02_Introduction_to_ASE_building_structures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started With ASE\n",
    "\n",
    "[The atomistic simulation environment](https://wiki.fysik.dtu.dk/ase/index.html) (ASE) is an extremely powerful set of tools and Python modules for generating atomic structures and performing density functional theory calculations (DFT.) It can be used for working with and analyzing atomistic simulations. To install ASE on your local machine, run the commands in the next code block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enabling notebook extension nglview-js-widgets/extension...\r\n",
      "      - Validating: \u001b[32mOK\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install ase #install ASE to your local/remote machine\n",
    "!pip install nglview #this needs to be install to visualize structures through jupyter notebook\n",
    "!jupyter-nbextension enable nglview --py --sys-prefix  # this might be needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson, we will learn how to use the ASE Atoms object, building different chemical systems like molecule, bulk and surfaces and setting up constraints. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Atoms Object\n",
    "ASE allows you to manipulate atomic structures in a programitic way using the [ASE atoms object](https://wiki.fysik.dtu.dk/ase/ase/atoms.html). This is a `class` in python that can store the positions and identities of atoms in a structure and manipulate them in useful ways. This is pretty abstract, so here is an example of generating an atoms object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='H2', pbc=False)\n"
     ]
    }
   ],
   "source": [
    "from ase.atoms import Atoms # import the Atoms class from ASE\n",
    "\n",
    "H2 = Atoms(symbols='HH', positions=[(0,0,0), (0,0,0.75)])\n",
    "print(H2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above just generates an H$_2$ molecule, one hydrogen at the origin and one 0.75 angstroms up in the z direction. We can now manipulate it in interesting ways. Let's say we want to add a second H$_2$ molecule 2 angstroms away. We can do this by simply adding atoms objects to the one we already have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='H4', pbc=False)\n",
      "[[0.   0.   0.  ]\n",
      " [0.   0.   0.75]\n",
      " [2.   0.   0.  ]\n",
      " [2.   0.   0.75]]\n"
     ]
    }
   ],
   "source": [
    "H2 = H2 + Atoms('H2', positions = [(2,0,0), (2,0,0.75)])\n",
    "print(H2)\n",
    "print(H2.positions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have have two H$_2$ molecules, we can shift all the atoms by simply adding an array to their positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.   1.  ]\n",
      " [0.   0.   1.75]\n",
      " [2.   0.   1.  ]\n",
      " [2.   0.   1.75]]\n"
     ]
    }
   ],
   "source": [
    "H2.positions = H2.positions + [0,0,1]\n",
