{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "Simple Silicon atomic structure and analysis example.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UwGjo8yx1i-"
      },
      "source": [
        "# Description of Atoms module and analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85IhBrwnx1jC"
      },
      "source": [
        "# Table of contents\n",
        "1. Creating Silicon Atoms class\n",
        "2. Basic Atoms properties\n",
        "3. Atoms class to_dict and from_dict methods\n",
        "4. Making supercells\n",
        "5. Spacegroup information and conventional structure\n",
        "6. Making point-defect vacancy\n",
        "7. Making surface structure\n",
        "8. Making interfaces\n",
        "9. Neighborhood analysis\n",
        "10. X-ray diffractio (XRD) patterns\n",
        "11. Obtaining machine-learning descriptors\n",
        "12. Get an atomistic graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Y2u3olRx3p8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "766b26ef-4cd7-406a-af98-f658e35d2754"
      },
      "source": [
        "!pip install jarvis-tools"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting jarvis-tools\n",
            "  Downloading jarvis_tools-2024.4.30-py2.py3-none-any.whl (4.2 MB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.20.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.11.4)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (3.7.1)\n",
            "Collecting spglib>=1.14.1 (from jarvis-tools)\n",
            "  Downloading spglib-2.4.0-cp310-cp310-manylinux_2_17_x86_64.whl (809 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m809.2/809.2 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (1.4.2)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (2.31.0)\n",
            "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (0.12.1)\n",
            "Collecting xmltodict>=0.11.0 (from jarvis-tools)\n",
            "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
            "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.10/dist-packages (from jarvis-tools) (4.66.4)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->jarvis-tools) (2.8.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->jarvis-tools) (2024.6.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->jarvis-tools) (1.16.0)\n",
            "Installing collected packages: xmltodict, spglib, jarvis-tools\n",
            "Successfully installed jarvis-tools-2024.4.30 spglib-2.4.0 xmltodict-0.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlV04uh3x1jD"
      },
      "source": [
        "### A crystal structure is defined by lattice-matrix, atomic coordinates and element types. As an example, let's create Siliocn crystal structure. We need to to import Atoms class from jarvis-tools to do that."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7H09Z0Jx1jF"
      },
      "source": [
        "# If you don't have jarvis-tools package installed, try\n",
        "# !pip install jarvis-tools\n",
        "# and restart runtime for your notebook\n",
        "from jarvis.core.atoms import Atoms"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4fH6qU0x1jO"
      },
      "source": [
        "#Silicon crystal structure example. These crystal-structure data are generally obtained from XRD-type experiments.\n",
        "box = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]\n",
        "coords = [[0, 0, 0], [0.25, 0.25, 0.25]]\n",
        "elements = [\"Si\", \"Si\"]\n",
        "Si = Atoms(lattice_mat=box, coords=coords, elements=elements)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# RDF\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "atoms=Si\n",
        "\n",
        "dists=[]\n",
        "for i in atoms.get_all_neighbors(r=10):\n",
        "    for j in i:\n",
        "      dists.append(j[2])\n",
        "dists=np.array(dists)\n",
        "hist, bins = np.histogram(dists, bins=np.arange(0.1, 10.2, 0.1))\n",
        "number_density = atoms.num_atoms / atoms.volume\n",
        "shell_vol = (\n",
        "            4.0\n",
        "            / 3.0\n",
        "            * np.pi\n",
        "            * (np.power(bins[1:], 3) - np.power(bins[:-1], 3))\n",
        "        )\n",
        "\n",
        "rdf = (\n",
        "            hist / shell_vol / number_density / atoms.num_atoms\n",
