{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This notebook can be run on Google Colab.\n",
        " \n",
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nanotheorygroup/kaldo/blob/master/docs/docsource/amorphous_presentation.ipynb)\n",
        "\n",
        "\n",
        "\n",
        "In Colab, you can enable the GPU acceleration from `Edit` > `Notebook Settings` > `Accelerator` > `GPU`."
      ],
      "metadata": {
        "id": "syFWKP6m_eoB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Amorphous silicon tutorial\n",
        "\n",
        "## Complie [LAMMPS](https://github.com/lammps/lammps) as shared-library of python ($\\sim$ 8 min)"
      ],
      "metadata": {
        "id": "JcTIeInI_kff"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R9inKEBR-w1Q"
      },
      "outputs": [],
      "source": [
        "# Sudo apt-get softwares\n",
        "! apt-get update\n",
        "! apt install -y cmake build-essential git ccache openmpi-bin libopenmpi-dev python3.10-venv\n",
        "! echo \"Sudo apt-get finishes!\"\n",
        "\n",
        "# Upgrade python packages\n",
        "! pip install --upgrade pip\n",
        "! pip install numpy torch scipy virtualenv psutil pandas tabulate mpi4py Cython\n",
        "! echo \"Python packages installation finishes!\"\n",
        "\n",
        "# Build lammps with cmake\n",
        "%cd /content\n",
        "!rm -rf lammps\n",
        "! git clone https://github.com/lammps/lammps.git lammps\n",
        "%cd /content/lammps\n",
        "! rm -rf build\n",
        "! mkdir build\n",
        "%cd build\n",
        "! cmake ../cmake -DLAMMPS_EXCEPTIONS=yes \\\n",
        "               -DBUILD_SHARED_LIBS=yes \\\n",
        "               -DMLIAP_ENABLE_PYTHON=yes \\\n",
        "               -DPKG_PYTHON=yes \\\n",
        "               -DPKG_MANYBODY=yes \\\n",
        "               -DPKG_KSPACE=yes \\\n",
        "               -DPKG_PHONON=yes \\\n",
        "               -DPYTHON_EXECUTABLE:FILEPATH=`which python`\n",
        "\n",
        "# Complie lammps as share-libary of python\n",
        "! make -j 2\n",
        "! make install-python\n",
        "! echo \"LAMMPS compilation done!\"\n",
        "\n",
        "# Redirect back to main folder\n",
        "%cd /content/"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Remote fetch  and install source code from Github"
      ],
      "metadata": {
        "id": "PEkjhpcF_z4G"
      }
    },
    {
      "cell_type": "code",
      "source": [
	"! git lfs install --skip-repo --skip-smudge\n",
        "! pip install git+https://github.com/nanotheorygroup/kaldo"
      ],
      "metadata": {
        "id": "Py4sAQQe_0FG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fetch supplyment data remotely"
      ],
      "metadata": {
        "id": "qcMNDVbc_1v2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remote fetch kaldo resources from drop box\n",
        "! wget https://www.dropbox.com/s/bvw0qcxy397g25q/kaldo_resources.zip?dl=0\n",
        "! mv kaldo_resources.zip?dl=0 kaldo_resources.zip\n",
        "! unzip kaldo_resources.zip\n",
        "\n",
        "# Unzip files\n",
        "! unzip forcefields.zip\n",
        "! unzip structure_a_si_512.zip\n",
        "\n",
        "# Clean workspace\n",
        "! rm -r forcefields.zip\n",
        "! rm -r structure_a_si_512.zip\n",
        "! rm -r kaldo_resources.zip"
      ],
      "metadata": {
        "id": "IB5XSmCO_3zd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Thermal transport simulation for Amorphous-Silicon (a-Si)"
      ],
      "metadata": {
        "id": "elAQwoOqABm3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from kaldo.forceconstants import ForceConstants\n",
        "\n",
        "# Read amporphous silicon with eskm format\n",
        "forceconstants = ForceConstants.from_folder(folder='structure_a_si_512', format='eskm')"
      ],
      "metadata": {
        "id": "aako_THWABwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create phonons object\n"
      ],
      "metadata": {