    "print(H2.positions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this look like though? We can visualize any structure using ASE's built in visualization tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d6e04982b764f28ae452f1776b554dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(NGLWidget(), VBox(children=(Dropdown(description='Show', options=('All', 'H'), value='All'), Dr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ase.visualize import view\n",
    "\n",
    "view(H2, viewer='ngl') # removing viewer='ngl' makes a pop-up window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of visualizing the atoms object by using ASE visualizer or nglview, we can also use **plot_atoms** method in ASE which uses matplotlib as backend. It is useful to generate simple projections in jupyter notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8796493690>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from ase.visualize.plot import plot_atoms\n",
    "\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(H2,rotation='10x,20y,2z')\n",
    "#plt.savefig('cell_example.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atoms objects are also able to be indexed like lists. Each individual atom has an index and can be accessed in this way. When you call an index of an atoms object, you get an `Atom` object. This is just an object that represents a single atom. `Atoms` objects are simply a collection of `Atom` objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atom('H', [0.0, 0.0, 1.0], index=0)\n",
      "[0. 0. 1.]\n",
      "H\n"
     ]
    }
   ],
   "source": [
    "print(H2[0]) #prints the atom at index 0 of list H2\n",
    "print(H2[0].position) #prints the position of atom at index 0 of list H2\n",
    "print(H2[0].symbol) #prints the symbol of atom at index 0 of list H2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our DFT calculations involving metals and metal oxides, we will be doing periodic calculations. For these periodic calculations, we would want to put these atoms into a unit cell (a simulation box) to do this we want to use the `set_cell` method. Let's use a 10 angstrom box. We will also need to write this to a file. ASE atoms objects have a `write` method that allows you to write to [almost any file type you can imagine](https://wiki.fysik.dtu.dk/ase/ase/io/io.html) (including .png images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell([10.0, 10.0, 10.0])\n"
     ]
    }
   ],
   "source": [
    "H2.set_cell([10,10,10])\n",
    "H2.center() # this centers it in the unit cell\n",
    "print(H2.cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8796542550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(H2,rotation='10x,20y,2z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also write and read files using ASE. For this we will need to import `read` and `write` method from ase.io. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "H2.write('2_hydrogens.xyz')\n",
    "\n",
    "from ase.io import read # for reading files\n",
    "\n",
    "H2_2 = read('2_hydrogens.xyz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are lots of useful things you can do to manipulate atoms, they are all documentated in the [ASE atoms object documentation](https://wiki.fysik.dtu.dk/ase/ase/atoms.html).\n",
    "\n",
