{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [], "pycharm": { "name": "#%% md\n" } }, "source": [ "# Ultrafast electron cooling in an expanding ultracold plasma\n", "\n", "The YAML input file can be found at [input_file](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/examples/Ultrafast_EC/input_files/uec.yaml) and this notebook at [notebook](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/examples/Ultrafast_EC/Ultrafast_EC_Validation.ipynb).\n", "\n", "\n", "## Ultracold Microplasma\n", "\n", "Let us observe the [**Ultrafast electron cooling in an expanding ultracold plasma**](https://doi.org/10.1038/s41467-020-20815-8). Here, a cloud of neutral atoms is trapped and cooled to quantum degeneracy at ~nK temperatures. The Bose-Einstein condensate (BEC) with a peak number density of $n \\sim 2 \\times 10^{20}\\, \\textrm{m}^{-3}$ is then rapidly ionized by a femtosecond laser pulse of $511\\, \\textrm{nm}$ wavelength and $215\\, \\textrm{fs}$ duration so that $N \\sim 4000$ ions and electrons immediately emerge after two-photon ionization. The excess energy of $0.68\\, \\textrm{eV}$ is distributed between ions and electrons leading to an initial ion temperature of $T_{\\rm i} \\sim 33\\, \\textrm{mK}$ and fast escaping electrons of $T_e \\sim 5250\\, \\textrm{K}$.\n", "\n", "The ions are initially strongly coupled as the temperture is very small compared to the Coulomb energy of the dense, ionized BEC:\n", "\n", "$$ \\Gamma_{\\rm i} = \\frac{(e)^2}{4\\pi \\varepsilon_0 a_{\\rm ws}} \\frac{1}{k_B T_{\\rm i}} = 4800 $$\n", "\n", "where $T_{\\rm i}$ is the ion temperature, and $a_{\\rm ws} = [3 / (4\\pi n)]^{1/3}$ is the Wigner-Seitz radius defined by the number density $n$.\n", "\n", "The high density also leads to ultrafast dynamics as the inverse electron plasma frequency is given by:\n", "\n", "$$ \\nu^{-1}_{\\rm p,e} = 2 \\pi \\sqrt{\\frac{m_{\\rm e} \\varepsilon_0}{n e^2}} \\sim 8\\, \\textrm{ps} $$" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Initialization\n", "\n", "We start by importing the usual libraries and creating a path to the input file of our simulation." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: \n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab\n", "%matplotlib inline\n", "import os\n", "import pandas as pd\n", "\n", "from sarkas.processes import PreProcess, Simulation, PostProcess\n", "\n", "plt.style.use('MSUstyle')\n", "input_file = os.path.join('input_files', 'uec.yaml')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Simulation\n", "\n", "Let us start the simulation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[38;2;0;129;131m\n", "\n", "\n", "\n", "\n", "\n", " __ _ \n", "/ _\\ __ _ _ __| | ____ _ ___ \n", "\\ \\ / _` | '__| |/ / _` / __|\n", "_\\ \\ (_| | | | < (_| \\__ \\\n", "\\__/\\__,_|_| |_|\\_\\__,_|___/\n", " \n", "\n", "\u001B[0m\n", "An open-source pure-python molecular dynamics suite for non-ideal plasmas.\n", "\n", "\n", "\n", "\n", "\n", "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n", " Simulation \n", "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n", "\n", "Job ID: UEC_4000\n", "Job directory: Simulations/UEC_4000\n", "\n", "Equilibration dumps directory: \n", " {'Simulations/UEC_4000/Simulation/Equilibration/dumps'}\n", "Production dumps directory: \n", " {'Simulations/UEC_4000/Simulation/Production/dumps'}\n", "\n", "Equilibration Thermodynamics file: \n", "Simulations/UEC_4000/Simulation/Equilibration/EquilibrationEnergy_UEC_4000.csv\n", "Production Thermodynamics file: \n", "Simulations/UEC_4000/Simulation/Production/ProductionEnergy_UEC_4000.csv\n", "\n", "PARTICLES:\n", "Total No. of particles = 8000\n", "No. of species = 2\n", "Species ID: 0\n", "\tName: e\n", "\tNo. of particles = 4000 \n", "\tNumber density = 2.000000e+20 [N/m^3]\n", "\tAtomic weight = 5.446170e-04 [a.u.]\n", "\tMass = 9.109384e-31 [kg]\n", "\tMass density = 1.821877e-10 [kg/m^3]\n", "\tCharge number/ionization degree = -1.0000 \n", "\tCharge = -1.602177e-19 [C]\n", "\tTemperature = 5.250000e+03 [K] = 4.524100e-01 [eV]\n", "\tDebye Length = 3.535658e-07 [m]\n", "\tPlasma Frequency = 7.978230e+11 [rad/s]\n", "Species ID: 1\n", "\tName: Rb\n", "\tNo. of particles = 4000 \n", "\tNumber density = 2.000000e+20 [N/m^3]\n", "\tAtomic weight = 8.700000e+01 [a.u.]