        "        )\n",
        "plt.plot(bins[:-1], rdf)\n",
        "plt.xlim([0,10])\n",
        "plt.xlabel('Distance (Angstrom)')\n",
        "plt.ylabel('RDF')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "JGYCISAgv0W8",
        "outputId": "b84c10bc-d34c-4d04-a36f-7066e72a0f32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OMiZ2OD4yIXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "diabyQSqxckT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87EzahSOx1jV"
      },
      "source": [
        "### Once the Atoms class is created, several properties can be calculated. Examples are given below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAuG5dqTx1jW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d570b66-fc6a-47d5-ed23-4175697d0ee6"
      },
      "source": [
        "print ('volume',Si.volume)\n",
        "print ('density in g/cm3', Si.density)\n",
        "print ('composition as dictionary', Si.composition)\n",
        "print ('Chemical formula', Si.composition.reduced_formula)\n",
        "print ('Spacegroup info', Si.spacegroup())\n",
        "print ('lattice-parameters', Si.lattice.abc, Si.lattice.angles)\n",
        "print ('packing fraction',Si.packing_fraction)\n",
        "print ('number of atoms',Si.num_atoms)\n",
        "print ('Center of mass', Si.get_center_of_mass())\n",
        "print ('Atomic number list', Si.atomic_numbers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "volume 40.02575174999999\n",
            "density in g/cm3 2.3303545408113413\n",
            "composition as dictionary OrderedDict([('Si', 2)])\n",
            "Chemical formula Si\n",
            "Spacegroup info Fd-3m (227)\n",
            "lattice-parameters [3.83959, 3.83959, 3.83959] [60.0, 60.0, 60.0]\n",
            "packing fraction 0.27858\n",
            "number of atoms 2\n",
            "Center of mass [0.67875 0.67875 0.67875]\n",
            "Atomic number list [14, 14]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBDZYn0Ex1jf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9d87be7-6dbf-48e5-f484-426f39dcdf76"
      },
      "source": [
        "# Print Atoms class in POSCAR format\n",
        "print (Si)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Si2\n",
            "1.0\n",
            "2.715 2.715 0.0\n",
            "0.0 2.715 2.715\n",
            "2.715 0.0 2.715\n",
            "Si\n",
            "2\n",
            "Cartesian\n",
            "0.0 0.0 0.0\n",
            "1.3575 1.3575 1.3575\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bU7u12bx1jp"
      },
      "source": [
        "### For databases purposes, it maybe important to store the Atoms class as a dictionary and then reloading the dictionary as Atoms object. An example is given below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwABuZjex1jr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a31bc1da-8234-4b7f-cad2-edb0184b5302"
      },
      "source": [
        "Si.to_dict()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('lattice_mat',\n",
              "              [[2.715, 2.715, 0.0], [0.0, 2.715, 2.715], [2.715, 0.0, 2.715]]),\n",
              "             ('coords', [[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]]),\n",
              "             ('elements', ['Si', 'Si']),\n",
              "             ('abc', [3.83959, 3.83959, 3.83959]),\n",
              "             ('angles', [60.0, 60.0, 60.0]),\n",
              "             ('cartesian', False),\n",
              "             ('props', ['', ''])])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOezw_oTx1jy"
      },
      "source": [
        "d = Si.to_dict()\n",
        "new_atoms = Atoms.from_dict(d)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0UyPplxx1j6"
      },
      "source": [
        "Making a supercells: there are two methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV5s1vP5x1j7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10ca8358-4028-4ebb-bb7e-6edf3d5bff74"
      },
      "source": [
        "supercell_1 = Si.make_supercell([2,2,2])\n",
        "supercell_2 = Si.make_supercell_matrix([[2,0,0],[0,2,0],[0,0,2]])\n",
        "supercell_1.density == supercell_2.density"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEqsP20Ux1kC"
      },
      "source": [
