorial runs two molecular dynamics (MD) simulations with BigDFT and analyses its outputs. We investigated the dynamics of a water environment with NVE ensamble at DFT level and the motion of a 38 atoms Lennard-Jones cluster with fixed NVT." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from BigDFT import Logfiles as lf\n", "from BigDFT import Calculators as calc\n", "from BigDFT import Inputfiles as I\n", "import matplotlib.pyplot as plt " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A water environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To study and reproduce bulk water, we simulate the motion of four water molecules placed in a periodic cubic cell. The cubic unit cell side has been chosen to adjust the effective\n", "density of water to its ambient density." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Box size: 4.932703172202558 [AA]\n" ] } ], "source": [ "n = 4 # number of water molecules\n", "m = 18.01528 # [g/mol] molar mass\n", "NA = 6.02214076e23 # [1/mol] Avogadro number\n", "rho = 997e-27 # [g/AA^3] water density at room temperature\n", "\n", "volume = ( n * m / NA ) / rho\n", "box_size = volume ** (1.0/3.0)\n", "print('Box size: {} [AA]'.format(box_size))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from io import StringIO\n", "input_file = StringIO(\"\"\"HETATM 1 H HOH 0 2.924 3.219 1.550 1.00 0.00 H \n", "HETATM 2 H HOH 0 4.098 2.445 0.948 1.00 0.00 H \n", "HETATM 3 O HOH 0 3.850 2.984 1.714 1.00 0.00 O \n", "HETATM 4 H HOH 1 1.432 4.761 0.988 1.00 0.00 H \n", "HETATM 5 H HOH 1 0.066 4.168 0.637 1.00 0.00 H \n", "HETATM 6 O HOH 1 0.884 3.972 1.119 1.00 0.00 O \n", "HETATM 7 H HOH 2 2.280 0.928 1.171 1.00 0.00 H \n", "HETATM 8 H HOH 2 0.907 1.567 1.386 1.00 0.00 H \n", "HETATM 9 O HOH 2 1.335 0.779 1.019 1.00 0.00 O \n", "HETATM 10 H HOH 3 4.694 4.067 3.659 1.00 0.00 H \n", "HETATM 11 H HOH 3 3.377 4.728 4.071 1.00 0.00 H \n", "HETATM 12 O HOH 3 3.945 4.569 3.303 1.00 0.00 O \"\"\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from BigDFT.Atoms import AU_to_A\n", "from BigDFT.IO import read_pdb\n", "from BigDFT.UnitCells import UnitCell\n", "\n", "sys = read_pdb(input_file)\n", "sys.cell = UnitCell([box_size, box_size, box_size], units=\"angstroem\")" ] }, { "cell_type": "code", "execution_count": 5, "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": [ "from BigDFT.Visualization import InlineVisualizer\n", "viz = InlineVisualizer(400,300)\n", "viz.display_system(sys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geometry optimization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To avoid large forces (and associated large spatial displacements) at the first MD step,\n", "we relaxed the initial geometry of the atomistic system.\n", "\n", "Soft norm-conserving pseudo-potentials including\n", "non-linear core corrections along with the Perdew-Burke-Ernzerhof\n", "functional were used to describe the core electrons and exchange-correlation\n", "both for the geometry optimization and the MD trajectory.\n", "The wavelet basis functions were distributed on an adaptive uniform mesh\n", "with a resolution of $h_{\\text{grid}} := h_x = h_y = h_z = 0.40$ Bohr.\n", "The $h_{\\text{grid}}$ of a single KS SCF calculation has to be chosen to guarantee a well converged energy\n", "as done for an usual DFT single point calculation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a Calculator with OMP_NUM_THREADS=2 and command mpirun -np 2 /Users/wddawson/Documents/CEA/binaries/bds/install/bin/bigdft\n" ] } ], "source": [ "water_inp = I.Inputfile()\n", "water_inp.set_hgrid(0.4)\n", "water_inp.set_rmult([8.0, 10.0])\n", "water_inp.set_xc(\"PBE\")\n", "water_inp.set_symmetry(False)\n", "water_inp.optimize_geometry(method=\"SBFGS\", nsteps=500, frac_fluct=3.0, forcemax = 1e-4, betax=1.0)\n", "# water_inp['geopt'] = {'method': 'SBFGS', 'ncount_cluster_x': 500, 'frac_fluct': 3.0, 'forcemax': 1.E-4, 'betax': 1.0E0}\n", "study = calc.SystemCalculator(skip=True) " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating the yaml input file \"./water-geopt.yaml\"\n", "Executing command: mpirun -np 2 /Users/wddawson/Documents/CEA/binaries/bds/install/bin/bigdft -n water-geopt -s Yes\n", "Found 71 different runs\n" ] } ], "source": [ "water_geopt = study.run(posinp=sys.get_posinp(), name=\"water-geopt\", \n", " input=water_inp) #Run the code with the name scheme geopt" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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U7BLG94tv1TPF1iWoAoUx5iPgo8TExJsDnRellO8lp+ay+XB2i3kYbzqYxdrvT7B4+gB+GiTzOtUlqAKFUip4OZcWtVbZCA+18ObC8c0eLNwDVWWVjUVvf0vnmHBun3pOs+ajuWmgUEq1Cl/uO+VaWtTZYNycgSI5NZdfuK2BbXPMUR0WIuw5UdAiSjj+ElS9nnQchVLBKyrs9CC2QDQYbz6cTVmlDcPpIAH2+Z2CrZdTTUEVKHQchVLBK6uoHICYiNCAVDt1aBMOONbADrEQHiKESHD2cqpJq56UUq3C1hT7ILbC8kp6dYhq9ut/seckMREhLJjUj8kDOwG0qIZ1f9JAoZRq8fJLrfxwsoBJ/Tuy8WAWO4/lc/GQyGa7/vfH8vjv/kx+d8kgfj3tdPf7YA8QTkFV9aSUCk7fpuZiDPxqYgICvLrxcLNOk/H8Vz8SGxXGDRf0abZrtiQaKJRSLd6WIzmEhQhRYSEYIOlwDr94ZXOzBIuLnvwv/9mXwaxhXYmJDPP79VqioAoU2utJqeD03/0ZdGwbwce70l1pzTGnUnJqLoezSgD44LvjQTfZn7eCKlBoryelgk/SoSz2nywkPb+MVclphIg9vTl6G3222y0wVQXfZH/eCqpAoZQKPmt2nHC9rqqyuRqQn7t2lN8bk4/llAKcNd1g66O9npRSLZrFIq7XYaEWfjK0K1tTcl3jGvwlp7iC9QcymD64M+f3iTsrusHWRwOFUqpFO5lfRo/2kVw3rg/j+8XTLjKUP32yj+N5pST6+Fruczm9u+0YZVYbl4/szmUje/j4Sq2LBgqlVItlsxm+PZrLJUO6usYvlFRUAnA8r9Sn10pOzeWqFzdhAIvbXE6/X72THnHRZ21pArSNQinVgh3OKiavxFrtIR0dHkpcdBjHc30bKDYfzsY5hZP7XE7BuGJdYwVVoNDusUoFl+TUHABGJ1T/Nt+9fZTPSxTj+3ZwvQ4PteBsGTmbG7GdgqrqSRcuUiq4JKfm0j46jH4d21RL79E+iiNZxT69VqcY+5QgFw/pwu1T7OtLnC1zOTUkqAKFUiq4JKfmMrp3HCJSLb1HXBT/O5iFMabWtqba7ii93D1jIOd2awecPXM5NSSoqp6Uai1mPbuBSX/96qwd6euN3OIKDmUW16p2AnuJoriiivxSq8+utz01l5jIUAZ2ifHZOYOFBgqlmllyai77ThaSllvabPMVtUbfHrXfl9G96w4UAGkNNGg3JiAnp+Ryfu84Qiy+KaEEEw0USjWzzYezMY5eNdqjpn7bU3MJtQjn9Wpfa1uPOHug8NSg7R6Qr3pxE2Me+3e9ASO/xMqBjEIStaqpThoolGpm7j1otEdN/ZJTcxnaI5ZItyVQnZwlCk9dZN0DsgEyCyvqLcF9e9Q+jXld1VxKA4VSzc69gTQQS3q2BtYqG98fy6uz2gnsy5JGhlk4UaNE4V7VVFcArqinBPe79763vzC1Nik0UCgVUBok6rbnRAHllTYS6/mGLyK1xlLUbPsBexVV9/anV8IThPH94qsFlG0pOWQVVwBw07+2aZtRHYIqUOiAO9UaWKtsgc5Ci+d8WHsKpD1qBIqabT/r92dwPLeU68b2Zki3GNpGhGIzhvT80mptF/Ne2uw6h7YZ1S2oAoWuR6Fag8zC8kBnocV74asfiQi10KVd/eti94yLqtZGMaLn6b/7sFALa747DsD5veNYt/hCvrpnCuGhFn6/ame1totKt/k6tM2obkEVKJRqDU4WlAU6C002d1kSc5cl+fUaxhjyy6yEiHisBuoeG0V2cQVl1ioAyqynS2pPXDWCEke6Mwx0jonkunG9KamoqnUui8DkAR21zageGiiUaman8u2BolNMRIBz0niFZVaO55X6tR7/s90nqbJBibXK4ziTml1kv/kx07XtVEE5WUX2docFbu0OE8/pWOe5wkMt3HXxQA0S9dBAoVQzS3cEij4dogOck8Zxbyy+7mX/DRR8fXOq67WnNgNnF9k73viWucuSeHfbMYZ2t0+9selQdp3n2H+q0JUeItA5JpyecVFakmiAzvWkVDM75ah6OuzjSe38rVpjsWP9aF8/XK9eusk1Ihs8txk4SxQZhWUUlVkpq7Qxtm8HDmYUke7WyO1+jvH94l1rTYSFWnjxl4kaILzQ5BKFiPh3HUKlgpSzjaLMWtWiumI21P7g/sAOsYhfGn1P5JVSZYMuMRENftN3jqHILbFy3FFKe2vLUTrFRLDvpL3k0C02sto5RveJY3DXGC1FNJJXgUJE1otIgtv7scA2P+VJqaD2o6P6o6TCcx18cyso9dz+cG6305Pl/XJ8H58/ZJNTcjieZ3/g55dZefbaUR6vsS2ldj4rq2xkOXqV9evUhqT7p9c6x7rFF7Lx3os0SDSCtyWKx4HPROQOEXkMWAr8yn/ZUip4ncg/3euppfTb33Qwq8GJCt3zGR1ee1qNM7Vu90nXa2/ui7MayV1YqIWwEHviOR3b+jyPZyuvAoUx5nPgNuBZ4CZgljHmW39mTKlgZIyhuLzS9T4spGX021++KcX12lppY/E739WqhtpwIIvIMAvto8PIdvQo8qXYqDDXa2/GM7hXIyXER9MzLoqHZg+lqNze/XX9gYwWU1pr7bxqzBaRJcA1wIXACGC9iNxtjPnEn5lTKtjkl1qxVhnahodQVFHF3ZcMCngVyDVLN/HdsTzX+7BQC+0iqz8a5i5L4vu0PC7oF096fpmr66kvOcdD9GgfyXPzzvfqvqxbfGG19y/89+Dpda9txi8N7mcjb6ueOgJjjTFJxphlwCXAXf7LllLB6fpXtwDw/y4fRniIpUWM0j6RX4a1yhAeYn8cvHDd+QAczy1xfSMvs1ZRZrVx4cBOdGwbQU6xb/Lt3oC+L72AqLAQesZFN/nh7l4dpaOsfcerEoUxZnGN96nADL/kSKlm5nxQrbz1Ar9fq6LSPno4IT6a8/u0543NqXx/LK9Zrl2X5NTcWov/pGQXsy+9EIO9u+q1Y3q59olvG06HNuEcyy2p83xnci9/OFnIzGFdeXruyEYf6+Ssjiooq2ywMVx5z9uqp07AvcAQwDX5ijHmIj/lq+b1rwB+CnQGXjDGfNEc11XK1wrK7Et3ZhWVM+Gcjmw+nOPVJIH+CmZf/XDK9brKZs/Hs//58XT1jYG3th5z7fP7VTuZPrizz9ooCsusFJRVcsnTX5OeX8bgrme+DGlMZBgxkWEaJHzI2wF3bwIrsT+sbwNuBDI9HuEgIsuB2UCGMWaYW/pM7I3jIcArxpi/1HcOY8yHwIciEgc8CWigUD7jfFglp+b69eGSnJrLyXx7lc1dK3fw0OwhjutXVtuvOUs47qPDw0ItWGyGIrfG9sgwCz8d3o3V39on2LNW2igoq6SovJIyaxU3Lt9aLa8N3Uv3BvLCMiv7ThZiDDg7L53brd0Zf6ZAlc6CmbdtFPHGmFcBqzHma2PMTcB4L49dAcx0TxCREOAF4FLspZR5IjJERIaLyMc1fjq7HfoHx3FK+URyai770h3dQv04LQU4RjY7XlsrbWQXVyDYB441dF1v51hq7KR97aLs42a7xETw5sLxRIZa7KOWQ4Se7e2D1a4b16davb9zmoyc4uqliu0pOa57ed3Lm5n17AaPeckvtVabxRVgcLczL1Eo3/M2UFgd/6aLyE9FZBTQ05sDjTEbgJwayWOBg8aYw8aYCuAd4HJjzC5jzOwaPxli91fgU+2WG5yaY1bSuvz3hwzXQ6qs0latKsbXai6BGt8mAgMUV1Qxd1lStUntnNJyS7j9jWT2pnse49AY7vfaObrZOR2Gs2tpZZWhXZS9+qbmaObzHavOuVc/FZRZuXf1Lte9LK+0UVBqpSZnwDtVULvnlADHcupf2lQFjreB4k8iEgvcDdwDvAL89gyu2wM45vY+zZFWnzuBi4GrROS2unYQkVtEZLuIbM/M9KpWTAUxbwNPRmH1Kb9XJ6eR4qc5mEb3iSMmMpRQi/DmwvHklpx+UFbaDAv+tZ3/++wHcksqSM0uZv7yrUx9Yj2f72ncQLTGSM8vRQRCLVKtxGMR+OmI7q79YiLD6NE+itF94ohva5/19p73dlBYZiUlu5hLnt5ASlYR7uPfymu0vXy6K90V8FKySyh3NOzHRNgH7xloUSPV1Wne9nr62PEyH5jmg+tKHWn1rlZrjHkOeM7TCY0xLwEvASQmJurKt6pBpRVV/GdfBm3DQ2jfJpxfT+vP/332A1e+uIlX549hZK/2Pr9miAjhoRZX/b1zgrrwUAuje7fnH+sPufY9WZDJT4d35bKRPbj19WSg4S6fjW1vOZFfRp8O0bx72wSSU3OrTZjnfh33ev/4NuGOa1WSnl/m+sP90xXDeGtLKgWlVvp0bMv/DmaBKeKtLUf55sdMPnMbee1O5PTjwBkItSG6ZfF2rqe+IvKUiLwvImudP2dw3TSgl9v7nsCJMzgfoEuhtmaFZVaO5ZSw9UjzTWexKvkYOcUV9IqPpkf7KOaN7c3q2ycQHRHCvJc211kNdaZVZFXGEOJ4MLpX6bx983jevuUCFk7u69o3RGBI91guGdqV9lGhiMAbC8bV+xDdcjj7dHuLh2/m7u0d6XmldIuNqpUfTxPmxbe1B4qi8kpXkAgRe5tDTGQYPeKiuX3qOQBkFlXwwAe7+PpAJleM6oHU+IpoEejQJlzHPrRw3vZ6+hB4FfgI8MWCv9uAASLSFzgOXAtcd6YnNcZ8BHyUmJh485meSzUPYwyvbDzC3nT7RHnXLNtM++gwesVF06VdBJ3bRdIlJpIu7SLoEnv6dVx0OJaaE/00QpXN8PI3RxjZqz3hIafP069TW96/fSI3rdjGza8l89gVw7h2bO96z9PYHkr9OrahffTpiZdrduW8dFg3lm88UutbfYc2EeSVVtLObZqLmlZ/m1atsbyub+bOxntnNU+b8FCmDjrdX8SbrqVtI+xBy713lDOvGw7Yq32/dxvpLcAtk/tx14yB7D9ZQEFZJaEWodJmXGMdZj27Qcc+tGDeBooyR/VPo4nI28BUoKOIpAF/NMa8KiKLgM+xd49dbozZ05Tzq9bJWmXj450nWPb1YX44eXoxGQF6x0UT3zac43llfHc0j+zi2n32w0KEzs4A0i6SLu0i6dwugi4xkXSNjSSrsJzyyqpaVTDOB/sNFyRwNKeEB2YNZuawbtXO3SkmgnduGc8db37Lfe/vIj2/jLsuHlCtiqSpSiqq6N6+/gn16hswFuOYUmPrkRwGdjndM8g9UEWFnT5vfd/MNxzIrBZMcqwVdG9f/7rUdRERQsT+oI8OD6FDm3BXXp0B070aKyLMwuSBnRyfwx6I3D+ve7oGiZbJ20DxrIj8Efv4BdfYfW96IBlj5tWTvg5Y5+X1vSIic4A5/fv39+VplQ+VVFSyctsxXvnmCMfzShnQuS2LLupvn6PH8VD542VDqz0wKiptZBaVczK/jIyCMk4VlHGqsNz+b0EZP2YUsfFgVq3xCADXvbyZt26uXo1ijOGlDYdIiI9mxpCudeazTUQor9yYyP3v7+LZL3/kZH4Zj/1sWJ371uSplFFSUUWU28yrde1T10Pzw19PZOyfv2R7Sg6/HN+nzmseyixyvX/iqhF1PnT7xJ8eN2HD3jDorHqqLz81JafmUmmzh5tSaxXtIkNrXUtHSAcXbwPFcOB64CJOVz0Zx/sWQ6ueWq6c4gr+tSmF15JSyC2xktgnjocvG8pFgztjsQhf7TtV70MlPNRCj/ZRrqUv61NSUUlGQTlLvz7EO9vsnerKK228t/1YtXMWllWy72Qhj/1sGCEeqq/CQiw8cdUIusVG8vxXB8koLCO/1EpRedMH55Vaq5o0RbeIMDahA9tScusNRCUV9nOXVFSRml33FBvu3+a7t4/ieG4p3RpZonDvdWUMFNQRoJ3X8raUoIPkWjZvA8XPgH6OMQ/qLNbYOvm03BJe+eYIK7cdo9RaxcXndua2KeeQmNCh2n6+qHqIDg8loWMoVyf24t3tx7A5Rvy+t/0Yw3rEur6Jp+eXEd8mnCvPb3gokIhw908G0TU2kj98sNtVbXPdy5t5Y+G4RvcyKqmoJDq8aSsQJybE8cmudNSw6XAAABkzSURBVDrFhBMRWj3Y2IyhtKKKrrGRWET4ZNdJFl00oNY5juWcDiBFjulEGgrANbkvJ2oRas0064kGhNbJ2//h74H2QIYf83LGtOqp5diXXsCyrw/x0c50BLh8ZA9undKvWv26v7hXezz+8+Es33iEP3y4m33pBWQWlpFXamXemF5Ehnn/zf4X4/qwPSWXD76zT2VRXmlj7rIkHDUwdVZx1WSzGcqstmptCXWp72E6xhFcC8sqiWhb/Rxl1ioM9gWFwkMs7Esv4HBmEf06VV+8Jy23FIvYlzLNL7WXBLrFNq5EodVKZx9vA0UX4AcR2Ub1NorL/JKrJmpNVU/NOZ9PczHGsOVIDku/PsT6/ZlEh4cwf0ICCyb1pXsjv7WeKWcJZfKATkw4pyNPfrGfF93GKLz/3XGuSuzVqIfcL8f34cMdxzGOKS76d27LPkdvrfJKGw+8v4vHrxzuKmXMenYDMZFhrv/jUsd6C01dHe7cbu1oGxFKYVklHR2D3pzXcq7lcOdFAxjXrwMXPP4V63al882PWYD992zusiQOnCokIjSEthEhZBZVECJSrTrKW96UAIPpd/ts522g+KNfc6FaNZvN8MXeUyz9+hA7juUR3yacu2cM5PoL+lTrCuqJPx8qIRbh3pmDOZZTwsc70wH72sqNHdg1uk8c57p9kwb7NNw2Y7/GsdwSfv6PTa79LUK12VBLKs4sUIRYhPP7xLHtiH1GnNXJaa5uxU73vb+TNxeOJ7FPHB/vTHetGjd3WRJ70wuoshkiQi3ERoWRWVSBCH6fDFG1ft4uhfo1kAKEOV5vA3TOpbNceWUVK7cd5eKnv+a2N5LJKa7g0SuG8b/7LuLO6QO8DhLN5VcT+7oGfDV1YJf7VBbuA9TevfUCtj14MdMHnx6TYKvR0FvqCBRRTWyjABibEEeptYqDGUXcu3pnre3O8ROzhnfjh5OFrmuCvcRXbrVxxageLHHMXFtpMzpthmqQt+tR3AzcAnQAzsE+L9NSYLr/stZ42kbhvaZWfRWWWckrtbLkw918vuckGYXlDO3ejufnjeLSYV0JDfF2+jD/qutz1SwR+OJbdM0qmDum9ecrx0SDQvWG3hKrPWg0tUQBuAbcOWeerckZAHu0j+KRj/eSU1xBj/DT1X5VxtAzLordJwpcaTpthmqIt19tfo19xtctAMaYH2tM/90itKY2itbovz+cclV1vL45leE92vG3a85jUv+OPhmM1hz8PbBrdJ84BnWNYf+pQtqGh1Sr/y9xlSiaHihy3QYfWsQ+nUZ4aEitkc4AYxLi2H28wDUzrLPhvWdcFJ1iIuud18kb2v5wdvH261+5e9dYEQnFwyR+rVWgpro+E82R51MFZTz68V5ufi3ZlWYRmDmsG5MHdGo1QaK5xEaF0T02ksLyKldwgNNVT9GN6G1V06QBnapVn3VsG0GP9lF0aRfpqhJzmjW8G6XWKtd1bY7FH5xrUnszr5NS4H2g+FpEHgCiRGQG8B72eZ9UE3m7EE0gpeWW8IcPdzH5r/9lxaYUJvaPdz2kwnXyNo+6tItEgEOZRa7/49ON2U1vo3BWnzkf8J56LF3qmJrEucBQlaNIkeNYB8K9vUUpT7z9jb0PWADsAm7FPvXGK/7KVFO1hjaKMmsV72w96qrC8ab/fXNLySrmH+sP8v63xxGBq0b35PYp/ekdH33WT97mbZWLc1xDSUWV6//4L5/uA86s6gm8rz7rGhtJdFgIJwvKqKyyYa2yB4pb3tjOmwu9XaBSKe8DRRT2ifteBtdSplFA3fMEBEhLbKMwxvDDyUI2/pjFhh8z2Xokx7VgC9j73/9xzW7+76rzGNL9zNcLPhM/nirkhf8eZO33JwgLsfDL8X245cJ+1cZA6ORt1dUXONx7O1kdXXGdVT9n0pjdGMmpuZQ6AtbJQtfwJyqsvl38SAU/bwPFl9hXmHPOOhaFfYLACf7IVGuXUVDGxoNZfPNjFhsPZpHp+CPt37kt88b2pnv7SB7/9AeMo//9gVNFzHruGyacE89NE/u65j9qLntO5PP3rw7y2Z6TRIWFsHByPxZO7kvnmMaN2FWntYu0T8VtjH0akE92nuBEnn01veYKFO4r1rmzWKTalOBKNcTbQBFpjHFNTWmMKRKRaE8HnE1KK6rYmpLDNwcy2XgwyzVtdoc24Uzs35HJA+w/7rN0fvjdcVcVzjmd2vD21mO8lpTCwte207djG341MYErz+9Jm4im12d7UlhmJauogqte3MT21FxiIkJZNK0/v5rYlw5tWtb4B19qrt46MZFhnNs1hh8zihjcNYbo8FDXQ7sxU4ecCfc5mZwEeOTyYVoiVI3i7VOoWETOd04rLiKjgbN2FXSbzbA3vcBRYshk25FcKqpshIdYSEyI496Zg5k8oCNDurWrt2RQswrn9qnnsHByXz7dfZJXNx7hoTV7ePLz/cwb25sbJiQ0euK2uhhjOJhRxOubU11tJBmF5cwb04v7Zp3rGsWrzowzGM1dlkRcdDip2SX2EdqOqqeIUN+NNfEU+NznZAq1CCfyy+jfqQ3Xjat/ISal6uJtoFgMvCcizuVKuwFz/ZOllik9v9QeGH7M4n8Hs1yL6QzuGsMNF/Rh8sBOjE3ocEYNlWEhFi47rztzRnTj26N5LN94hJe/OcwrG48wc1hXFkzqy/m9G/dNsLi8kk2Hslm/P4P1+zM5nlc9vocI9OwQrUHCT6LDQ8goLKe80obB3q24ObsTuy8UlF1c0aR5nZRqMFA4Gq4nA4OBQdhLrz8YY6x+zlujnWmvJ/cpowd3jWHLkWw2HLC3MxzMsNe8dWwbwYUDOzF5QEcm9e9I53a+r8cXEdcUEWm5JbyWlMrbW4/yyc50RvZqz4JJfesdBW2M4VBmsSswbD2SQ0WVjejwECb278ivp/Unvm04t7+R3OTBVsp7zqpDZ9dYT+tfNDcdNKe81WCgMMZUicjlxpingd3NkKcmc/Z66j/kvJvX7DjeqGOPZBW71hK+eukmRKDKZq8mGNcvnrmJvZg8sCODusQ06zfCnnHRPDDrXH4zfQCrk9P45/+OcOfb39E9NpIbJySQV1JBUXkly74+RFpuKf/dn0Farr3U0L9zW26c0IepgzqTmBBXbQ0DnSa6eUSHhRBiEYrLK12dF5RqbbytevqfiPwdWAkUOxO9WQo1EI7llrD4nR1NPt5mYGxCBxZPH8DoPnHN1vjoSduIUG6ckMAvx/fhqx8yeHXjYR7/9AfX9sc//YHwEAsXDuzIrVPOYerATvTqUH9/A+3m2jwsFmFA57ak59t7PPkiUGhJQDU3bwOFsxvsI25pLW4pVKeBXWL48O4pjTpmz4l8Fr+zA2MgMszCvTMHt8iHaIhFmDGkCzOGdOGPa3bzr6RUwF73/etp57D44oF+vb4+pBpvWI9YDmYUOdootEShWh+vAoUxZpq/M+JLEaEWzqmxsldDzunUlqXrD7Wq6pjLRvbg9c2p2Ix9So1JAzoFOkuqDkO7t2NVchpgH3yn6z+o1sarfnoiEisiT4nIdsfP30Qk1t+Za26tbe4bnditdRjW4/SfSnmlTdd/UK2Otx26lwOFwDWOnwLgn/7KlPJeawtuZ6Nzu1WfmsW5/oNSrYW3bRTnGGOudHv/sIg0vbXYT1rDpIBOWtcf/Or7Pw5Ul+Qh3drp751qEm9LFKUiMsn5RkQm0gJHZhtjPjLG3BIbG3S1YqoVq1nN9NDsoc1WAlx56wUaHNQZ87ZEcRvwmlu7RC5wo3+ypFRwqVnNlFtSUc+e/qPBQp0Jj4FCRHobY44aY74HzhORdgDGmAJPxymlTnOfnM8i6Eh41eo0VPX0ofOFiKw2xhRokFCqcZy908JDLQzuGqMdD1Sr01CgcB8d1M+fGVEqmMVEhhERatFJ+VSr1FCgMPW8VkopdZZoqDH7PBEpwF6yiHK8xvHeGGMCu3anajJt3FRKectjoDDGBH42PKWUUgHlu6W2WgARmSMiL+Xn5wc6K0opFTSCKlDogDullPK9oAoUSimlfE8DhVJKKY80UCillPLI27mezgraZVQppWrTEoVSSimPtETRymkpSCnlb1qiUEop5ZEGCqWUUh5poFBKKeWRBgqllFIeaaBQSinlkQYKpZrJkG7ttJeaapVafKAQkXNFZKmIrBKR2wOdH6WUOtv4NVCIyHIRyRCR3TXSZ4rIfhE5KCL3eTqHMWafMeY24Bog0Z/5VUopVZu/SxQrgJnuCSISArwAXAoMAeaJyBARGS4iH9f46ew45jJgI/Cln/OrlFKqBr+OzDbGbBCRhBrJY4GDxpjDACLyDnC5MeZxYHY951kLrBWRT4C36tpHRG4BbgHo3bu3T/KvlFIqMFN49ACOub1PA8bVt7OITAV+DkQA6+rbzxjzEvASQGJiovFFRpVSSgUmUEgdafU+2I0x64H1/sqMUkopzwLR6ykN6OX2vidwwhcn1jWzlVLK9wIRKLYBA0Skr4iEA9cCa31xYl0zWymlfM/f3WPfBpKAQSKSJiILjDGVwCLgc2Af8K4xZo+PrqclCqWU8jExJvjafRMTE8327dsDnQ2llGpVRCTZGFNrvFqLH5mtlFIqsDRQKKWU8iioAoW2USillO8FVaDQXk9KKeV7QRUolFJK+V5QBQqtelJKKd8LqkChVU9KKeV7QRUolFJK+Z4GCqWUUh5poFBKKeVRUAUKbcxWSinfC6pAoY3ZSinle0EVKJRSSvmeBgqllFIeaaBQSinlUVAFCm3MVkop3wuqQKGN2Uop5XtBFSiUUkr5ngYKpZRSHmmgUEop5ZEGCqWUUh5poFBKKeVRUAUK7R6rlFK+F1SBQrvHKqWU7wVVoFBKKeV7GiiUUkp5pIFCKaWURxoolFJKeaSBQimllEcaKJRSSnmkgUIppZRHGiiUUkp5FFSBQkdmK6WU7wVVoNCR2Uop5XtBFSiUUkr5ngYKpZRSHmmgUEop5ZEGCqWUUh5poFBKKeWRBgqllFIeaaBQSinlkQYKpZRSHmmgUEop5ZEGCqWUUh5poFBKKeVRqwgUItJGRJJFZHag86KUUmcbvwYKEVkuIhkisrtG+kwR2S8iB0XkPi9OdS/wrn9yqZRSypNQP59/BfB34DVngoiEAC8AM4A0YJuIrAVCgMdrHH8TMALYC0R6e9H9+/czderUamnXXHMNd9xxByUlJcyaNavWMfPnz2f+/PlkZWVx1VVX1dp+++23M3fuXI4dO8b1119fa/vdd9/NnDlz2L9/P7feemut7X/4wx+4+OKL2bFjB3fddVet7X/+85+ZMGECmzZt4oEHHqi1/ZlnnmHkyJH85z//4U9/+lOt7cuWLWPQoEF89NFH/O1vf6u1/fXXX6dXr16sXLmSF198sdb2VatW0bFjR1asWMGKFStqbV+3bh3R0dH84x//4N13a8fs9evXA/Dkk0/y8ccfV9sWFRXFp59+CsCjjz7Kl19+WW17fHw8q1evBuD+++8nKSmp2vaePXvyxhtvAHDXXXexY8eOatsHDhzISy+9BMAtt9zCgQMHqm0fOXIkzzzzDAC//OUvSUtLq7b9ggsu4PHH7b96V155JdnZ2dW2T58+nSVLlgBw6aWXUlpaWm377NmzueeeewBq/d6B/u7p717r/d1z8mugMMZsEJGEGsljgYPGmMMAIvIOcLkx5nGgVtWSiEwD2gBDgFIRWWeMsdWx3y3ALQARERG+/BhKKXVWE2OMfy9gDxQfG2OGOd5fBcw0xix0vL8eGGeMWdTAeeYDWcaYjz3tB5CYmGi2b99+hjlXSqmzi4gkG2MSa6b7u+qpzrzUkdZgtDLGrPB9VpRSSjUkEL2e0oBebu97Aid8cWJdClUppXwvEIFiGzBARPqKSDhwLbDWFyfWpVCVUsr3/N099m0gCRgkImkissAYUwksAj4H9gHvGmP2+Oh6WqJQSikf83tjdiBoY7ZSSjVefY3ZrWJktlJKqcDRQKGUUsqjoAoU2kahlFK+F5RtFCKSCeQBniJGrIftdW1rSlpHIKuh/PqAp8/iq+Ma2re+7Y1Jr5lW831z3M+m3svGHNvUe+lpm97Ppm1v6t96sN7LPsaYTrVSjTFB+QO81NTtdW1rShqwvSV8Vl8c19T72Zj0mml1vPf7/WzqvWzMsb7+3dT72fx/68F6L+v7Caqqpxo+OoPtdW07kzR/a+o1G3NcU+9nY9JrprWme9mYY339u1lfut7Phrc39e86WO9lnYKy6qmlEJHtpo6uZqpp9H76lt5P3wn2exnMJYqW4KVAZyDI6P30Lb2fvhPU91JLFEoppTzSEoVSSimPNFAopZTySAOFUkopjzRQBJCItBGRZBGptQSsahwROVdElorIKhG5PdD5ac1E5AoReVlE1ojITwKdn9ZORPqJyKsisirQeWkqDRRNICLLRSRDRHbXSJ8pIvtF5KCI3OfFqe4Faq8Wf5bxxf00xuwzxtwGXAMEbTfFhvjoXn5ojLkZmA/M9WN2Wzwf3c/DxpgF/s2pf2mvpyYQkQuBIuA1c3ot8BDgADAD+yp+24B5QAjweI1T3ASMwD7sPxIv1wIPVr64n8aYDBG5DLgP+Lsx5q3myn9L4qt76Tjub8Cbxphvmyn7LY6P7+cqY8xVzZV3XwrEmtmtnjFmg4gk1EgeCxw0xhwGEJF3gMuNMY8DtaqWRGQa0AYYApSKyDpjjM2vGW+hfHE/HedZC6wVkU+AszJQ+Oh3U4C/AJ+ezUECfPe72dppoPCdHsAxt/dpwLj6djbGPAggIvOxlyjOyiDhQaPup4hMBX4ORADr/Jqz1qdR9xK4E7gYiBWR/saYpf7MXCvU2N/NeOAxYJSI3O8IKK2KBgrfkTrSGqzXM8as8H1WgkKj7qcxZj2w3l+ZaeUaey+fA57zX3Zavcbez2zgNv9lx/+0Mdt30oBebu97AicClJdgoPfTd/Re+tZZdz81UPjONmCAiPQVkXDgWmBtgPPUmun99B29l7511t1PDRRNICJvA0nAIBFJE5EFxphKYBHwObAPeNcYsyeQ+Wwt9H76jt5L39L7aafdY5VSSnmkJQqllFIeaaBQSinlkQYKpZRSHmmgUEop5ZEGCqWUUh5poFBKKeWRBgrVoolIlYjscPvxZvr2Fk9EbhORG7zYb5SIvOJ4PV9EMmvcjyH+z22d+ZotIg8H4tqq+ek4CtWiiUiRMaatj88Z6hg01eKJyHvAn4wx3zsmkEw0xizy8TUafT8cM8x+C0w0xpT4Mj+q5dEShWqVRCRFRB4WkW9FZJeIDHakt3EsNrNNRL4Tkcsd6fNF5D0R+Qj4QkSiReRdEdkpIitFZIuIJIrIAhF52u06N4vIU43I11QR+dpx7gMi8hcR+YWIbHXk8xzHfv9PRO5xvF4vIn917HNARCY70mOAEcaY77245nqxr+73g4i86