\n", "\tMass = 1.455181e-25 [kg]\n", "\tMass density = 2.910362e-05 [kg/m^3]\n", "\tCharge number/ionization degree = 1.0000 \n", "\tCharge = 1.602177e-19 [C]\n", "\tTemperature = 3.300000e-02 [K] = 2.843720e-06 [eV]\n", "\tDebye Length = 8.864364e-10 [m]\n", "\tPlasma Frequency = 1.996147e+09 [rad/s]\n", "\n", "SIMULATION AND INITIAL PARTICLE BOX:\n", "Units: mks\n", "No. of non-zero box dimensions = 3\n", "Wigner-Seitz radius = 8.419452e-08 [m]\n", "Box side along x axis = 2.375452e+03 a_ws = 2.000000e-04 [m]\n", "Box side along y axis = 2.375452e+03 a_ws = 2.000000e-04 [m]\n", "Box side along z axis = 2.375452e+03 a_ws = 2.000000e-04 [m]\n", "Box Volume = 8.000000e-12 [m^3]\n", "Initial particle box side along x axis = 2.579187e+01 a_ws = 2.171534e-06 [m]\n", "Initial particle box side along y axis = 2.579187e+01 a_ws = 2.171534e-06 [m]\n", "Initial particle box side along z axis = 5.037475e+01 a_ws = 4.241278e-06 [m]\n", "Initial particle box Volume = 2.000000e-17 [m^3]\n", "Boundary conditions: absorbing\n", "\n", "Equilibration: \n", "No. of equilibration steps = 0\n", "Total equilibration time = 0.0000e+00 [s] ~ 0 w_p T_eq\n", "snapshot interval step = 10\n", "snapshot interval time = 5.0000e-13 [s] = 0.3989 w_p T_snap\n", "Total number of snapshots = 0\n", "\n", "Production: \n", "No. of production steps = 600\n", "Total production time = 3.0000e-11 [s] ~ 23 w_p T_eq\n", "snapshot interval step = 4\n", "snapshot interval time = 2.0000e-13 [s] = 0.1596 w_p T_snap\n", "Total number of snapshots = 150\n", "\n", "POTENTIAL: coulomb\n", "Short-range Cutoff radius: a_rs = 2.000000e-08 [m]\n", "Effective coupling constant: Gamma_eff = 2386.77\n", "\n", "ALGORITHM: pp\n", "rcut = 1187.7258 a_ws = 1.000000e-04 [m]\n", "No. of PP cells per dimension = [2 2 2]\n", "No. of particles in PP loop = 10800000000\n", "No. of PP neighbors per particle = 1675516081\n", "Tot Force Error = 0.000000e+00\n", "\n", "INTEGRATOR: \n", "Equilibration Integrator Type: verlet\n", "Production Integrator Type: verlet\n", "Time step = 5.000000e-14 [s]\n", "Total plasma frequency = 7.978255e+11 [rad/s]\n", "w_p dt = 0.0399 ~ 1/25\n", "\n", "\n", "-------------------------Initialization Times------------------------- \n", "\n", "\n", "Potential Initialization Time: 0 sec 14 msec 697 usec 371 nsec\n", "\n", "Particles Initialization Time: 0 sec 1 msec 747 usec 688 nsec\n", "\n", "Total Simulation Initialization Time: 0 sec 14 msec 697 usec 371 nsec\n", "\n", "\n", "------------------------------Production------------------------------ \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/luciano/Documents/Programming/sarkas/sarkas/sarkas/potentials/core.py:523: AlgorithmWarning: Use the PP method with care for pure Coulomb interactions.\n", " warn(\"Use the PP method with care for pure Coulomb interactions.\", category=AlgorithmWarning)\n", "/home/luciano/Documents/Programming/sarkas/sarkas/sarkas/potentials/core.py:416: AlgorithmWarning: \n", "Short-range cut-off enabled. Use this feature with care!\n", " warn(\"\\nShort-range cut-off enabled. Use this feature with care!\", category=AlgorithmWarning)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "59498d23a1404ef89d74db65c8582ddd", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/600 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize the Postprocessing class\n", "postproc = PostProcess(input_file)\n", "# Read the simulation's parameters and assign attributes\n", "postproc.setup(read_yaml=True)\n", "# Calculate observables\n", "postproc.run()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Let us have a closer look at the energy evolution over time which we can extract from our Sarkas simulation into a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# Rename the Thermodynamics class for convenience\n", "E = postproc.therm\n", "# Setup its attributes\n", "E.setup(postproc.parameters)\n", "# Grab the data\n", "E.parse()\n", "\n", "# Results dataframe\n", "Temp_vel_data = pd.DataFrame()\n", "# Time\n", "Temp_vel_data['Time'] = E.dataframe[\"Time\"] * 1e12 # scale by picosec\n", "# Total energies\n", "eVpp = 1 / (np.abs(E.qe) * postproc.species[0].num) # scale by eV per particle\n", "Temp_vel_data['Total Energy'] = E.dataframe[\"Total Energy\"] * eVpp\n", "Temp_vel_data['Total Kinetic Energy'] = E.dataframe[\"Total Kinetic