    "But having to have all the positions for the atoms in an atoms object is quite onerous. Luckily, there are [tools to build structures in ASE](https://wiki.fysik.dtu.dk/ase/ase/build/build.html). There are methods like `molecule`, `'bulk`, `surfaces` etc that can be used to build structures using ASE's database if the positions and unit cell information is not known. For example, the `ase.lattice` module has functions that can create common crystal structures if the use specifies miller indices. There is a `molecule `function that holds the positions of lots of common molecules. Similarly, the `bulk` function contains tons of bulk structures for metals. We'll use this to generate surfaces a little later on. Let's see how the `molecule` and `bulk` methods work:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='OH2', pbc=False)\n",
      "[[ 0.        0.        0.119262]\n",
      " [ 0.        0.763239 -0.477047]\n",
      " [ 0.       -0.763239 -0.477047]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8796cb5450>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ase.build import molecule, bulk\n",
    "#from ase.data.pubchem import pubchem_atoms_search\n",
    "\n",
    "water = molecule('H2O')\n",
    "print(water)\n",
    "print(water.positions)\n",
    "#view(water, viewer='ngl')\n",
    "\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(water,rotation='10x,20y,2z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ASE has the G2-database of common molecules. To see the list of available molecules in this database, run the following code block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PH3', 'P2', 'CH3CHO', 'H2COH', 'CS', 'OCHCHO', 'C3H9C', 'CH3COF', 'CH3CH2OCH3', 'HCOOH', 'HCCl3', 'HOCl', 'H2', 'SH2', 'C2H2', 'C4H4NH', 'CH3SCH3', 'SiH2_s3B1d', 'CH3SH', 'CH3CO', 'CO', 'ClF3', 'SiH4', 'C2H6CHOH', 'CH2NHCH2', 'isobutene', 'HCO', 'bicyclobutane', 'LiF', 'Si', 'C2H6', 'CN', 'ClNO', 'S', 'SiF4', 'H3CNH2', 'methylenecyclopropane', 'CH3CH2OH', 'F', 'NaCl', 'CH3Cl', 'CH3SiH3', 'AlF3', 'C2H3', 'ClF', 'PF3', 'PH2', 'CH3CN', 'cyclobutene', 'CH3ONO', 'SiH3', 'C3H6_D3h', 'CO2', 'NO', 'trans-butane', 'H2CCHCl', 'LiH', 'NH2', 'CH', 'CH2OCH2', 'C6H6', 'CH3CONH2', 'cyclobutane', 'H2CCHCN', 'butadiene', 'C', 'H2CO', 'CH3COOH', 'HCF3', 'CH3S', 'CS2', 'SiH2_s1A1d', 'C4H4S', 'N2H4', 'OH', 'CH3OCH3', 'C5H5N', 'H2O', 'HCl', 'CH2_s1A1d', 'CH3CH2SH', 'CH3NO2', 'Cl', 'Be', 'BCl3', 'C4H4O', 'Al', 'CH3O', 'CH3OH', 'C3H7Cl', 'isobutane', 'Na', 'CCl4', 'CH3CH2O', 'H2CCHF', 'C3H7', 'CH3', 'O3', 'P', 'C2H4', 'NCCN', 'S2', 'AlCl3', 'SiCl4', 'SiO', 'C3H4_D2d', 'H', 'COF2', '2-butyne', 'C2H5', 'BF3', 'N2O', 'F2O', 'SO2', 'H2CCl2', 'CF3CN', 'HCN', 'C2H6NH', 'OCS', 'B', 'ClO', 'C3H8', 'HF', 'O2', 'SO', 'NH', 'C2F4', 'NF3', 'CH2_s3B1d', 'CH3CH2Cl', 'CH3COCl', 'NH3', 'C3H9N', 'CF4', 'C3H6_Cs', 'Si2H6', 'HCOOCH3', 'O', 'CCH', 'N', 'Si2', 'C2H6SO', 'C5H8', 'H2CF2', 'Li2', 'CH2SCH2', 'C2Cl4', 'C3H4_C3v', 'CH3COCH3', 'F2', 'CH4', 'SH', 'H2CCO', 'CH3CH2NH2', 'Li', 'N2', 'Cl2', 'H2O2', 'Na2', 'BeH', 'C3H4_C2v', 'NO2']\n"
     ]
    }
   ],
   "source": [
    "from ase.collections import g2\n",