XiQIyKjHflJFpHPRaSb2zX/LCJfA4tFZIzjXiSJyBPiWKhHRL4RkZFu1/qfiIww9m+Y6wnSabVVdRooVEsXVaOqxX3FtSxjzPnAi8A9jrQHga+MMWOAacATItLGse0C4EZjzEXAHUCuMWYE8Cgw2rHPO8BlIhLmeP8r4J+NzPN5wGJgOHA9MNAYMxZ4BfsU3nUJdexzF/BHR1oisLvGfnNr3I8oR/oox7FDgH7ARMdneB64yhgzGliOfbprp/bGmCnGmL85PuNtxpgLgCq3fV7BvtIdIjIQiDDG7HRs2w5M9uqOqFZNpxlXLV2pMWZkPdved/ybjH0tCoCfYH/QOwNHJNDb8frfxpgcx+tJwLMAxpjdIrLT8bpYRL4CZovIPiDMGLOrkXneZoxJBxCRQ8AXjvRd2INXQ58lwfG6G5BZY7+VNaueHIWHrcaYNMf7HY5z5AHDgH879gkB0t3P5di/PRBjjNnkSH+L0yWF94AlIvI77CszrnA7PgPoXs/nUUFEA4Vqzcod/1Zx+ndZgCuNMfvddxSRcUCxe5KH874CPAD8QB2lCRH5Gae/9S80xmyvJ18ANrf3Nur/m6vrs5RiD3TecL+m8xwC7HGUEurivB/13gtjTImI/Bu4nNrrkUc68qiCnFY9qWDzOXCnWx39qHr224j9wYej59Bw5wZjzBbs6w1cB7xd80BjzAfGmJGOn5pBwpf2Af3P4Pj9QCcRuQBARMJEZGjNnYwxuUChiIx3JF1bY5dXsC9ktM2tRAYwkNpVYyoIaaBQLV3NNoq/NLD/o0AYsNPRIPtoPfv9A/tDdCdwL7ATyHfb/i7wP8dDNCCMMT9gX440xi25ZhvFBA/HVwBXAX8Vke+BHUB9+y8AXhKRJOwlDNe9MMYkAwXULl1NAz5p7OdSrY92j1VnJREJwd7+UOboifQl9kbnCsf2j4GnjTFfBjifvwUKjTGv+Pk6bY0xRY7X9wHdjDGLHe+7Y+/hNNi5truIdAHeMsZM92e+VMugJQp1tooGNjq+aX8A3G6MqRCR9iJyAHsjekCDhMOLVG9/8JefOkoou7H3ZPoTgNgHBW4BHnQGCYfewN3NkC/VAmiJQimllEdaolBKKeWRBgqllFIeaaBQSinlkQYKpZRSHmmgUEop5ZEGCqWUUh79f+TbDgjuMs0qAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "water_geopt.geopt_plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can extract the steps of the geometry optimization process, and then visualize." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from copy import deepcopy\n", "optsys = []\n", "\n", "for inst in water_geopt:\n", " optsys.append(deepcopy(sys))\n", " optsys[-1].update_positions_from_dict(inst.log[\"Atomic structure\"])" ] }, { "cell_type": "code", "execution_count": 10, "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": [ "from BigDFT.Visualization import InlineVisualizer\n", "viz = InlineVisualizer(400,300)\n", "viz.display_system(*optsys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Molecular dynamics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we run a molecular dynamics trajectory with fixed NVE (Number of particles, Volume, Energy) conditions.\n", "To fix the MD time step, such a value has to be small enough to guarantee a proper integration of\n", "the MD trajectory in terms of Energy conservation and a low ratio (below $\\sim 2 \\%$) between the kinetic energy and the total energy. It is import to check that Energy is conserved during an NVE MD run. On the other side, the time step should not be too small to reduce the total number of MD steps needed\n", "to reach the desired overall time for the evolution of the target properties to be observed.\n", "\n", "Only the $\\Gamma$ point has been used\n", "for the *k*-space integration during the MD trajectory.\n", "The choice of *k*-points both for the geoemetry optimization and MD depends on the cell sizes.\n", "In the present case, although the cell is small,\n", "we simply want to save computational time and we placed at $\\Gamma$ point.\n", "The molecular dynamics simulations use the Born-Oppenheimer method.\n", "The time step for the MD simulation has been set to 0.5 fs.\n", "To accelerate the tutorial, we run only ten steps." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "del water_inp['geopt']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialize a Calculator with OMP_NUM_THREADS=2 and command mpirun -np 2 /Users/wddawson/Documents/CEA/binaries/bds/install/bin/bigdft\n" ] } ], "source": [ "water_inp['md'] = {'mdsteps': 10, 'timestep': 20.6706866726, 'temperature': 330.0, 'print_frequency': 10}\n", "#{'thermostat': 'nose_hoover_chain','restart_pos': 'Yes','restart_vel': 'Yes','restart_nose': 'Yes'}\n", "# include ** 'thermostat': 'nose_hoover_chain' ** to run an MD trajectory at the fixed temperature.\n", "# include the three restart keys for subsequent the restart an MD run.\n", "study = calc.SystemCalculator() #Create a calculator" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating the yaml input file \"./water-md.yaml\"\n", "Executing command: mpirun -np 2 /Users/wddawson/Documents/CEA/binaries/bds/install/bin/bigdft -n water-md\n", "Found 11 different runs\n" ] } ], "source": [ "water_md = study.run(posinp=optsys[-1].get_posinp(),\n", " name=\"water-md\",input=water_inp) #Run the code with the name scheme md" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'istep': 0, 'T': 330.0, 'Eke': 0.018811, 'Epe': -68.900582, 'Ete': -68.881771, 'tcpu': 0.0}\n" ] } ], "source": [ "print(water_md[0].log['(MD)'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "md_trajectory = []\n", "md_steps = []\n", "temperatures = []\n", "kinetic_energies = []\n", "tot_energies = []\n", "ene_ratio = []\n", "for log in water_md:\n", " md_trajectory.append(log.log['Atomic structure'])\n", " md_info = log.log['(MD)']\n", " md_steps.append(md_info['istep'])\n", " temperatures.append(md_info['T'])\n", " ekin = md_info['Eke']\n", " etot = md_info['Ete']\n", " ratio = ekin / etot * 100.00\n", " kinetic_energies.append(ekin)\n", " tot_energies.append(etot)\n", " ene_ratio.append(ratio)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we check than the MD total energy is constant during the whole run." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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hOQf8z/8tYXdBEXedN4ikGjwkeXWTxB1nD+CYXq259YUFfJK7NeyQXA3mCcnVebOX5/HC/LVcc3xPerdvGnY4NU695CQmff8IOrdoyC3PL6Cw2MsLucrxhOTqtL37i7ntxYWktWnMtSf0CjucGqt5o3r8+qz+5G7ew9S5qytu4FwUnpBcnfbgrOWs2prPH84dRIN6yRU3cGU6sW87RqS14sE3l7OnwLuCu2/PE5Krsxav28ljs3O4MKMLR/dsHXY4NZ4kbj29L5t3F/DY7Jyww3E1kCckVycVlxi3vrCAlo3q8csz+oUdTq0xtFtLzhjUgUffyyFvV0HY4bgaxhOSq5Oe/nAFn6/ZwW++O4AWjVLDDqdW+flpfSkoKuHPs5aHHYqrYTwhuTpn7fa93Dsji1F92vLdwzuGHU6tk9amMeNGdOWZT1aRu3lP2OG4GsQTkqtTzIzfvLQQM7hzzMA6NSR5dbrhpN6kpiRx74ylYYfiahBPSK5OeX3hBmYt3cTNp/ama6tGYYdTa7VtWp+rvnMYr32xgfmrtoUdjqshPCG5OmPH3kJ+O30RAzs347KRPcIOp9a76rjDaNMklbteX+rDnruYeEJydcY9byxly+4C7j7vcFLq+JDk1aFJ/RRuOCmdT3K38nbWprDDcTVAKH8rJbWSNFPS8uCzZRn7jZaUJSlb0sRY2ku6Ndg/S9JppdanSnpU0jJJSyWdX2rbhZIWS1ok6V/xOm8XnrkrtvKvj1cx4dg0BnZuHnY4dcbYEd1Ia9OYe17PotiHqHAVCOtn4kRglpmlA7OC5a+RlAw8DJwO9AfGSepfXvtg+1hgADAamBQcB+A2YJOZ9Q6O927QJh24FTjGzAYAP63603VhKigq5tYXvqBzi4bceErvsMOpU+olJ/Hz0/qQtXEXz3+6JuxwXIILKyGNAaYE81OAc6LsMwLINrMcM9sPTA3aldd+DDDVzArMLBfIDo4DcAVwF4CZlZjZ5mD9VcDDZrYt2Ob3FmqZv72TQ/am3fz+3IE0SvURV6rb6QM7MKRrC/40cxn7CovDDsclsLASUnszWw8QfLaLsk9noHSVxjXBuvLaR20jqUWwfKekTyU9J6l9sK430FvSHEkfSRpdVtCSrpaUKSkzLy8v9rN1ocnetJuH387m7MGdOKFPtD9mLt4OlBRav2MfT85ZEXY4LoHFLSFJelPSwijTmIpbRw4RZV1FN6HLapMCdAHmmNkw4EPgvmB7CpAOjALGAZNLJbCvH8jsUTPLMLOMtm3bVnwGLlQlJcYvX/yChqnJ/Pqs/hU3cHFz5GGtOalvOya9k822PfvDDsclqLglJDM72cwGRpleBjZK6ggQfEa7TbYG6FpquQuwLpgvq31ZbbYA+cCLwfrngGGl2rxsZoXBbb4sIgnK1XBvZ23ik9ytTDy9L22b1g87nDrvF6P7sqegiIffzg47FJegwrplNx0YH8yPB16Oss9cIF1SmqRUIp0VplfQfjowVlJ9SWlEEssnFnkJ4hUiV0EAJwGLg/mXgBMAJLUhcgvPSxXXApNn59KxeQMfVjtB9OnQlPOHdeHpD1eyemt+2OG4BBRWQrobOEXScuCUYBlJnSS9BmBmRcB1wAxgCTDNzBaV1z7YPo1IsnkDuNbMDjxFvQW4XdIC4FLg5mD9DGCLpMXA28DPzWxL3M7cVYuFa3fwYc4WLj+mB/X8naOEcdOpvZHg/pnLwg7FJSD5G9SVk5GRYZmZmWGH4cpw47Of8Z9FG/jg1pNo3rBe2OG4Uu5+fSmPvPclr15/LAM6+TthdY2keWaWEW2b/3R0tc6GHft45fN1XDi8qyejBPSjUT1p3rAe97yRFXYoLsF4QnK1zlMfrKDEjCuOSQs7FBdF84b1uO6EXry3LI852ZsrbuDqDE9IrlbZU1DEvz5eyeiBHbyadwK79OjudG7RkLteX0KJlxRyAU9IrlZ5LnM1O/cVceV3Dgs7FFeO+inJ/Oy03ixcu5NXFqyruIGrEzwhuVqjuMR4Ys4KhnVrwbBuUev1ugQyZnBn+ndsxn3/yaKgyEsKOU9IrhaZuXgjq7bm+9VRDZGUJCae3pfVW/fyz49WhR2OSwCekFytMXl2Dl1bNeS0AR3CDsXF6LjebTm2Vxseems5O/cVhh2OC5knJFcrzF+1jcyV27h8ZBrJSdFKGrpEdcvovmzLL+SRd78MOxQXMk9IrlZ4/P1cmjZI4cLhXSve2SWUQV2ac/bgTjz+fi4bd+4LOxwXIk9IrsZbsy2f1xdu4PsjutGkvo93VBP9/LQ+FJcYD7zpJYXqMk9IrsZ7as4KBIwf2SPsUFwldW3ViEuO6s6zc1eTvWlX2OG4kHhCcjXazn2FTJ27mjMP70inFg3DDscdgutPTKdxaoqXFKrDPCG5Gm3a3NXsLihiwrFeJqima9U4lWtG9WTm4o1krtgadjguBJ6QXI1VVFzCk3NWMCKtFYd3iTrIr6thrjgmjfbN6vOH15bgIxHUPRUmJEnPSzpTkicvl1BeX7iBtdv3cpW/CFtrNExN5saTe/Ppqu3MWLQx7HBcNYslyfwV+D6wXNLdkvrGOSbnKmRmTJ6dQ1qbxpzUt13Y4bgq9L0jutCrXRP+OGMpRcUlYYfjqlGFCcnM3jSzi4FhwApgpqQPJF0uyQebcaHIXLmNz9fs4Ipj00jyF2FrlZTkJG4Z3ZecvD08m7k67HBcNYrpNpyk1sBlwJXAfOBBIglqZmW+VFIrSTMlLQ8+o1bClDRaUpakbEkTY2kv6dZg/yxJp5VanyrpUUnLJC2VdH6wvpuktyXNl7RA0hmVOSdXvSbPzqFFo3qcP6xz2KG4ODi5XzsyurfkgTeXk7+/KOxwXDWJ5RnSC8BsoBHwXTM728yeNbPrgSaV/N6JwCwzSwdmBcsHf28y8DBwOtAfGCepf3ntg+1jgQHAaGBScByA24BNZtY7ON67wfpfAdPMbGjQdlIlz8lVk5Vb9vCfxRu5+MhuNEr1F2FrI0ncekZf8nYV8Pjs3LDDcdUkliukv5hZfzO7y8zWl95Q1rjoMRgDTAnmpwDnRNlnBJBtZjlmth+YGrQrr/0YYKqZFZhZLpAdHAfgCuCuIO4SMzswVKUBzYL55oAPzpLgnng/l5QkMf7oHmGH4uLoiO6tOG1Aex55L4ctuwvCDsdVg1gSUgtJ5x00nSTpUJ4ktz+Q3ILPaMfqDJS+gbwmWFde+6htJB3oE3ynpE8lPSepfbDuduASSWuA14Drywpa0tWSMiVl5uXlxXiqrirtyC9kWuYazh7cmXbNGoQdjouzX4zuy97CYh56KzvsUFw1iCUhTQAmAxcH02PATcAcSZeW1UjSm5IWRpnGlNXm4ENEWVfRiwlltUkBugBzzGwY8CFwX7B9HPCUmXUBzgD+XlYXdzN71MwyzCyjbdu2sZyDq2L//GQlewuLufI7/iJsXdCzbRMuGt6Vf368kpVb9oQdjouzWBJSCdDPzM43s/OJPH8pAI4EbimrkZmdbGYDo0wvAxsldQQIPjdFOcQaoHTp5i7893ZaWe3LarMFyAdeDNY/R6RTBkQS7rQg5g+BBkCb8v6DuHDsLyphygcrOLZXG/p1bFZxA1cr/PSkdFKSkrh3hpcUqu1iSUg9zKz0G2qbgN5mthWo7Iha04Hxwfx44OUo+8wF0iWlSUol0uFgegXtpwNjJdWXlAakA59Y5JXvV4BRwX4nAYuD+VXBMpL6EUlIfj8uAf3fF+vYuLOACX51VKe0a9aAq76TxqsL1vP56u1hh+PiKJaENFvSq5LGSzrwj/97khoDlf3TcTdwiqTlwCnBMpI6SXoNwMyKgOuAGcASIj3hFpXXPtg+jUiyeQO41syKgza3ALdLWgBcCtwcrL8ZuErS58AzwGXmNUsSjpnx2Hu5pLdrwqjefru0rrn6+J60bpzK3a8v9ZJCtZhi+Z8bvLNzLJFnNO8Dz9f1f7QzMjIsMzMz7DDqjA++3Mz3H/uYu88bxNgR3cIOx4Vgygcr+O30RTx5+XBO6OPVOWoqSfPK6qFd7kscwcP9BWY2EHg+HsE5F4vJs3Np3TiVc4b6i7B11bgR3XhiTi73vL6U49Lb+lD1tVC5t+zMrAT4XJL/JHWhyd60m7eWbuLSo7vToF5yxQ1crZSaksTPTu3D0g27eGn+2rDDcXEQyzOkjsAiSbMkTT8wxTsw5w54Yk4uqSlJXHJU97BDcSE7c1BHDu/SnPtnLmNfYXHFDVyNEkvdlTviHoVzZdiyu4Dn563h/GGdadOkftjhuJAlJYmJp/fl+499zNMfruDq43qGHZKrQrFU+36XSJXvesH8XODTOMflHAD//HgVBUUlXHGMd/V2ESN7tmFUn7Y8/PaX7Miv7JsnLhHFUlz1KuDfwCPBqs7AS/EMyjmAfYXFPP3hCkb1aUt6+6Zhh+MSyC2j+7JzXyGT3vGSQrVJLM+QrgWOAXYCmNlyoteec65KTf9sHZt37/cRYd039OvYjPOGduHJD1awdvvesMNxVSSWhFQQVNsGQFIKFdeUc+6QmBmT38+hb4emjOzZOuxwXAK66dTeANz/n2UhR+KqSiwJ6V1JvwQaSjqFSB24V+Iblqvr3lu+mWUbd3PVdw5D8vdN3Dd1btGQy0f24IX5a1iyfmfY4bgqEEtCmkikttsXwA+JDNHwq3gG5dzk2Tm0a1qf7w7uFHYoLoH9eFQvmjWoxz1vLA07FFcFYullV2Jmj5nZBWb2vWDeb9m5uMnasIvZyzczfmQPUlNi+c3k6qrmjepx7Qk9eScrj/eWeU3kmi6WXnbHSJopaZmkHEm5knKqIzhXN02enUPDeslcfKQXCHEVGz+yBz1aN+J3ry6msLgk7HDcIYjl5+fjwP1EiqsOBzKCT+eq3KZd+3j5s3V874gutGiUGnY4rgaon5LMr8/qT/am3Tz94cqww3GHIJaEtMPMXjezTWa25cAU98hcnfT3D1dSWFLCFcf6i7Audif2bceoPm15YOYyNu8uCDscV0mxJKS3Jd0r6WhJww5McY/M1Tl79xfzj49WcnK/9qS1aRx2OK4GkcSvz+rP3sJi7n3DR5atqWKpZXdk8Fl6/AoDTqz6cFxd9sL8NWzLL+RKvzpyldCzbROuODaNx2bncPFR3Ti8S4uwQ3LfUiy97E6IMnkyclWqpMR4fHYuh3dpzoi0VmGH42qo60/sRevG9bl9+iIfWbYGiqWXXXtJj0t6PVjuL2lC/ENzdcnbWZvI2byHCcem+YuwrtKaNqjHLaP78Omq7bz0mY+ZVNPE8gzpKWAGcOANxWXATw/lSyW1CrqSLw8+W5ax32hJWZKyJU2Mpb2kW4P9sySdFqxrKumzUtNmSQ8E2+pLejZo87GkHodybq5yHpudQ8fmDThjUMewQ3E13PnDujC4awvuem0puwuKwg7HfQuxJKQ2ZjYNKAEwsyLgUEfGmgjMMrN0YFaw/DWSkoGHgdOB/sA4Sf3Lax9sHwsMAEYDkyQlm9kuMxtyYAJWAi8Ex5oAbDOzXsCfgHsO8dzct7Rw7Q4+ytnK5cf0oF6yvwjrDk1Skrj9u/3ZtKuAv7zl1cBrklj+9u+R1JqgoKqko4Adh/i9Y4ApwfwU4Jwo+4wAss0sJyjuOjVoV177McBUMysws1wgOzjOVySlE6lWPjvKsf4NnCS/Z1StHn8/l8apyVw03F+EdVVjaLeWfO+ILjzxfi65m/eEHY6LUSwJ6SZgOtBT0hzgaeD6Q/ze9ma2HiD4jDacRWdgdanlNcG68tqX1+aAccCzpcoffdUmuPrbAUQtLy3pakmZkjLz8rxMSVVYv2Mvr3y+jouGd6N5w3phh+NqkV+M7kNqShK/f3Vx2KG4GFXY7dvMPpV0PNAHEJBlZhUO0yjpTaBDlE23xRhbtKuUirrNxNJmLHBpZb7HzB4FHgXIyMjwLjxVYMoHKykx4/JjeoQdiqtl2jVtwE9O6sUfXlvK21mbOKGPD+OW6GJ5D+nAlcOib3NgMzu5rG2SNkrqaGbrJXUENkXZbQ3QtdRyF2BdMF9W+/LaIGkwkGJm86J8z5pgrKfmwNaYTtIdkj0FRfzr45WMHtiBrq0ahR2Oq4UuG5nG1GkDxZ0AABvvSURBVE9Wc+crizmmZxsv1pvgwvq/Mx0YH8yPB16Oss9cIF1SmqRUIlc20ytoPx0YG/ScSwPSgU9KHXMc8Ew5sXwPeMurmVeP5zJXs3NfEVf6iLAuTlJTkvj1d/uTs3kPT32QG3Y4rgJhJaS7gVMkLQdOCZaR1EnSa/DVVdl1RLqcLwGmmdmi8toH26cBi4E3gGvNrHSPwAv5ZkJ6HGgtKZvI87Jv9PhzVa+4xHhizgqGdWvBsG5Re/07VyVO6NOOk/q248+zstm0a1/Y4bhyqKyLgYrq1ZnZp3GJqIbIyMiwzMzMsMOosd5YuJ5r/vEpky4e5u8eubjL3byHU//0LmOGdOa+CwaHHU6dJmmemWVE21beM6T/LWeb17Jzh2Ty7Fy6tmrIaQOi9XtxrmqltWnMhGMP42/vfsklR3VnSFevc5eIykxIZnZCdQbi6o75q7aRuXIbvzmrP8lJ/sqXqx7XndiLFz5dw2+nL+LFH40kyf/sJZyYniFJGijpQkk/ODDFOzBXe01+P5emDVK4cHjXind2roo0qZ/CxNP78vnq7Tz/6Zqww3FRxFJc9bfAQ8F0AvBH4Ow4x+VqqdVb83n9i/V8f0Q3mtSP6a0D56rMOUM6M7RbC+55I4td+yp8ndJVs1iukL4HnARsMLPLgcFA/bhG5Wqtpz5YQZLE+JE9wg7F1UGROncD2LKngIe8zl3CiSUh7TWzEqBIUjMiL6H6iyPuW9u5r5Bn567mzMM70qlFw7DDcXXU4K4tuPCIrjw5J5cv83aHHY4rJZaElCmpBfAYMA/4lK+/bOpcTJ79ZDW7C4q48lj/PePC9fPRfWiQkszvXlnsA/klkFhGjP2xmW03s78ReQl1fHDrzrmYFRWX8OScXI5Ma8WgLs3DDsfVcW2a1OeGk9N5d1keby2NVrnMhSGWTg2zDsyb2QozW1B6nXOxeH3hBtbt2OdlglzCGD+yBz3bNubOVxdTUHSoQ7y5qlBmQpLUQFIroI2klsEora2CEVU7ldXOuYOZGZNn55DWpjEn9fWKyy4x1EtO4rffHcCKLfk88f6KsMNxlH+F9EMiz4z6EnluNC+YXiYykqtzMclcuY3P1+zgimPT/GVEl1CO692Wk/u15y9vLWfjTq9zF7YyE5KZPWhmacDPzCyt1DTYzP5SjTG6Gu6ht7Jp0age5w87eKxE58L367P6UVhs3PP60rBDqfNi6WX3iKSfSPp3MF0nyYf2dDGZk72Z95blce2oXjRK9RdhXeLp3roxVx2Xxgvz1zJv5baww6nTYklIk4Ajgs8D83+NZ1CudigpMe56fQmdWzTk0qO7hx2Oc2X68ahetG9Wn9unL6KkxLuBh6W8Tg0Hfs4ON7PxZvZWMF0ODK+e8FxN9sqCdSxcu5ObT+1Ng3rJYYfjXJka10/hl2f044u1O3hu3uqww6mzyrtCOvDya7GkngdWSjoM8D6SrlwFRcXc958s+nVsxjlD/NmRS3xnD+5ERveW/PGNLHbs9Tp3YSgvIR3oDvUz4G1J70h6B3gLuDnegbma7Z8frWL11r3cMrqP96xzNYIkbj97AFvz9/PnWcvDDqdOKu8pc1tJNwXzjwDJwB6gATAUeDvOsbkaaue+Qh56azkje7bm+N5tww7HuZgN7NycscO7MuWDFYwb0ZVe7ZqGHVKdUt4VUjLQBGhKJHEpWE4J1lVa8ILtTEnLg8+WZew3WlKWpGxJE2NpL+nWYP8sSacF65pK+qzUtFnSA8G2myQtlrRA0ixJ/vT9ED36bg7b8gu59fR+SH515GqWn53ah4apydzhde6qXXlXSOvN7Hdx+t6JwCwzuztINBOBW0rvICmZyAu4pwBrgLmSppvZ4rLaS+oPjAUGEKkm8aak3ma2CxhS6tjzgBeCxflAhpnlS/oRkfGeLorTedd6G3fuY/L7OXx3cCevWedqpNZN6nPTKb2545XFzFy8kVMHdAg7pDojlmdI8TAGmBLMTwHOibLPCCDbzHLMbD8wNWhXXvsxwFQzKzCzXCA7OM5XJKUD7YDZAGb2tpnlB5s/Aroc4rnVaQ+8uYziEuPnp/YJOxTnKu2So7qT3q4Jv/+/Jewr9D5c1aW8hHRSHL+3vZmtBwg+oxU46wyU7n+5JlhXXvvy2hwwDnjWol+LTwBeLytoSVdLypSUmZeXV9ZudVb2pl08O3c1Fx/ZnW6tG4UdjnOVdqDO3aqt+Tz+fm7Y4dQZ5ZUO2nooB5b0pqSFUaYxFbeOHCJaWFXQZizwTJR4LwEygHvLOriZPWpmGWaW0batP6w/2D1vZNEoNYXrT+wVdijOHbJj09tw2oD2/OWtbNbv2Bt2OHVCLJUaKsXMTjazgVGml4GNkjoCBJ/RBiRZA3QttdwFWBfMl9W+vDZIGgykmNm80l8k6WTgNuBsMyuo5CnXaZkrtjJz8UauOf4wWjfxEe5d7fCrM/tTbMbdXueuWsQtIVVgOjA+mB9PpIL4weYC6ZLSJKUSubKZXkH76cBYSfUlpQHpfH1023EcdHUkaSiRbu1nm5mP1FUJZsYfXltCu6b1ueLYtLDDca7KdG3ViB8edxgvf7aOuSsO6aaRi0FYCelu4BRJy4n0orsbQFInSa8BmFkRcB0wA1gCTDOzReW1D7ZPAxYDbwDXmlnpJ5IX8s3bdfcS6c7+XNAlfDruW/nP4o18umo7N57S2wuoulrnR6N60rF5A26fvohir3MXV/J+9pWTkZFhmZmZYYcRuqLiEk574D0AZvz0OFKSw/qN41z8vPL5Oq5/Zj5/OHcQ3z+yW9jh1GiS5plZRrRt/q+HOyTTMtfwZd4efjG6rycjV2uddXhHRqS14t4ZS9mR73Xu4sX/BXGVlr+/iD+9uYwjurfk1P7tww7HubiRxG+/258dewv505vLwg6n1vKE5Crt8dm55O0q4Jdn9PUSQa7WG9CpOeNGdOPvH60ka8OusMOplTwhuUrZsruAR97L4dT+7Tmie6uww3GuWtx8ah+a1E/hd68u8jp3ceAJyVXKQ29ls7ewmF+M7ht2KM5Vm1aNU7nplN7Myd7CjEUbwg6n1vGE5L61lVv28M+PV3JhRld6tWsSdjjOVauLj+xGn/ZNufNVr3NX1TwhuW/t3hlZpCQlcePJ6WGH4ly1S0lO4rdn92ft9r08+l5O2OHUKp6Q3LeyYM12Xl2wniu/k0a7Zg3CDse5UIzs2YYzBnVg0jvZrN3ude6qiickFzMLanq1apzK1ccdFnY4zoXql2f0A+CKJ+eyzpNSlfCE5GL27rI8PvhyC9ef2IumDeqFHY5zoerSshGTfzCctdv3ct6kD1i8bmfYIdV4npBcTIpLIldH3Vo14uIjfZR35yAyRMVz1xwNwIWPfMjs5T5O2qHwhORi8tL8tSzdsIufndaH1BT/Y+PcAf06NuPFa0fSpWVDLn9yLs9lrq64kYvK/2VxFdpXWMz9M5cxqHNzzhrUMexwnEs4HZs3ZNo1R3PkYa34+b8X8OCby/3F2UrwhOQq9PSHK1i7fS+3nt6XpCQvEeRcNM0a1OPJy0Zw3rDO/OnNZdzy/AIKi0vCDqtG8cFrXLl25Bfy8Ntfcnzvtozs1SbscJxLaKkpSfzvBYPp0rIRf561nA07C5h08TCa1Pd/amPhV0iuXJPezWbnvkImnu4lgpyLhSRuOqU395w/iDnZm7nwbx+ycee+sMOqETwhuTKt276XJ+es4NyhnenXsVnY4ThXo1w0vBuPj89g5ZY9nPvwHJZt9ArhFQklIUlqJWmmpOXBZ8sy9hstKUtStqSJsbSXdGuwf5ak04J1TYPhyQ9MmyU9cNB3fU+SSYo6kmFddP/MyLgvN5/aJ+RInKuZRvVpx7M/PJrCEuP8v37AB19uDjukhBbWFdJEYJaZpQOzguWvkZQMPAycDvQHxknqX177YPtYYAAwGpgkKdnMdpnZkAMTsBJ4odR3NQV+Anwcl7OtgZZu2Mnzn65h/NHd6dyiYdjhOFdjDezcnBd/PJIOzRow/olPePmztWGHlLDCSkhjgCnB/BTgnCj7jACyzSzHzPYDU4N25bUfA0w1swIzywWyg+N8RVI60A6YXWr1ncAfAb/RG7jn9aU0rZ/CtSf0CjsU52q8Li0b8e9rRjKsW0tumPoZD7+d7d3CowgrIbU3s/UAwWe7KPt0Bkq/YbYmWFde+/LaHDAOeNaCPw2ShgJdzezVyp9O7fLhl1t4OyuPH5/QixaNUsMOx7laoXmjejw9YQRnD+7EvTOy+NVLCynybuFfE7e+iJLeBDpE2XRbrIeIsq6inxSxtBkLXAogKQn4E3BZTAFJVwNXA3Tr1i2WJjVOpIDqEjo2b8BlI3uEHY5ztUr9lGQeuGgInVs25K/vfMn6Hft4aNxQGnu3cCCOV0hmdrKZDYwyvQxslNQRIPjcFOUQa4CupZa7AOuC+bLal9cGSYOBFDObF6xqCgwE3pG0AjgKmF5WxwYze9TMMswso23btjH+l6hZ/u+L9Xy+Zgc3ndKbBvWSww7HuVonKUncMrovvz9nIO9kbWLsox+xaZc/LYDwbtlNB8YH8+OBl6PsMxdIl5QmKZXIlc30CtpPB8ZKqi8pDUgHPil1zHHAMwcWzGyHmbUxsx5m1gP4CDjbzDIP9QRrov1FJdw7I4u+HZpy3rAuYYfjXK12yVHdeewHGWRv2s15kz4ge9PusEMKXVgJ6W7gFEnLgVOCZSR1kvQagJkVAdcBM4AlwDQzW1Re+2D7NGAx8AZwrZmVHmP4QkolJPd1U+euYuWWfG4Z3ZdkLxHkXNyd1K89U68+in2FxZz/1w+Yu2Jr2CGFSt7To3IyMjIsM7P2XEjtLiji+D++TXr7Jjxz1VFInpCcqy6rt+Yz/slPWLNtL/dfOJizDu8UdkhxI2memUV9LOKVGhwAj76Xw5Y9+7n19H6ejJyrZl1bNeL5a0ZyeOfmXPev+Tz2Xk6d7BbuCcmxadc+Js/O4czDOzK4a4uww3GuTmrZOJV/XHkkZw7qyP+8toQ7XllMcUndSkre19Dx4JvL2V9Uws+9RJBzoWpQL5mHxg2lU4sGPDY7l3Xb9/Lg2KE0TE2sHq9mFpc7KZ6Q6rgv83Yzde5qLj6yGz3aNA47HOfqvKQkcduZ/encoiF3vLqYcY99xOPjM2jdpH61x7K/qISczbvJ2rCLpRt2kRVMvxjdhzFDDq45cOg8IdVx976RRYOUJH5yUnrYoTjnSrnsmDQ6NG/IDVPnc95fP+Cpy0eQFqcfjWbG2u17v5F4cjbvprA4ctswJUn0bNuEI7q3pF3TBnGJwxNSHTZv5TbeWLSBG0/uTZsQfn0558o3emAHnrn6KK6cksn5f/2Ax36QwRHdow6OELPt+fu/SjpLN+xi2cZdLNuwi10FRV/t07lFQ/p0aMqJ/drRt0NT+nRoymFtmpCaEt9uB97tu5JqerdvM+OiRz4iZ/Me3v35KC9d4lwCW7F5D5c9+Qnrd+zjwbFDGT0wWlW2r9tXWEz2psjttqyNB658drJxZ8FX+zRvWI8+HZp+lXT6dmhKevumNGtQL27nUl63b/9XqI6atWQTn6zYyu/PGejJyLkE16NNY57/0UiufDqTH/1zHr89qz+XHZMGQEmJsXpb/tdutS3dsJMVW/K/6qWXmpJEr7ZNOKZnG/p8lXya0b5Z/YR6zcP/JaqDiopLuOeNpRzWpjEXDe9acQPnXOhaN6nPv648ihumzuf2VxbzdlYe2/cWsnzjLvL3/7cgTbdWjejToSlnDOr41VVPj9aNSUlO/Ld8PCHVQc9/uoblm3bzt0uGUa8G/CF1zkU0TE3mr5ccwd2vL+HVBetJC35URm65NSO9XZMafcej5kbuKmXv/mLun7mMod1acNqAiu9DO+cSS3LQLfy2M/tXvHMN4z+P65gn5uSycWeBlwhyziUcT0h1yNY9+/nbO19ycr92jEhrFXY4zjn3NZ6Q6pC/vJXNnv1F3DK6b9ihOOfcN/gzpDqgsLiEh2Yt56kPcrngiK6kt28adkjOOfcNnpBquZy83dz47Gd8vmYH3zuiC7/5bu17EOqcqx08IdVSZsa/PlnF719dQv16SUy6eBhnDOoYdljOOVcmT0i10ObdBdzy7wXMWrqJ76S34b4LBtO+WXyKITrnXFUJpVODpFaSZkpaHnxGrRYoabSkLEnZkibG0l7SrcH+WZJOC9Y1lfRZqWmzpAdKtblQ0mJJiyT9K57nHm+zlmxk9APvMTt7M7/9bn+mXD7Ck5FzrkYIq5fdRGCWmaUDs4Llr5GUDDwMnA70B8ZJ6l9e+2D7WGAAMBqYJCnZzHaZ2ZADE7ASeCFokw7cChxjZgOAn8brpOMpf38Rv3zxCyZMyaRt0wa8ev2xXH5MGklJ/q6Rc65mCCshjQGmBPNTgHOi7DMCyDazHDPbD0wN2pXXfgww1cwKzCwXyA6O85UgAbUDZgerrgIeNrNtAGa26RDPrdp9vno7Z/75fZ75ZBU/PP4wXrp2JL29J51zroYJ6xlSezNbD2Bm6yW1i7JPZ2B1qeU1wJEVtO8MfHRQm4OHNRwHPGv/HXejN4CkOUAycLuZvREtaElXA1cDdOvWrcKTjLei4hImvfMlD85aTvumkcKLR/dsHXZYzjlXKXFLSJLeBKIVS7st1kNEWVfR4E2xtBkLXFpqOQVIB0YBXYDZkgaa2fZvHMjsUeBRiIyHVEEscbVyyx5ufPYzPl21nTFDOvG7MQNp3jB+Y5g451y8xS0hmdnJZW2TtFFSx+DqpiMQ7TbZGqD02AhdgHXBfFnty2uDpMFAipnNO+h7PjKzQiBXUhaRBDU3phOtZmbGc5lruOOVRSQliQfHDonL2PbOOVfdwnqGNB0YH8yPB16Oss9cIF1SmqRUIlc20ytoPx0YK6m+pDQiieWTUsccBzxz0Pe8BJwAIKkNkVt4OZU8r7jaumc/1/xjHr94fgGHd2nBjJ8e58nIOVdrhPUM6W5gmqQJwCrgAgBJnYDJZnaGmRVJug6YQeTZzhNmtqi89ma2SNI0YDFQBFxrZsWlvvdC4IyDYpkBnCppMVAM/NzMtlT9KR+ad5fl8bPnPmdHfiG3ndGPCcd6DzrnXO2i/z7bd99GRkaGZWZmxv179hUWc9drS5jy4Up6t2/CAxcNpX+nZnH/XueciwdJ88wsI9o2r9SQwBau3cFPn/2M7E27mXBsGj8/rQ8N6iWHHZZzzsWFJ6QEVFxiPPpeDvfPzKJV41T+MeFIjk1vE3ZYzjkXV56QEszqrfncPO1zPlmxlTMGdeAP5w6iRaPUsMNyzrm484SUIMyMlz5by29eWoQB/3vBYM4b1tmHGXfO1RmekBLAjvxCbnvpC15dsJ7hPVpy/4VD6NqqUdhhOedctfKEFLI52Zu5edrnbN5dwC9G9+GHx/Uk2btzO+fqIE9IIdlXWMx9M7KY/H4uPds25rEfHMOgLs3DDss550LjCSkESzfs5KdTP2Pphl384Oju3Hp6Pxqmendu51zd5gmpmj2XuZrbXlxIs4b1ePLy4ZzQJ1qhc+ecq3s8IVWztDaNObFvO/7n3IG0blI/7HCccy5heEKqZhk9WpHRo1XYYTjnXMIJq9q3c8459zWekJxzziUET0jOOecSgick55xzCcETknPOuYTgCck551xC8ITknHMuIXhCcs45lxBkZmHHUCNJygNWVrJ5G2BzFYZTE/g51w1+znXDoZxzdzNrG22DJ6QQSMo0s4yw46hOfs51g59z3RCvc/Zbds455xKCJyTnnHMJwRNSOB4NO4AQ+DnXDX7OdUNcztmfITnnnEsIfoXknHMuIXhCcs45lxA8IVUzSaMlZUnKljQx7HjiTVJXSW9LWiJpkaQbwo6pOkhKljRf0qthx1IdJLWQ9G9JS4P/10eHHVO8Sbox+DO9UNIzkhqEHVNVk/SEpE2SFpZa10rSTEnLg8+WVfV9npCqkaRk4GHgdKA/ME5S/3Cjirsi4GYz6wccBVxbB84Z4AZgSdhBVKMHgTfMrC8wmFp+7pI6Az8BMsxsIJAMjA03qrh4Chh90LqJwCwzSwdmBctVwhNS9RoBZJtZjpntB6YCY0KOKa7MbL2ZfRrM7yLyD1XncKOKL0ldgDOByWHHUh0kNQOOAx4HMLP9ZrY93KiqRQrQUFIK0AhYF3I8Vc7M3gO2HrR6DDAlmJ8CnFNV3+cJqXp1BlaXWl5DLf/HuTRJPYChwMfhRhJ3DwC/AErCDqSaHAbkAU8GtyknS2ocdlDxZGZrgfuAVcB6YIeZ/SfcqKpNezNbD5EfnEC7qjqwJ6TqpSjr6kS/e0lNgOeBn5rZzrDjiRdJZwGbzGxe2LFUoxRgGPBXMxsK7KEKb+MkouC5yRggDegENJZ0SbhR1XyekKrXGqBrqeUu1MLL/INJqkckGf3TzF4IO544OwY4W9IKIrdkT5T0j3BDirs1wBozO3Dl+28iCao2OxnINbM8MysEXgBGhhxTddkoqSNA8Lmpqg7sCal6zQXSJaVJSiXyEHR6yDHFlSQRebawxMzuDzueeDOzW82si5n1IPL/9y0zq9W/nM1sA7BaUp9g1UnA4hBDqg6rgKMkNQr+jJ9ELe/IUcp0YHwwPx54uaoOnFJVB3IVM7MiSdcBM4j0ynnCzBaFHFa8HQNcCnwh6bNg3S/N7LUQY3JV73rgn8EPrRzg8pDjiSsz+1jSv4FPifQknU8tLCEk6RlgFNBG0hrgt8DdwDRJE4gk5guq7Pu8dJBzzrlE4LfsnHPOJQRPSM455xKCJyTnnHMJwROSc865hOAJyTnnXELwhORcApBkkv5eajlFUt6BauGSLguW5wdVlmdIivlFTElDJJ0Rj9idqyqekJxLDHuAgZIaBsunAGsP2udZMxsaVFm+G3hBUr8Yjz8E8ITkEponJOcSx+tEqoQDjAOeKWtHM3ubyIuYVx+8TdIFwRg9n0t6L3hZ9XfARZI+k3SRpMbBWDdzg6uuMUHbyyS9LOmNYNyu31b5WTpXBk9IziWOqcDYYKC3w6m4KvqnQN8o638DnGZmg4Gzg6FOfkPkCmuImT0L3EakrNFw4ATg3lIVukcAFxO5qrpAUsahnphzsfCE5FyCMLMFQA8iV0exlFaKVj0eYA7wlKSriJSoiuZUYGJQzukdoAHQLdg208y2mNleIkVDj43pBJw7RF7LzrnEMp3IODujgNYV7DuUKAU9zewaSUcSuf33maQhUdoKON/Msr62MtLu4HpiXl/MVQu/QnIusTwB/M7MvihvJ0nHE3l+9FiUbT3N7GMz+w2wmciQJ7uApqV2mwFcH1SqRtLQUttOkdQq6GBxDpErLufizq+QnEsgZrYGeLCMzRdJOpbIcNm5RK5wog15cK+kdCJXQbOAz4lUZT5wi+4u4E4iI9suCJLSCuCsoP37wN+BXsC/zCyzKs7NuYp4tW/n3FckXQZkmNl1Ycfi6h6/Zeeccy4h+BWSc865hOBXSM455xKCJyTnnHMJwROSc865hOAJyTnnXELwhOSccy4h/D++afm2y6MoPgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(md_steps,tot_energies)\n", "plt.xlabel('MD step')\n", "plt.ylabel('Total energy')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following we plot the ratio between the kinetic energy and the total energy. The lower the MD time step is, the lower is such ratio. It is enough to have a ratio below $\\sim 2 \\%$. In our case we are even below 0.03 $\\%$." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(md_steps,ene_ratio)\n", "plt.xlabel('MD step')\n", "plt.ylabel('100*Ekin/Etot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following we check the temperature during our NVE MD run." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(md_steps,temperatures)\n", "plt.xlabel('MD step')\n", "plt.ylabel('Temperature [K]')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can visualize the trajectory of the water molecules." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "trajectory = []\n", "\n", "for inst in water_md:\n", " trajectory.append(deepcopy(sys))\n", " trajectory[-1].update_positions_from_dict(inst.log[\"Atomic structure\"])" ] }, { "cell_type": "code", "execution_count": 22, "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": [ "viz = InlineVisualizer(400,300)\n", "viz.display_system(*trajectory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also run an MD trjectory with fixed NVT conditions imposed by a Nose-Hoover thermostat with\n", "a target temperature of 330 K. The temperature of 330 K is usually chosen\n", "to avoid the glassy behavior of PBE liquid water on the 20 ps time scale\n", "observed for trajectories at lower temperatures.\n", "In MD runs, 1-2 ps of equilibration period is followed by 5 ps of production period.\n", "\n", "An NVT run can be set with the key 'thermostat': 'nose_hoover_chain' in the 'md' dictionary." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A 38 atoms Lennard-Jones cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following we run an NVT molecular dynamics trajectory for a 38 atoms Lennard-Jones cluster in free boundary conditions, that is none of the three Cartesian directions is periodic in the simulation box.\n", "\n", "As done for the water environment, first we relax the system and then we run a molecular dynamics trajectory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geometry optimization" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "input_file = StringIO(\"\"\"HETATM 1 MG UNL 1 -0.002 -0.042 -0.058 1.00 0.00 Mg \n", "HETATM 2 MG UNL 1 -0.002 -0.003 0.532 1.00 0.00 Mg \n", "HETATM 3 MG UNL 1 0.131 -0.483 0.251 1.00 0.00 Mg \n", "HETATM 4 MG UNL 1 -0.410 -0.285 0.251 1.00 0.00 Mg \n", "HETATM 5 MG UNL 1 -0.389 0.286 0.216 1.00 0.00 Mg \n", "HETATM 6 MG UNL 1 0.162 0.444 0.206 1.00 0.00 Mg \n", "HETATM 7 MG UNL 1 0.484 -0.028 0.235 1.00 0.00 Mg \n", "HETATM 8 MG UNL 1 -0.434 -0.571 -0.233 1.00 0.00 Mg \n", "HETATM 9 MG UNL 1 -0.519 0.006 -0.288 1.00 0.00 Mg \n", "HETATM 10 MG UNL 1 -0.150 0.462 -0.297 1.00 0.00 Mg \n", "HETATM 11 MG UNL 1 0.404 0.271 -0.305 1.00 0.00 Mg \n", "HETATM 12 MG UNL 1 0.641 -0.263 -0.252 1.00 0.00 Mg \n", "HETATM 13 MG UNL 