        "### Many tensor-like material properties such as elastic constants require conventional cell representations, which can be obtained as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_Ccr-1Rx1kD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f528f177-5954-4ba9-cbcb-48e89b7e0152"
      },
      "source": [
        "from jarvis.analysis.structure.spacegroup import Spacegroup3D\n",
        "\n",
        "spg = Spacegroup3D(Si)\n",
        "Si_cvn = spg.conventional_standard_structure\n",
        "print ('Number of atoms in the conventional cell', Si_cvn.num_atoms)\n",
        "print ('Wycoff multoplicity in the primitive-cell', spg._dataset[\"wyckoffs\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of atoms in the conventional cell 8\n",
            "Wycoff multoplicity in the primitive-cell ['b', 'b']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWIdi33jx1kJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "995fe0ea-1b03-47a3-b938-77c45587338e"
      },
      "source": [
        "# Example to create Atoms object from POSCAR format file\n",
        "from jarvis.io.vasp.inputs import Poscar\n",
        "import urllib\n",
        "url = 'https://raw.githubusercontent.com/usnistgov/jarvis/v2020.6.9/jarvis/examples/vasp/SiOptb88/POSCAR'\n",
        "webf = urllib.request.urlopen(url)\n",
        "txt = webf.read()\n",
        "f = open('POSCAR', 'wb')\n",
        "f.write(txt)\n",
        "f.close()\n",
        "\n",
        "pos = Poscar.from_file('POSCAR').atoms\n",
        "print (pos)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Si2\n",
            "1.0\n",
            "3.348898 0.0 1.933487\n",
            "1.116299 3.157372 1.933487\n",
            "0.0 0.0 3.866975\n",
            "Si\n",
            "2\n",
            "Cartesian\n",
            "3.90705 2.7626987499999998 6.767215\n",
            "0.55815 0.39467125 0.966745\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-mSV4GBx1kS"
      },
      "source": [
        "### Realistic materials contain several types of point, line, surface and volume defects. Examples to create point-defects and surfaces are shown below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzmvpAR7x1kT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfe2f492-7574-4ef3-ba98-438d4ac84928"
      },
      "source": [
        "from jarvis.analysis.defects.vacancy import Vacancy\n",
        "\n",
        "vacs = Vacancy(atoms=Si).generate_defects(enforce_c_size=10.0) #enforces cell-size to be close to 10 Angstroms\n",
        "print (len(vacs), Atoms.from_dict(vacs[0].to_dict()[\"defect_structure\"]).num_atoms)\n",
        "# We find that there are only one unique point vacanc available based on Wycoff-position information"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwiymVzex1kc"
      },
      "source": [
        "from jarvis.analysis.defects.surface import wulff_normals, Surface\n",
        "\n",
        "# Let's create (1,1,1) surface with three layers, and vacuum=18.0 Angstrom\n",
        "# We center it around origin so that it looks good during visualization\n",
        "surface_111 = Surface(atoms=Si, indices=[1, 1, 1], layers = 3, vacuum = 18 ).make_surface().center_around_origin()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_jIjs5wx1ki",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dbad125-211d-497e-fb48-c3bac0a8527f"
      },
      "source": [
        "print (surface_111)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Si40\n",
            "1.0\n",
            "7.679179643685906 0.0 0.0\n",
            "3.8395898218429525 6.650364651656329 0.0\n",
            "0.0 0.0 33.67505980849834\n",
            "Si\n",
            "40\n",
            "Cartesian\n",
            "2.8796928117748335 -1.6625888370859176 10.959382571813123\n",
            "2.8796926336177857 0.554194728476531 17.229406495212462\n",
            "-0.9598975445392609 -1.6625934887422462 20.36441845691213\n",
            "-0.9598977226963086 2.7709782940389776 23.499430418611794\n",
            "-2.879692099146646 -2.770978294038978 10.175629581388206\n",
            "4.799487722696308 1.6625934887422456 13.310641543087874\n",
            "0.9598975445392608 -0.5541947284765302 16.445653504787543\n",
            "-2.879692633617782 -2.770982945695306 19.58066546648721\n",
            "-0.9598971882251685 2.7709829456953052 14.09439453351279\n",
            "-2.8796928117748273 1.662588837085917 22.715677428186883\n",