Energy\"] * eVpp\n", "Temp_vel_data['Potential Energy'] = E.dataframe[\"Potential Energy\"] * eVpp\n", "# Kinetic energies\n", "Temp_vel_data['e Kinetic Energy'] = E.dataframe[' '.join([postproc.species[0].name, 'Kinetic Energy'])] * eVpp\n", "Temp_vel_data['Ion Kinetic Energy'] = E.dataframe[' '.join([postproc.species[1].name, 'Kinetic Energy'])] * eVpp\n", "# Potential energies\n", "Temp_vel_data['ee Potential Energy'] = E.dataframe[' '.join([postproc.species[0].name, 'Potential Energy'])] * eVpp\n", "Temp_vel_data['IonIon Potential Energy'] = E.dataframe[' '.join([postproc.species[1].name, 'Potential Energy'])] * eVpp\n", "Temp_vel_data['eIon Potential Energy'] = Temp_vel_data['Potential Energy'] \\\n", " - Temp_vel_data['ee Potential Energy'] - Temp_vel_data['IonIon Potential Energy']" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It is now time to plot and compare. For this tutorial we grabbed data from Fig. 4f of this [paper](https://doi.org/10.1038/s41467-020-20815-8). The data was obtained using [WebPlotDigitizer](https://automeris.io/WebPlotDigitizer/) and saved into the files ``Ee.csv``, ``Ei.csv`` and ``Ekinp.csv`` in the folder ``uec_data``, reflecting the ultrafast energy evolution for the electrons, ions and plasma electrons, respectively.\n", "\n", "We will compare the results with our simulation. The ion energy $E_{\\rm i}$ is composed of $E_{\\rm i} = E^{\\rm kin}_{\\rm i} + E^{\\rm pot}_{\\rm ii} + 0.5 E^{\\rm pot}_{\\rm ei}$ while the electron energy $E_{\\rm e}$ is composed of $E_{\\rm e} = E^{\\rm kin}_{\\rm e} + E^{\\rm pot}_{\\rm ee} + 0.5 E^{\\rm pot}_{\\rm ei}$ so that the inter-species potential energy $E^{\\rm pot}_{\\rm ei}$ is equally shared by the electrons and ions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Read paper data\n", "Ee = pd.read_csv(os.path.join('uec_data/', 'Ee.csv'), names=['Time', 'Energy'])\n", "Ei = pd.read_csv(os.path.join('uec_data/', 'Ei.csv'), names=['Time', 'Energy'])\n", "Ekinp = pd.read_csv(os.path.join('uec_data/', 'Ekinp.csv'), names=['Time', 'Energy'])\n", "\n", "# Plot\n", "fig_energy, (ax_etot) = plt.subplots(1, 1, figsize=(10, 7))\n", "\n", "# Simulation data\n", "plt.plot(Temp_vel_data['Time'], Temp_vel_data['Total Energy'], linestyle='--', color='black', label='Total Energy')\n", "ion_energy = Temp_vel_data['Ion Kinetic Energy'] + Temp_vel_data['IonIon Potential Energy'] \\\n", " + 0.5*Temp_vel_data['eIon Potential Energy']\n", "electron_energy = Temp_vel_data['e Kinetic Energy'] + Temp_vel_data['ee Potential Energy'] \\\n", " + 0.5*Temp_vel_data['eIon Potential Energy']\n", "plt.plot(Temp_vel_data['Time'], electron_energy, linestyle='-', color='skyblue', label='Electron Energy')\n", "plt.plot(Temp_vel_data['Time'], ion_energy, linestyle='-', color='firebrick', label='Ion Energy')\n", "\n", "# Paper data\n", "plt.scatter(Ekinp['Time'], Ekinp['Energy'], marker='x', color='cadetblue', alpha=0.5, label=r'$E_{\\rm kin,plasma}$')\n", "plt.scatter(Ee['Time'], Ee['Energy'], marker='.', color='skyblue', alpha=0.5, label=r'$E_{\\rm e}$')\n", "plt.scatter(Ei['Time'], Ei['Energy'], marker='o', color='firebrick', alpha=0.5, label=r'$E_{\\rm i}$')\n", "\n", "plt.xlim((0, 30))\n", "plt.ylim((0, 0.8))\n", "ax_etot.legend()\n", "ax_etot.set(xlabel =r'Time (ps)', ylabel = 'Energy (eV)')\n", "fig_energy.tight_layout()\n", "fig_energy.savefig(os.path.join(postproc.io.postprocessing_dir,'Energy_Plot.png'))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Nice! Sarkas accurately reproduces the ultrafast electron cooling. Minor descrepancies in the amplitude and electron plasma frequency are attributed to the initial particle distribution which is an elongated box in sarkas and a cylinder in the original publication." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## MD Movie\n", "\n", "Let's make a movie of our simulation. Running the following cell allows you to create XYZ files that can be read by [Ovito](https://www.ovito.org/) to make nice MD movies." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9ef5f9ac29904a3d9d07073140d4ddd0", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/151 [00:00\n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo(\"AxP1R5WQOCk\",width=800,height=450)" ] } ], "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": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 4 }