    "print(g2.names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a bulk crystal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For building a bulk crystal, different inputs like type of crystal structure, lattice constants, type of cell (`orthorhombic` and `cubic`) can be provided to the `bulk` method of ASE. If any of the inputs are not provided, then they will be guessed by ASE's method. Let's look at the following examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Fe2', pbc=True, cell=[2.87, 2.87, 2.87])\n",
      "[[0.    0.    0.   ]\n",
      " [1.435 1.435 1.435]]\n"
     ]
    }
   ],
   "source": [
    "iron = bulk('Fe', cubic = True)\n",
    "print(iron)\n",
    "print(iron.positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8796d676d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#view(iron, viewer='ngl')\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(iron,rotation='10x,20y,2z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8796ea14d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKgAAACMCAYAAADoduKUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAcS0lEQVR4nO2deXzNV/7/nyeLJGILCbm3KWqnKCKCkP74oWpp+5tqB6VltNpqtAbTjqUzNf3NqM5Q+i2ttRVUtVWMpdbYl5IEobaKNJZce0L27b5/f4T8RLZ7b+4an+fjcR+P+JxzPud945Wzvd/nHCUiaGg4K26ONkBDoyw0gWo4NZpANZwaTaAaTo0mUA2nRhOohlPjYYuX+vv7S8OGDW3xao1KSkxMzE0RCXj4uU0E2rBhQ6Kjo23xao1KilIqsaTnNhGohnUxGo2MGjWKuLg4GjZsiE6nY9SoUbRv3x6AnJwcUlNTqV27NkopB1trXTSBOjkiwpgxY4iMjGT58uV4eHhgMBjw8vIqzHPy5El69epFRkYGOp2OtWvX8tRTTwFw/vx5Dhw4gE6nQ6/X07x5czw8XOe/3XUsfUT5+9//ztatWxk0aBBDhgwpMU+HDh24ffs2mZmZGAwGdDpdYdqtW7fYtm0bSUlJJCUlsX//fmrXrg3AypUrWbNmDXq9nqCgICZOnFhYLicnB09PT4e3yJpAnRgRwdfXl0OHDlGnTp1y8/v4+NCoUaMiz0JDQwkNDS0xf0hICG5ubiQlJZGSklIkbezYsSxbtgy9Xk+XLl1YtmxZYdru3bsxGo3odDoee+wxqlevbsG3Mw1li2CRjh07ijZJsg65ubl4eno6pO7U1FQMBgMZGRm0a9eu8PmHH37I3r17SUpKIiQkhBUrVhSmjRs3Dnd3d/R6PWFhYXTu3NmkupRSMSLS8eHnWgvqhKSlpVGtWjVEhPDwcObMmUOnTp3sbkf16tVLbB0//vjjUst069aN33//nStXrpCYmFgo0JycHAICAggMDMTPz48BAwYwderU8o0QEat/goODRcMy1qxZIy1btpTc3FzZu3evNGnSRPLz8x1tVoUxGo2SkpIiBw4ckDp16kizZs2KpAPRUoKWtBbUidi2bRujR4/m559/xsPDg8aNGxMZGYmbm+s7/JRSuLm5MW7cOESExYsXm1ROE6iTkJGRwejRo1m9ejXBwcEA6HS6IjNyVycuLo7Q0FDWrFlj8vfSJklORHp6Or6+vgAsXLiQ8PBwmjdv7mCr7ENpkyTX7zsqEffFmZyczAcffGDT5Rt78WADGB0dzejRo80qrwnUgSQkJPDZZ58Ve37q1ClGjhyJXq93gFXWw2g08sYbb7Bq1SoAZs2aRYsWLcx6hyZQB3HlyhV69eqFt7d3sbSwsDBmzpzpAKush4gwfvx4Tp06Rf/+/QFo27Yto0aNMv9F1v5oy0xlc/36dWnZsqXMmDGjWNqVK1cqxbLS3LlzpV27dpKcnGxSfkpZZjJZdIA7cBTYUF5eTaBlc+fOHVm0aFGx50ajUTp06CBbtmxxgFXW5fbt23Lt2jUREbl7966sX7++zPylCdScLv494LR57bNGSdSoUaPErm7fvn2kp6fTq1cvB1hlXfz8/Khbty4AS5YsITIy0qL3mCRQpVQQ0B9YZFEtGmRlZRULyHiYzp07s2nTJpddmN+8eTN3794t9nzNmjWMHz/eonea+puYDbwPGC2q5REnNzeXwYMHM2PGjDLzeXp6FotGchXWrl3LiBEjSEpKKpa2fft2k4NGHqZcgSqlBgDXRSSmnHyjlVLRSqnoGzduWGRMZcRoNDJy5EhycnKYNm1aqfmmTZvGvn377GiZ9bjvot24cWOxZaS8vLyKBUiXNDCVopOj6cBl4HfgKpABLC+rjDZJ+v9s2bJFnn76acnIyCg1z82bN6VWrVpiMBjsaJn1+Pe//y179uwp9vzgwYPSvXt3k95BRWfxBe/gf6HN4s0mNze3zPQzZ87If/7zHztZYz9efvllmT17tkl5SxOoFixiB8rr4po3b+5yPndTuu5XX32V8PDwCtVj1nRRRHaJyIAK1fgIMHfuXLZv325S3qNHj3L9+nUbW2RdEhISaNOmTbl29+/fv8LxBK65nuHELFmyhE8//ZSmTZuWm1dEGDFiBLGxsXawzDrcd9GOHTu2cJ3zYe7cucP06dOtUp/WxVuR1atXM3XqVHbt2kWDBg3KzX/s2DHy8/N55pln7GBdxRERXn75ZUaPHs2YMWNKzbd48WJOnDhhlTo1gVqRVq1asXnzZpo1a2ZS/vbt23P48GGHb+01FaUUq1evJjAwsMx8Z8+e5c