            "-2.8796922773036915 -0.5541947284765302 14.09439453351279\n",
            "0.9598975445392618 -2.7709829456953052 17.229406495212462\n",
            "0.9598973663822165 1.662588837085917 20.36441845691213\n",
            "-2.8796928117748375 -0.5541993801328584 23.499430418611794\n",
            "-0.9598970100681216 0.554199380132858 10.175629581388206\n",
            "2.8796928117748335 -1.6625888370859176 13.310641543087874\n",
            "2.8796926336177817 2.7709829456953052 16.445653504787543\n",
            "0.9598979008533545 1.6625934887422456 10.959382571813123\n",
            "-0.9598970100681228 -1.6625888370859176 10.959382571813123\n",
            "-0.95989790085336 -1.6625934887422433 22.715677428186883\n",
            "2.879692277303693 0.5541947284765287 19.58066546648721\n",
            "2.8796926336177857 2.7709829456953075 14.09439453351279\n",
            "-0.9598975445392612 0.5541947284765295 17.229406495212462\n",
            "-4.7994877226963055 -1.6625934887422462 20.36441845691213\n",
            "2.8796920991466406 2.77097829403898 23.499430418611794\n",
            "0.9598977226963068 -2.770978294038978 10.175629581388206\n",
            "0.9598975445392612 1.6625934887422456 13.310641543087874\n",
            "-2.879692633617784 -0.5541947284765298 16.445653504787543\n",
            "0.959897188225171 -2.7709829456953083 19.58066546648721\n",
            "0.9598970100681169 1.6625888370859192 22.715677428186883\n",
            "4.799487722696308 1.6625934887422456 10.959382571813125\n",
            "0.9598975445392626 -0.5541947284765302 14.09439453351279\n",
            "-2.8796926336177857 -2.770982945695306 17.229406495212462\n",
            "-2.8796928117748304 1.6625888370859176 20.36441845691213\n",
            "0.9598970100681222 -0.5541993801328554 23.4994304186118\n",
            "2.879692811774832 0.554199380132858 10.175629581388206\n",
            "-0.9598973663822142 -1.6625888370859176 13.310641543087874\n",
            "-0.959897544539261 2.770982945695306 16.445653504787543\n",
            "-0.9598975445392612 0.5541947284765295 19.58066546648721\n",
            "-4.799487722696305 -1.6625934887422447 22.715677428186883\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaHThAhmx1ko"
      },
      "source": [
        "### For practical applications, materials are generally used as heterostructures. We can use the following as an example to make Si(111)-Si(111) interface"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3CYqCLlx1kp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcc5fb79-1baa-4907-edbb-dcfb9ea6d11a"
      },
      "source": [
        "from jarvis.analysis.interface.zur import ZSLGenerator, mismatch_strts, get_hetero,make_interface\n",
        "\n",
        "film = Surface(atoms=Si, indices=[1, 1, 1], layers = 3, vacuum = 18 ).make_surface().center_around_origin()\n",
        "substrate = Surface(atoms=Si, indices=[1, 1, 1], layers = 3, vacuum = 18 ).make_surface().center_around_origin()\n",
        "info = make_interface(film=film, subs=substrate)['interface'].center(vacuum=18)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mismatch_u,mismatch_v 0.0 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOTDooSPx1kw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "147bbdb6-115d-451e-d253-7cdbd0fb30ec"
      },
      "source": [
        "info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Si80\n",
              "1.0\n",
              "7.67918 -0.0 -0.0\n",
              "3.83959 -6.65036 -0.0\n",
              "-0.0 -0.0 -65.6476016744472\n",
              "Si\n",
              "80\n",
              "Cartesian\n",
              "-2.879692811774834 1.6625888370859174 -39.48894538838912\n",
              "-0.9598971882251709 2.7709829456953083 -48.110228283063215\n",
              "-2.8796926336177857 -2.7709829456953075 -42.62395735008879\n",
              "0.9598975445392615 -0.5541947284765293 -45.75896931178847\n",
              "4.799487722696304 1.6625934887422462 -48.89398127348814\n",
              "-2.8796920991466393 -2.7709782940389798 -52.0289932351878\n",
              "-0.9598977226963069 2.7709782940389776 -38.705192397964204\n",
              "-0.9598975445392604 -1.6625934887422456 -41.84020435966388\n",
              "-2.879692277303693 -0.5541947284765285 -48.110228283063215\n",
              "2.8796926336177835 0.5541947284765301 -44.97521632136355\n",