        "id": "xr4BVk4MADDe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from kaldo.phonons import Phonons\n",
        "\n",
        "# Create a phonon object\n",
        "# is_classic flag can be true or false, as\n",
        "# well as 0 (quantum) or 1 (classic)\n",
        "phonons = Phonons (forceconstants=forceconstants,\n",
        "                   is_classic=0,\n",
        "                   temperature=300,\n",
        "                   folder='si-amorphous',\n",
        "                   third_bandwidth=0.05/4.135,\n",
        "                   broadening_shape='triangle',\n",
        "                   storage='numpy')"
      ],
      "metadata": {
        "id": "I5rDTAerAEN3"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Access and visualize properties calculated during simulations"
      ],
      "metadata": {
        "id": "A5Xe1oj3AFs3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-poster')\n",
        "\n",
        "# Direct access to properties\n",
        "# calculated during the simulation\n",
        "frequency = phonons.frequency.flatten(order='C')\n",
        "bandwidth = phonons.bandwidth.flatten(order='C')\n",
        "\n",
        "# Plot phonon bandwidth vs frequency\n",
        "print('\\n')\n",
        "plt.plot(frequency[3:],bandwidth[3:],'.',markersize=10,label= 'broadening shape: ' + str(phonons.broadening_shape))\n",
        "plt.ylabel('$\\Gamma$ (THz)', fontsize=25, fontweight='bold')\n",
        "plt.xlabel(\"$\\\\nu$ (THz)\", fontsize=25, fontweight='bold')\n",
        "plt.ylim([bandwidth.min(), bandwidth.max()])\n",
        "plt.legend(loc=2,frameon = False)\n",
        "plt.show()\n",
        "\n",
        "phase_space = phonons.phase_space.flatten(order='C')\n",
        "\n",
        "# Plot phase space vs frequency\n",
        "print('\\n')\n",
        "plt.figure()\n",
        "plt.plot(frequency[3:], phase_space[3:], '.', markersize=10)\n",
        "plt.ylabel (\"Phase space\", fontsize=25, fontweight='bold')\n",
        "plt.xlabel(\"$\\\\nu$ (THz)\", fontsize=25, fontweight='bold')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "olUsoHlRAG41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate and visualize $\\kappa_{per \\ mode}$ and $\\kappa_{cum}$"
      ],
      "metadata": {
        "id": "jtiSfn6AAJqH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from kaldo.conductivity import Conductivity\n",
        "from kaldo.controllers import plotter\n",
        "import numpy as np\n",
        "\n",
        "# Compute conductivity with per phonon mode basis using qhgk method\n",
        "kappa_qhgk_per_mode = np.einsum('maa->m',1/3*Conductivity(phonons=phonons, method='qhgk',n_iterations=20).conductivity)\n",
        "\n",
        "# Compute cumulative conductivity by frequency with qhgk method\n",
        "kappa_qhgk_cum_freq = plotter.cumulative_cond_cal(phonons.frequency,Conductivity(phonons=phonons, method='qhgk').conductivity,phonons.n_phonons)\n",
        "\n",
        "print('\\n')\n",
        "\n",
        "# Visualize the per mode conductivity vs frequency\n",
        "plt.figure()\n",
        "plt.plot(frequency[3:],kappa_qhgk_per_mode[3:],'.',label='QHGK',ms=8)\n",
        "plt.xlabel (\"$\\\\nu$ (THz)\", fontsize=25, fontweight='bold')\n",
        "plt.ylabel(r'$\\kappa(W/m/K)$', fontsize=25, fontweight='bold')\n",
        "plt.legend(loc=1,frameon=False)\n",
        "plt.show()\n",
        "\n",
        "print('\\n')\n",
        "\n",
        "# Visualize the cumulative conductivity vs frequency\n",
        "plt.figure()\n",
        "plt.plot(frequency[3:],kappa_qhgk_cum_freq[3:],'.',label='QHGK',ms=8)\n",
        "plt.xlabel (\"$\\\\nu$ (THz)\", fontsize=25, fontweight='bold')\n",
        "plt.ylabel(r'$\\kappa_{cum}(W/m/K)$', fontsize=25, fontweight='bold')\n",
        "plt.legend(loc=4,frameon=False)\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# Please check out amorphous_silicon_Tersoff_LAMMPS in the example folder for more detail."
      ],
      "metadata": {
        "id": "uVcoDXswAK-2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}