9//rN16hQtYNkhiIjLCNMeaAHLNiI+Ph6j0XwHW0REBD/++KMNLLIu2dnZLFiwAFMaMhHBYDBYtX5NoBUgLi6Orl27cvToUbPK3bhxg2+//bbCSzC25r6Ldtu2bSb9ER44cICePXuaJGZT0cagFnLu3DmeffZZPv/888JNbubw1VdflToLdgbuu2izsrJYt24d7u7u5Zb57LPPeOedd6w7dClp9b6in0fBkxQRESGLFy92tBk2IzMzU6ZMmSLp6ekml0lISJC0tDSL6qMUT5I2SbIQsXCSs337dvz9/YscJaOhTZKsjiXiFBHGjRvHzZs3bWCRdcjIyDC7TEpKCsOGDbNoslgemkBNJDU1lZdffpmKhBKuWrWK5ORk1q1bx/Tp09m7d2+RdFv0ZuYwZ84cXnrpJbPLLVy4sPDkEGujCdQEMjIyGDhwILVr18bf39+id+zcuZN3332XCRMm0LhxY1JSUrhy5Uphen5+PjVq1KBRo0Z069aNn376qTAtKyuLHTt2cPr0aVJSUmwi5CVLljBr1izmzZtndlkvLy8mTJhgdZsAbZJUHkajUQYOHCivvPKKxbstDx06JAEBAbJz584y86Wnp8v58+dlz549cuHChcLnSUlJ0qNHD2nevLlUr15d1q5dW5h29uxZGTdunHz66aeyfPlyk3dRPkhCQoIEBQXJ2bNnzS5rLbA0HhTwBg4Dx4FfgWnllalMAhUR2bt3r+Tk5Fhcfvbs2bJhwwar2WM0Ggt/vnjxosycOVMmTJggQ4YMkcTExMK0ZcuWSdOmTSU8PFwGDx5cZEZ+6dIlOXv2rKSmpopIwc5LS+zYtWtXEXsspSICVUC1ez97Ar8AncsqU9kE6qpkZGTImTNnJCoqSpYtW1akB/jiiy+kSZMm4uPjIw0bNixSbtWqVTJz5kxZuXKlHD58uNT37969W5o1a2aVffylCbTchfp7hdPu/dPz3qdS3+EtIhw5cqRCh8YajUaH78708fEpNRg6JCSEf/3rXwwaNIj09PQiaW5ubly8eJFDhw4REBBASEhIYdrQoUO5du0agYGBhUEhNv2eJan24Q8FhzYco0CoM8rL7+ot6NSpUyU4OLjcrRqlYTAY5KmnnirS3ToTcXFxUrdu3XIPUyiJc+fOydatW6Vfv37Stm1bycrKsopNWGlPUi1gJ9C6hLTRQDQQXb9+fasY7QhmzJghLVq0kOvXr1tU/tatW9KmTRv5xz/+YWXLrMO5c+dEr9fLd999Z/E7Pv74Y2ndurXcvHnTanZZRaAF7+HvwMSy8rhqCxoVFSV+fn4SGhoqf/zjHyUyMrJI+vnz58udTPTr108mTpxolYmDLUhISJCVK1daXN5gMEjHjh0lKSnJilaVLtByXZ1KqQAgV0RSlFI+wNZ73fyG0sq4qqszLCyMIUOG0Lp1a5KSktDpdPTo0QMo+ENu06YNCQkJuLm5MX36dCIiIgDIzMxk3rx56PV6PDw86Nmzp0nXxrgqItaPZa3ILR86YKlSyp2Chf3vyxKnq2IwGMjOzuatt94qcbeiUoqTJ08iIqSmpvLgH3Z2djaXL1/m8OHDGAwGqlevTt++fQE4ceIE7733Hnq9Hr1ez9ixY3n88ceBAmGLCFWrVrXpd0tJScHX19dq19nYM9DalFl8HNDeDrZYjIhw+fJl0tLSyMrKIicnBy8vL7y9vfHz86NevXrlvkOn03HkyJFyf/lKKWrUqFH47/z8fNasWcPMmTNLnM02aNCAyZMnYzAYSEpKKiL+jRs3MmzYMLy9vdHr9ezbt6/wFrhjx45x7ty5QmFbeiROamoqffr0YcyYMYwYMcKid2zatInY2FjTro2xMi4XDyoixMfHExMTw5HDvxB9aD9H407i5elGNZ8qeHu64+6myM03kpNn5PbdTLy8vAlu/xQhXbrTMSSE4ODgIqcXZ2Rk4O3tbfZyyf0ThC9evMjQoUOL3J95nxo1apR6Wt2gQYN48cUXSU5OxmAwULNmzcK0xMREvv/+e5KSkkhLSyMuLq4wbc6cOcTGxqLT6WjZsiWvvfZaYVp+fn5h7GZmZiYDBw6kffv2RfKYw65duxgxYgTr16+3qHxFcZlwO4PBwMIF85n/5VwkP4eWj1WnaR1FC503LfRV8fMtufsSEa7eyeFMUgZnr2bz2y3h1MUUdHod77w7nuHDh/PRRx/h7+/PpEmTTLZHpCAyKTo6mq1btxaeL28PYmNjOX78eOGlsg/6wXv06EFcXBx6vZ6qVavSpEkTIiMjTQo4fpjjx4/Tu3dvVq1aVTgWtxWljUGdWqAiwp49e/hizky2bttOrydr83/a16BpYMXGbCJCdEIqa46lEXPhDlm5+WzYsMGsczlzc3OZOnUqkyZNolatWhWyx5oYjUZu3bpFVFQUERERJCYmWjzGzcjI4OjRo4SFhVnZyuK4nED37dvHm6+PJDv1Fi+0q86zbWtTzdv8VqA8rtzOYvGe60QnZtK67VMsWLyUxo0bW70eexMREUFQUBB//etfHW2KSbiMQDMyMpj0wft8t2Ip43rXpUfLWnaZNeblCz8cvknkwVt89PH/JSLi3RLHpFlZWSVefOBs5OTkkJeXZ3brefHiRby9ve2+X8olIur37dtHm1bNObf/J5a90ZierfxsKs4Dv90h6lQyAB7uiiFdAvjq1YZ8PeefPB3Wmfj4+CL5ly9fzjPPPIMt/qitTZUqVcwW59WrV+nVqxcbN260kVXm4xQCNRqNTBw/jhefe5Y3O3vx0fN6ala1/QLDkt0G3B7SfwN/b+YOq09wzWuEdGjH4kULgYJjrCdOnMiXX37p1AcupKenExoaavbWjdu3b9OnTx+GDx/OyJEjbWSd+Th8mSk3N5dXXxnC+WN7WPZGY7sIEyAtK5/AmlXo3rz4BMfdraA17dKkOn+Z8hfi4+P5+pulbNq0iVatWtnFPkuJjIwsnMGbQ1RUFH379nXIWmdZOHQMmpOTwx+eH0DqpTj+8YIeb0+naNCLcONuDuO+u8zAQa8wa/bnTt16AmzduhV/f386dOjgaFPMoiKuTpuQn5/P0D8OIv1yHP/6w2N4uNvvP/52Wi7ubsqk1jqgRhXmDatPxIoV+PsHMOXDv9nBQsvp06ePo02wKg5pskSEt94YxaVTh5j2vN6u4gT4Zu9VVhy