              "0.9598975445392616 -2.770982945695306 -44.97521632136355\n",
              "-4.799487722696308 -1.6625934887422456 -39.48894538838912\n",
              "-0.9598975445392623 0.5541947284765302 -42.62395735008879\n",
              "2.8796926336177857 2.770982945695306 -45.75896931178847\n",
              "2.87969281177483 -1.6625888370859174 -48.89398127348814\n",
              "-0.959897010068122 0.5541993801328556 -52.02899323518781\n",
              "-2.879692811774831 -0.5541993801328577 -38.705192397964204\n",
              "-0.9598970100681162 -1.6625888370859192 -51.24524024476289\n",
              "0.9598973663822141 1.6625888370859174 -41.84020435966388\n",
              "0.9598979008533601 1.6625934887422433 -51.24524024476289\n",
              "-2.87969263361778 -2.7709829456953052 -44.97521632136355\n",
              "0.9598971882251693 -2.7709829456953052 -42.62395735008879\n",
              "-2.8796926336177857 -0.5541947284765307 -45.75896931178847\n",
              "0.9598975445392599 1.6625934887422462 -48.89398127348814\n",
              "0.9598977226963095 -2.7709782940389776 -52.0289932351878\n",
              "2.8796920991466446 2.7709782940389776 -38.705192397964204\n",
              "-4.799487722696308 -1.6625934887422456 -41.84020435966388\n",
              "-0.9598975445392604 0.5541947284765302 -44.97521632136355\n",
              "0.9598970100681226 1.6625888370859174 -39.48894538838912\n",
              "2.8796926336177804 2.770982945695306 -48.110228283063215\n",
              "-0.9598979008533537 -1.6625934887422456 -39.48894538838912\n",
              "2.8796922773036906 0.5541947284765302 -42.62395735008879\n",
              "-0.959897544539262 2.7709829456953052 -45.75896931178847\n",
              "-0.9598973663822155 -1.662588837085917 -48.89398127348814\n",
              "2.879692811774837 0.5541993801328584 -52.0289932351878\n",
              "0.9598970100681217 -0.5541993801328577 -38.705192397964204\n",
              "-2.879692811774834 1.6625888370859174 -41.84020435966388\n",
              "2.8796928117748273 -1.662588837085917 -51.24524024476289\n",
              "0.9598975445392615 -0.5541947284765293 -48.110228283063215\n",
              "4.799487722696303 1.6625934887422447 -51.24524024476289\n",
              "-2.8796928117748335 1.6625888370859172 -23.16514455116553\n",
              "0.9598975445392613 -0.5541947284765291 -29.435168474564865\n",
              "4.799487722696302 1.6625934887422458 -32.57018043626454\n",
              "-2.8796920991466375 -2.7709782940389793 -35.705192397964204\n",
              "-0.9598977226963072 2.770978294038977 -22.381391560740614\n",
              "-0.9598975445392597 -1.662593488742245 -25.516403522440278\n",
              "2.879692633617783 0.55419472847653 -28.651415484139946\n",
              "-0.9598971882251713 2.770982945695308 -31.786427445839614\n",
              "-0.9598970100681155 -1.6625888370859185 -34.92143940753928\n",
              "-4.799487722696306 -1.662593488742245 -23.16514455116553\n",
              "-0.9598975445392625 0.5541947284765302 -26.300156512865197\n",
              "2.879692633617784 2.7709829456953057 -29.435168474564865\n",
              "2.87969281177483 -1.6625888370859172 -32.57018043626454\n",
              "-0.959897010068122 0.5541993801328555 -35.705192397964204\n",
              "-2.8796928117748295 -0.5541993801328576 -22.381391560740614\n",
              "0.9598973663822136 1.6625888370859172 -25.516403522440278\n",
              "0.9598975445392622 -2.7709829456953057 -28.651415484139946\n",
              "-2.8796922773036915 -0.5541947284765284 -31.786427445839614\n",
              "-2.879692633617784 -2.770982945695307 -26.300156512865197\n",
              "0.9598979008533595 1.6625934887422429 -34.92143940753928\n",
              "0.95989718822517 -2.770982945695305 -26.300156512865197\n",
              "2.879692811774827 -1.6625888370859163 -34.92143940753928\n",
              "-2.879692633617785 -0.5541947284765306 -29.435168474564865\n",
              "0.9598975445392592 1.6625934887422458 -32.57018043626454\n",
              "0.9598977226963097 -2.770978294038977 -35.705192397964204\n",
              "2.8796920991466433 2.770978294038977 -22.381391560740614\n",
              "-4.799487722696306 -1.662593488742245 -25.516403522440278\n",