1 -0.017 -0.024 -0.625 1.00 0.00 Mg \n", "HETATM 14 MG UNL 1 0.004 0.015 1.116 1.00 0.00 Mg \n", "HETATM 15 MG UNL 1 0.142 -0.483 0.828 1.00 0.00 Mg \n", "HETATM 16 MG UNL 1 -0.419 -0.279 0.829 1.00 0.00 Mg \n", "HETATM 17 MG UNL 1 -0.398 0.314 0.802 1.00 0.00 Mg \n", "HETATM 18 MG UNL 1 0.174 0.479 0.792 1.00 0.00 Mg \n", "HETATM 19 MG UNL 1 0.508 -0.013 0.812 1.00 0.00 Mg \n", "HETATM 20 MG UNL 1 0.646 -0.509 0.514 1.00 0.00 Mg \n", "HETATM 21 MG UNL 1 0.277 -0.975 0.511 1.00 0.00 Mg \n", "HETATM 22 MG UNL 1 -0.282 -0.776 0.531 1.00 0.00 Mg \n", "HETATM 23 MG UNL 1 -0.836 -0.569 0.519 1.00 0.00 Mg \n", "HETATM 24 MG UNL 1 -0.822 0.025 0.510 1.00 0.00 Mg \n", "HETATM 25 MG UNL 1 -0.794 0.614 0.490 1.00 0.00 Mg \n", "HETATM 26 MG UNL 1 -0.227 0.778 0.478 1.00 0.00 Mg \n", "HETATM 27 MG UNL 1 0.340 0.940 0.469 1.00 0.00 Mg \n", "HETATM 28 MG UNL 1 0.677 0.456 0.483 1.00 0.00 Mg \n", "HETATM 29 MG UNL 1 1.005 -0.040 0.486 1.00 0.00 Mg \n", "HETATM 30 MG UNL 1 0.538 -0.794 0.001 1.00 0.00 Mg \n", "HETATM 31 MG UNL 1 0.133 -0.526 -0.339 1.00 0.00 Mg \n", "HETATM 32 MG UNL 1 -0.056 -0.965 0.011 1.00 0.00 Mg \n", "HETATM 33 MG UNL 1 -0.929 -0.283 0.008 1.00 0.00 Mg \n", "HETATM 34 MG UNL 1 -0.924 0.313 0.000 1.00 0.00 Mg \n", "HETATM 35 MG UNL 1 -0.550 0.786 -0.021 1.00 0.00 Mg \n", "HETATM 36 MG UNL 1 0.025 0.951 -0.031 1.00 0.00 Mg \n", "HETATM 37 MG UNL 1 0.593 0.749 -0.027 1.00 0.00 Mg \n", "HETATM 38 MG UNL 1 0.915 0.247 -0.026 1.00 0.00 Mg \"\"\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "ljsys = read_pdb(input_file)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "LJ_inp = I.Inputfile()\n", "LJ_inp['mode'] = {'method': 'lj'}\n", "LJ_inp.optimize_geometry(method=\"SQNM\", nsteps=500, frac_fluct=0.0, forcemax = 1e-3, betax=1e-3)\n", "LJ_inp[\"geopt\"][\"nhistx\"] = 15\n", "LJ_inp[\"geopt\"][\"steepthresh\"] = 100\n", "LJ_inp[\"geopt\"][\"trustr\"] = 0.1" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating the yaml input file \"./LJ-geopt.yaml\"\n", "Executing command: mpirun -np 2 /Users/wddawson/Documents/CEA/binaries/bds/install/bin/bigdft -n LJ-geopt\n" ] } ], "source": [ "LJ_geopt = study.run(posinp=ljsys.get_posinp(), name=\"LJ-geopt\", \n", " input=LJ_inp) #Run the code with the name scheme geopt" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "tot_energies = []\n", "forces = []\n", "for step in LJ_geopt.log['Initializing LENNARD_JONES_RUN_MODE']:\n", " tot_energies.append(step['LENNARD_JONES_RUN_MODE']['Energy (Hartree)'])\n", " forces.append(step['LENNARD_JONES_RUN_MODE']['Force Norm (Hartree/Bohr)'])\n", "ene_min = min(tot_energies)\n", "tot_energies = [ene - ene_min for ene in tot_energies]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(tot_energies,forces)\n", "plt.loglog()\n", "plt.xlabel('Energy - min(Energy) (Hartree)')\n", "plt.ylabel('Force Norm (Hartree/Bohr)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Molecular dynamics" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "del LJ_inp['geopt']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Lennard Jones code does not print the updated positions to the logfile, because it is typically used for very long trajectories. Instead, we can get the positions from a file which is created after the run." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "import yaml" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "with open(r'final_LJ-geopt.yaml') as file:\n", " # The FullLoader parameter handles the conversion from YAML\n", " # scalar values to Python the dictionary format\n", " posinp = yaml.load(file, Loader=yaml.FullLoader)\n", " \n", "optljsys = deepcopy(ljsys)\n", "optljsys.update_positions_from_dict(posinp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run MD." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "LJ_inp['md'] = {'mdsteps': 10000, 'timestep': 1.0, 'temperature': 330.0, 'print_frequency': 100,\n", " 'thermostat': 'nose_hoover_chain'}\n", "#{'restart_pos': 'Yes','restart_vel': 'Yes','restart_nose': 'Yes'}" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating the yaml input file \"./LJ-md.yaml\"\n", "Executing command: mpirun -np 2 /Users/wddawson/Documents/CEA/binaries/bds/install/bin/bigdft -n LJ-md\n" ] } ], "source": [ "LJ_md = study.run(posinp=optljsys.get_posinp(), name=\"LJ-md\",\n", " input=LJ_inp) #Run the code with the name scheme md" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'istep': 0, 'T': 330.0, 'Eke': 0.059568, 'Epe': -172.877736, 'Ete': -172.816601, 'tcpu': 0.0}\n" ] } ], "source": [ "print(LJ_md.log['Initializing LENNARD_JONES_RUN_MODE'][0]['(MD)'])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "md_steps = []\n", "temperatures = []\n", "kinetic_energies = []\n", "tot_energies = []\n", "ene_ratio = []\n", "for log in LJ_md.log['Initializing LENNARD_JONES_RUN_MODE']:\n", " md_info = log['(MD)']\n", " md_steps.append(md_info['istep'])\n", " temperatures.append(md_info['T'])\n", " ekin = md_info['Eke']\n", " etot = md_info['Ete']\n", " ratio = ekin / etot * 100.00\n", " kinetic_energies.append(ekin)\n", " tot_energies.append(etot)\n", " ene_ratio.append(ratio)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this NVT run, the total energy is not constant during the whole run." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(md_steps,tot_energies)\n", "plt.xlabel('MD step')\n", "plt.ylabel('Energy')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following we plot the ratio between the kinetic energy and the total energy. The lower the MD time step is, the lower is such ratio. It is enough to have a ratio below $\\sim 2 \\%$. In our case we are even below 0.03 $\\%$." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(md_steps,ene_ratio)\n", "plt.xlabel('MD step')\n", "plt.ylabel('100*Ekin/Etot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following we check the temperature during our NVT MD run, which is well aroung 330 K." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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