              "-0.9598975445392602 0.5541947284765302 -28.651415484139953\n",
              "2.8796926336177786 2.7709829456953057 -31.786427445839614\n",
              "-0.959897900853353 -1.662593488742245 -23.16514455116553\n",
              "0.9598970100681224 1.6625888370859172 -23.16514455116553\n",
              "2.8796922773036906 0.5541947284765302 -26.300156512865197\n",
              "-0.9598975445392628 2.770982945695305 -29.435168474564865\n",
              "-0.9598973663822149 -1.6625888370859163 -32.57018043626454\n",
              "2.8796928117748357 0.5541993801328584 -35.705192397964204\n",
              "0.9598970100681216 -0.5541993801328576 -22.381391560740614\n",
              "-2.8796928117748335 1.6625888370859172 -25.516403522440278\n",
              "-2.879692633617778 -2.770982945695305 -28.651415484139946\n",
              "0.9598975445392613 -0.5541947284765291 -31.786427445839614\n",
              "4.799487722696301 1.6625934887422444 -34.92143940753928"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uW-X6_PNx1k3"
      },
      "source": [
        "### Atomic-neighborhood analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "191YA5tMx1k4"
      },
      "source": [
        "from jarvis.analysis.structure.neighbors import NeighborsAnalysis\n",
        "\n",
        "nb = NeighborsAnalysis(Si)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83uvmomAx1k9"
      },
      "source": [
        "bins_rdf, rdf, nbs = nb.get_rdf() #Global Radial distribution function\n",
        "adfa, bins_a = nb.ang_dist_first() #Angular distribution function upto first neighbor\n",
        "adfb, bins_b = nb.ang_dist_second() #Angular distribution function upto second neighbor\n",
        "ddf, bins_d = nb.get_ddf() #Dihedral distribution function upto first neighbor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2SKw__2x1lH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 987
        },
        "outputId": "b97c18ef-5437-4dcb-f3ad-f100c44453f2"
      },
      "source": [
        "import matplotlib\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.gridspec import GridSpec\n",
        "\n",
        "the_grid = GridSpec(2, 2)\n",
        "plt.rcParams.update({'font.size': 24})\n",
        "plt.figure(figsize=(16,14))\n",
        "\n",
        "plt.subplot(the_grid[0, 0])\n",
        "plt.title('(a) RDF')\n",
        "plt.plot(bins_rdf, rdf)\n",
        "plt.xlabel(r'Distance bins ($\\AA$)')\n",
        "\n",
        "plt.subplot(the_grid[0, 1])\n",
        "plt.title('(b) ADF-a')\n",
        "plt.plot(bins_a[:-1], adfa)\n",
        "plt.xlabel(r'Angle bins ($^\\circ$)')\n",
        "\n",
        "plt.subplot(the_grid[1, 0])\n",
        "plt.title('(c) ADF-b')\n",
        "plt.plot(bins_b[:-1], adfb)\n",
        "plt.xlabel(r'Angle bins ($^\\circ$)')\n",
        "\n",
        "plt.subplot(the_grid[1, 1])\n",
        "plt.title('(d) DDF')\n",
        "plt.plot(bins_d[:-1], ddf)\n",
        "plt.xlabel(r'Angle bins ($^\\circ$)')\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1008 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### X-ray diffraction"
      ],
      "metadata": {
        "id": "9AkZkkLI9Qkt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from jarvis.analysis.diffraction.xrd import XRD\n",
        "theta,d_hkls,intens = XRD().simulate(atoms=Si)"
      ],
      "metadata": {
        "id": "y3yTi4VG9Xf1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(theta,intens)"
      ],
      "metadata": {
        "id": "SfvqvRQx-gyx",
        "outputId": "aecbd1b9-b7f8-4a88-a6ee-277129439890",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 12 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from jarvis.core.spectrum import Spectrum\n",
        "import numpy as np\n",
        "def gaussian(theta2, alpha, fwhm):\n",
        "\n",
        "    \"\"\"\n",
        "    Gaussian function for profiling peaks\n",
        "    \"\"\"\n",
        "\n",
        "    tmp = ((alpha - theta2)/fwhm)**2\n",
        "    return np.exp(-4*np.log(2)*tmp)\n",
        "\n",
        "def get_profile(fwhm=0.1,min2theta=0,max2theta=180,res=0.02,two_thetas=[],intensities=[]):\n",
        "    N = int((max2theta-min2theta)/res)\n",
        "    px = np.linspace(min2theta, max2theta, N)\n",
        "    py = np.zeros((N))\n",
        "    for two_theta, intensity in zip(two_thetas, intensities):\n",
        "        tmp = gaussian(two_theta, px, fwhm)\n",
        "        py += intensity * tmp\n",
        "    py /= np.max(py)\n",
        "    return px,py\n",
        "\n",
        "px,py=get_profile(two_thetas=theta,intensities=intens)"
      ],
      "metadata": {
        "id": "bMXm8u_P-s7F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(px,py)"
      ],
      "metadata": {
        "id": "0PbhpetX-7_F",
        "outputId": "48002a41-098b-40b5-eb1a-8fe6040b0358",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3723a57130>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CY1sFwVkx1lO"
      },
      "source": [
        "### Getting CFID descriptors: We can convert the chemo-physical information in the Atoms class to CFID features that can be used in Machine-learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1xRVBOnx1lQ"
      },
      "source": [
        "from jarvis.ai.descriptors.cfid import CFID\n",
        "\n",
        "cfid = CFID(Si)\n",
        "feature_names = cfid.get_comp_descp(print_names=True)\n",
        "feature_values = cfid.get_comp_descp().tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60brE0YHx1lV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afb66a1b-4fbf-4e0b-b6ef-cdc63c0b9e1f"
      },
      "source": [
        "print (feature_names[0], feature_values[0], len(feature_names), len(feature_values) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bp_mult_atom_rad 3490.3 1557 1557\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get an atomistic graph"
      ],
      "metadata": {
        "id": "i4mwNSrw_VfX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dgl"
      ],
      "metadata": {
        "id": "6ZSk2YEC_gJ-",
        "outputId": "f04c7d8e-6cac-4889-89ef-80e43dee41be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting dgl\n",
            "  Downloading dgl-1.0.1-cp38-cp38-manylinux1_x86_64.whl (5.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from dgl) (2.25.1)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from dgl) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.8/dist-packages (from dgl) (1.22.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from dgl) (4.64.1)\n",
            "Collecting psutil>=5.8.0\n",
            "  Downloading psutil-5.9.4-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (280 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.2/280.2 KB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.8/dist-packages (from dgl) (3.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->dgl) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->dgl) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->dgl) (1.24.3)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->dgl) (4.0.0)\n",
            "Installing collected packages: psutil, dgl\n",
            "  Attempting uninstall: psutil\n",
            "    Found existing installation: psutil 5.4.8\n",
            "    Uninstalling psutil-5.4.8:\n",
            "      Successfully uninstalled psutil-5.4.8\n",
            "Successfully installed dgl-1.0.1 psutil-5.9.4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "psutil"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6l1XgT0fx1la"
      },
      "source": [
        "from jarvis.core.graphs import Graph\n",
        "g,lg = Graph.atom_dgl_multigraph(atoms=Si)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g #graph"
      ],
      "metadata": {
        "id": "nZCKTYJq_fEV",
        "outputId": "a08f6eda-261b-48df-aa3e-15479778a870",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graph(num_nodes=2, num_edges=64,\n",
              "      ndata_schemes={'atom_features': Scheme(shape=(92,), dtype=torch.float32)}\n",
              "      edata_schemes={'r': Scheme(shape=(3,), dtype=torch.float32)})"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lg #line graph"
      ],
      "metadata": {
        "id": "goveM-AP_neW",
        "outputId": "34f523af-e135-41ad-ad23-a439db2f3d0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graph(num_nodes=64, num_edges=2000,\n",
              "      ndata_schemes={'r': Scheme(shape=(3,), dtype=torch.float32)}\n",
              "      edata_schemes={'h': Scheme(shape=(), dtype=torch.float32)})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dS8_MX-L_qfV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}