4ZnL+mlU9+Gzw48z/Yhbz5s21oWUVY+nSpSaPPfPz8xk+fHixnaXOhkME+sMPPxC1eR3TX3wMLzt365k5+Ww+fotBIQFmlfOv7slngx/nw8kfcPLkSRtZZzkxMTF8+OGHJgWE3HfRGgyGItHyzojdBXr9+nUi3n6Tyf0D8fWy/sJ7efhUcWf5262oW7OK2WWDansxOjyA114ZTF5eng2ss5xt27bx7rvvmiTQadOmcfbsWdatW+f0a7qmxIM+DkQCgRRc5LVAROaUVaa0SZKI8OILA6mWcpx3epYfYeSMiAh/XnWZAa+8w5SpzjUeNTVOMyEhAT8/P6dy0VZkoT4PmCAiLYHOwDtKKYvWWr7//nuORx/g9e7mda/OhFKKv/atx6z/fOo0XX1WVhZgepzmE0884VTiLItyBSoiBhGJvfdzKgUXyj5mbkXJycmMfectpvSrZ/dxp7UJrFWF0eH+jBg2xOFepbS0NBo3bszt27fLzLdjxw6nG5aYgllKUUo1pCB4+RdzK1qyZBEdG1TlySD7haXZkuc71CHlZhK7d+92qB1ff/01Xbt2LQx0Lonly5czYsQIrl69akfLrIPJC/VKqWrAbuCfIvJTCemjKdjZSf369YMTExML04xGI02fqM+k3tVo83g1qxjuDPxw+AYJnm1ZvdYxwbwA58+fx2g0lnpxw9q1a3n77bfZsWOHU3vBKhQsopTyBFYDK0oSJ4CILBCRjiLSMSCg6Bhz+/btVCGb1pWk9bxPv7a12b5jR4k3/NqLJk2alHmryK5du9iwYYNTi7MsTLntWAGLgdMiMsuSSj7/7N+88JSv07sJzcXX253erWuzcMFXDqn/b3/7W7lnjc6ePduiI8qdBVNa0DBgONBTKXXs3qefqRVcvHiR/fsP8EwbP4uNdGZeaFeD+V/OIzc31671Hj58mMjIyBJn47Y4SNZRmDKL3yciSkTaiki7e59NplawYcMGurfww6eK/Rfl7UHTwKrU9FbExMTYtd74+HgmT55cbIv0qVOnCAkJITMz06722AqbB4scObSfFnUrpzjv0yLQi5iYGDp37my3OocMGVLs2YULF+jTpw+ffPIJPj4+drPFlth8QfLI4V9oqbftwQSOplmAO0cO7bNbffHx8cXWX/Py8ujXrx9Tpkxh2LBhdrPF1thUoJmZmcT/fpHGdSvHX3NptNBXJfrIEbvUdffuXTp16sTly5eLPPfw8GDz5s28/fbbdrHDXthUoHFxcTwRWMvlPUfl0biuD/G/X7TLuC8yMpJevXoVHp/zIA0bNrR5/fbGpsqJjY2lWb3ip21UNrw83XgisJbVrqAui9dff53PP/8cKNh/5Ar3fVYEmwr0xo0b1PbOt2UVTkOd6p4VuqLGVLy9valXrx5ZWVm88MILbNq0yeHxALbEtmPQjHS8PCrX4nxpeHkom3fxw4YNIyEhofCSVz8/v8I7iiorNp4kZeDpUbnHn/ep4q4Kw95swcGDBzl48CD169cnNTWVRo0asXz5covOXHIlbLoO6uHhSZax8nY/D5JvlBLPFbXa+/PzmTFjBu7u7tSuXZtZsyzyOrscNhWoT1Vf7uZVHrdbWeTkY9PF8bCwMLu7U50BmwrU19eXzLzKOz4CSErO5rX5p8kzKqZNm0ZgYCChoaFAQZD26dOn0el06HQ6i/f/REVFsWPHDm7evMn8+fOtab7TY1OBtmjRgo0rbFmD49HVqsIPY59k8Fe/MWHCBBo0aFCYduHCBcaPH4/BYMBgMBAbG0vr1q2BgjHl+vXr0ev16HQ6Bg4cSJUqxTfy3blzh4EDB1K3bl0OHjxot+/lLNhUoMHBwZy+dAeRwEo701RKkZMnuHt4MnTo0CLfMzg4mEOHDgHFI4x8fX2pWrUqp06dYseOHTz33HOFaZ988gnffvster2e5OSCw8127dpFYGCgHb6Rc2FTgQYFBYGbOzfu5lq0zddVOGPIoEO7tmX+ET58lGPbtm1p27ZtiXnffPNN+vbtS1JSEnfv3iU8PLzIkeWPEuUKVCm1BBgAXBeR1ua8XClFh3ZtOWMwVHKBZhHStZvV3ufn54efnx/t2rWz2jtdFVMWKb8B+lpaQacu3TltsN36oDPw2y0hpFOoo82olJgSsLwHKHtPaxl06x5O9KUcS4s7PVk5Rk4kphTO3DWsi9XcPEqp0UqpaKVU9IM+6d69e5OcqTiTZN6Bqq7C1pO36dq1CzqdztGmVEqsJtDSdnW6u7vz1pgI1h67a62qnAYRYc3xdMaOm+hoUyotdnGUv/7GaKJO3SY10/VOtiiLX69kkGn0rHRncjoTdhFovXr16Nu3L5uOJ9ujOrux5lgqY8aOM/uGOg3TMWVf/ErgINBcKXVZKTXKkorGvjeeH4/eISu3cvjmDSnZ7DuTzJ/+ZNGvQ8NETJnFDxERnYh4ikiQiCy2pKJu3brRqUs4i/faPqjX1ogIn/x8nQ8mTa7U1247A3btm+bNX8TmX9M4eSnNntVanbUxt8n3qcdf3v/A0aZUeuwq0Lp16zL3ywX8c9M1l+3qk5KzWbj3BktXfGfT+E+NAuw+un/ppZfoENrdJbt6EWHG5utMfH+Syx7G5Wo4ZPr55YLFbDudyc5TrjWrX7z3BsaqgVrXbkcc0kcFBASwacs2evd8mqpe7oQ2ruEIM8ziu19usitBsf/QNq1rtyMOW8Br3749a9dvYtp/DRyOd24v049HbvLjsUx27Npr91uAH3UcusLcrVs31vx3I9PWX2X36RRHmlIiIsI3+26w+kQee/Yfon79+o426ZHD4X1VeHg4W7bvpF/f3py/kcNrYQF2v3muJO5m5jFn+3V+T/dl/6E9WjCIg3AKH11wcDAxR+NIVI14Y2kiv111bOTT/nN3GL7wAo8H9+fQkaOaOB2Iw1vQ+wQFBbF5206+/noJ4yaMZ1BwTYZ3tW9rmpqZx+c7bhB3VVj54zp69Ohht7o1SsYpWtD7KKX4059GcezEryTQiFHfJLL1xG1ybLy3PjUzj1W/3GD4ogQe69CPk6fPaeJ0EpxKoPe535rO+J/FRF2rxx+++I2vdl7DkJJt1XrOGjKY8fNVXpx7jss+7VmzYQtfzl9ItWqV56ocV8fke5LMobS7Oi3lzJkzzPvif1i+LJK29avT5QlPWuh8aVzX26yznzKy8zl3NZMzSens/C2bmxnw1pgIXn9j9CO5pdeZKO2eJJMEqpTqC8wB3IFFIvJJWfmtLdD7pKens2rVKqK2bSYm+gi/X7pCE70fzep60tjfHV8vN7w83PBwV+TmCdl5Ru5k5PHbLeGMIZMrN9No1bwxHTt15tn+zzFgwABt0d1JsFigSil34BzQG7gMHAGGiMip0srYSqAPk56ezrFjx4iJiSHuaDSpd++QlZVJTnYOXt5e+Pj44lfHnw4dOxEcHMyTTz5Z4ukdGo6nNIGa0nx0As6LyIV7L/oOeB4oVaD2wtfXl7CwMMLCwhxtioaNMGUA9xhw6YF/X8aC2441NCzBlBa0pIXIYuOCBy+TBbKVUs5xmbpp+ANl3ynofFQ2mxuU9NAUgV4GHrxSIggodnuqiCwAFgAopaJLGk84K65mLzw6NpvSxR8BmiqlnlBKVQEGA/+1xEANDXMptwUVkTylVASwhYJlpiUi8qvNLdPQwERf/L3LY02+QJZ7Xb0L4Wr2wiNis008SRoa1sIpffEaGvexqkCVUn2VUmeVUueVUn+15rttgVLqcaXUTqXUaaXUr0qp9xxtkykopdyVUkeVUhscbYspKKVqKaV+VEqdufe77mJyWWt18Za4RB2NUkoH6EQkVilVHYgBXnBmmwGUUuOBjkANERngaHvKQym1FNgrIovurQRVFRGT9vhYswUtdImKSA5w3yXqtIiIQURi7/2cCpzGyb1kSqkgoD+wyNG2mIJSqgYQDiwGEJEcU8UJ1hWoS7tElVINgfbAL461pFxmA+8DrnI0SyPgBvD1vWHJIqWUr6mFrSlQk1yizohSqhqwGhgnIk67B1opdf8yixhH22IGHkAH4EsRaQ+kAybPT6wpUJNcos6GUsqTAnGuEJGfHG1POYQBzymlfqdgCNVTKbXcsSaVy2Xgsojc75l+pECwJmFNgbqcS1QVXGy0GDgtIk5/O6uITLp3BGZDCn6/USIyzMFmlYmIXAUuKaWa33v0vzEjVNNq4eQu6hINA4YDJ5RSx+49m3zPc6ZhPcYCK+41XBeAkaYW1DxJGk6N5knScGo0gWo4NZpANZwaTaAaTo0mUA2nRhOohlOjCVTDqdEEquHU/D+vG5JuoGbbGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cu = bulk('Cu', 'fcc', a=3.6)\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(Cu,rotation='10x,20y,2z')\n",
    "#view(Cu, viewer='nglview')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8797230f50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cu_orthorhombic = bulk('Cu', 'fcc', a=3.6, orthorhombic=True)\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(Cu_orthorhombic,rotation='10x,20y,2z')\n",
    "#view(Cu_orthorhombic, viewer='nglview')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f879736ea50>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5pchWrFhBh8Z1CK6r7F6PI1isEk8HetVerfxJTi/iTI59k2NH8NZ6VLhX1rt3b5y5jVRd3FJkK5ct5e7WrncbXYnOU8Uv026yu71Wo+LONgFsPnTB6bZo1YJio/PFay9Go5EtW7bY3d7tRGa1WtmfkOgWvsl/cuWRNHto29CHw2mFithiq2ByOH36dJztdbmSnJwcHnnkEbvbu53Ijhw5QkAdHXW9rv+g3ehQLw4rsDFrtEj0FeTWWLZsmaLRsRUd2ikLtxPZnj17iA5zv14s9byRRxZWHgFxJWH+nhSbbOQU2LefZC8Gk7XCc5WOisBR1Go1nTt3tru924ls959/0Mx9tsZKMVlsWBzc9xJC0DzUi+Q05/ZmJ7IMtGrTttz3p02bVppuQAkiIiJYvXq13e3dTmSJCftp6sJIi/IwWSSaKgRINg3ROz3W7Ei2pNPNXct9f+LEiW6V1M/tRFZYWICX1u3MokmwjvnDmjl8nZenxzUxaNUlOaPY7kMcSlBYWMipU6fsbu92v6ahuBitm+zyX4lGrSLQ1/HT455qgdHiPPeSwWTlbHY+MTHX5t6AktX5nDlznPa8svjzzz8ZOXKk3e3d79espUISzxbRqkXTcl06RqORmTNnKmrDdb+61Ot0GBWMLq0q8SfzeecH+w5OXInJIsuNQasKGw4WMHT46HLfV3plCSVppeyNigU3PEHu7e1DoVG5oL+qctFg4Vy+41sRRSYrQVUYZsvifIGZnckXWDGy/FNLarWaxx57zCnPK48+ffo4lHXI7XqymHbtOa7wEbOqYLZWbXV5LNPgtHMJG/Zf4F//ur/CPK2+vr688847Tnmes3A7kXXq3IUj2a624lpiG/vyWM8wh66RUpKcXkR0aPUT0lltku8P5PH0hEnVvld1MZlMmEwmu9u7ncg6dOjAX2eUicOqDgE+GiKDHIvQPXvBhN5T5ZScZj/sP0+Dho0r3brIzc3lxx9/rPbzKiIuLo7x48fb3d6tRJaamsqz48eSnVtIXpHF1eZUm8NphbR0gossM8/Eou3n+DhueaVtT506xbRp06r9zIpw9MidW0z8pZR8+mkcz01+lkEd/IgJ9+bgmQK6tfBztWmlbEu6QL7Byj0d7EuJDnDwTCHRYSVDZU6+mcPpRSSnF5FTYMZksWGySDzVAk+1ikAfDS1CvYgO8yLwip5PSslbP2Uy4dnJtG1bvivpMjWR0cfRyFuXiywjI4ORjwzhdHIC7z0UTrP6Xvh7Cdbvy3ErkZ3OLqbQaP/WSrHZxo8HcmgRqufb3ecwmm20CPMiOtSLxkE6PNUqNB4Cs1VistjIzDPx5R+ZJKcVodWoiA7zIraxL0IIijwCeXGafeWdfXx86N69e1U/pl1MmjTJoVRWLhXZiRMnuKNnd3pGqXh5ZGRp8pS+bQJY+H9nycwzEeIm0bGOri63/nUBX50H/4oNpmWYF6F+nnbFo0kpSc81kZRWxPakC2w/nEv//v1JSEgoLdhQETExMcydO9duO6uCSqVyqKKKy0R2+PBhet3WnUc6+/Dvf5xI8tJ60LdNAGvjzzGmVwMXWXg1t0U71qt+szuLZ/tH0N3B3lgIQZi/ljB/LXfE+HO+wMyG/Qe5r38v6jdoyJtvz6d3794O3dPVuGTif/r0aXrf3oNHu/peI7DL/LtTEOv2ZiuaW8IRmtX3oll9+7YiDqcVcr7AQtdm1c8RFuCjYXi3YFY/1ZRBzYsYMWQgjz86kosXL5bZPjs7m+Tk5Go/tyKmT5/OokWL7G5f4yLLy8vjjp7deaCDFwNuKn9TsXGQnqYhXnyj0IkfpZBSsnhrGoM7Bzk137+HSnB7K3+WPxZF9qEttG7ZvMw4+y1btjBjxgynPbcsLly4gMVi/+q/xkX27ISnaRts5cHOla/SptwVwbLfMkhR4MSPo2xKOG9Xrtgf9ueQU2DmwS7K5KLw0Xkw9a76TOlVhxFDBjJx/NNXTcJrIqOPWzvIN23axM8/ruPpXvZlg4kI1DGqRyivrj2l2Glse9l1/GKlYs/KM/HB5rO8fH+k4hkgb25ah2WPNua3jat5ZNhDpSkDvL29adiwoaLPfu211xyqL1BjIsvLy+PRkY/wQr8QvHX21eQBGNw5CJUQrFYot4S9WKzlp42CkmHy9fWnGdw5yO65W3Xx1auZ90A4pw9sZ8gDA7FarQwcOJDXXntN0eeGhIQ4lJO2xkQ2aeIz3NzIk85NHIs9V6kEL93XiM9+TedQqjLHy+zh9lb+NKtfvqP7i9+zOF9gZkT30Bq0quQ86Ov/bkBq0h88/ujISnN1uIIaEdnp06f57ts1jLu9aqeaIwJ1vHx/JM99cYzjma6J0Li9lX+5aUQ37Mtm9a4s5jzUtEYTJV9Gq1Hxxr8bsHv7Rt584w3y8/MVfd748ePZuXOn3e1rRGSLFn5Evzb+Dg2T/6RbCz8m9o1g4oqjHM1wn3izH/fnsOj/0nj34WZlVparKby0Hsy8pz5vvDabqVOnKvqsxMREh8512pPOM0II8YsQIkkI8ZcQwr6KBZcwGo18snQJ97evvouob9sAxvcNZ/znRzl4pmaqglzm54Pnycq7Orxl9a4sFm89ywcjmjscoaEEEYE6Hu0RzMYN3yuaJM9R/6g9PZkFmCylbAl0AcYJIVrZ+4A1a9YQFaSlUT3n/Ah9WgdcGjqPE7c9HYu1ZuYgq3dlkXFJZOcLzExbfZzv9pxj4agWbiGwyzzQOQgfUciC995V7Bnz5s2zy1l/GXvSeaZLKfde+v98SnL52+3r+WjBPO5r69wT4V2b1WXZmJYknClg9MdJNTJ8miw2PNWCLYfO8/DCRML8tXz2REvC/JWNp3cUlUow494GvPKfmaSmpiryjE6dOuHnZ//I5NCcTAgRCbQHdtnT3mg0Er//IF2bOr8ES0hdT+YPa8rgzsE8s/woH/+SRkGxckPETY18WPJLGh9vS2fOkKY83SccrcJ1lapKRKCOPjF+LF74katNARwre+MDrAEmSimvcZyVlcf/0KFDNAz2VaxOpBCCezrUY/mYlqTkFPOvdw/y5vrTTuvZrDbJziN5TFp5lE0J52kR6sXyMS3dMuPQP/lX+7osWbLIoTBpexk7diyZmZl2t7crCkMIoaFEYCullN+W1UZKuQRYAiVV4gDi4+NpUV/5+UpwXU9eGRRFdr6ZdXuzmbTyGKF+ntx1UyAxDbxpHKS3e2uhyGjlSIaBfafy+WZ3FiF1PPl3pyBef6CJ4hXhnEnjYD2NAjxZu3at06v/rl+/nhdffNHu9pWKTJQEQX0CJEkp5zlizJ+/76RZvZr7Yer5ahh9WyjDu9Vnx5Fctibm8sV/M8m8aKZpiJ7oUC8ig3ToNCq0ahU2WTLXyi+2cjSjiMPpRaTnmogK0nG+0MyDNwcz/NLm6q+Hc7m5SR23HSLL4v523rw//22ni8zR1aU9PdmtwCPAQSHE/kuvTZNSVnpaIX7PLsZ2rhkXy5WoPQQ9W/rTs2VJztlCo5Uj6SUiOp5pwGiRGC02VAK0ahVeWg9uauTLkFtCiArWk1NgZsTiJIZ2/fsA66trT/H1+NbXlchui/Zj7qYkzpw5Q0REhNPuO3fuXIfcSpWKTEq5A6jSNnZ29nnq+bq+Moa31oP2kb60j/S1q33i2UJiGnhfNcRW9dylK1F7CGIa1mXPnj1OFZkjWRZB4R3/YpPJLZOnVMbh9KLSAyCXuamRj0OJid2FZvUEGzf+6FKfpqLfmtlscYkvr7okpxXR4h8Hcuc/3Oy6/CzR9XV89/UXnDhxgqSkJIcOgJSF1Wrl+eefd+gaRUXmqdFgrqEdeWchpSyzJ7teiQ71QgoPGjduzJNPPsm2bds4evSoQ5GtV2I0Gnn//fcdukbRgyR6nSdGJyeAU5oLhRZsUl6VJMVilRzLNFyXwguqo8FiNpOTk8O2bdsAGDlyJD169OCOO+4gJCTEoayMVal2omhPFhpav9Tf52rsnZMYTDa8tR5XHV8rKLYy4fMjSpmmKEIIfPSeFBQUIIRACMGyZcsYNWoUS5cu5Y033iAjI4O8PPtSQ2g0Gl544QWHbFBUZLGdb1Esj72jTPniOLuOXaxUbCaL7ZrFiqmSqFh3R+upvqZsoEql4tVXX2XWrFmsW7eOCRMmkJubW+lOvre3t0MbsaCwyDrdfAvJbpKhZ1K/CFqEevHWhhR+2J9TbrsyJSghItC9HOGOUt4fl0ql4oknniAuLo6dO3cybNgwzGazQzlhK0NRkXXs2JFkBavYOkKDAC1+3moe7xlGh0gf4ran89mv6de006pV12R6DK7rycJRLWrKVKdjNFkqnXepVCruvvtufv75Zw4dOsSAAQOwWCwcO3bsqnY5OTmsWrXKoecrKrLo6GjO5RWRb3CfDD2BvhpC/bQM7hxEz5b+/LA/hzkb/k7TqdOoKDK6tiqusykymissLnElKpWK9u3bk5CQQFZWFr179yY3N5fjx48DkJKS4nDiY0VFplarua37rWw/rGyBz6rgq1cTGaTjjlb+3N+xHvEn85m88hj+3h5IWZKF5zJmi42LbvSH4gjZ+WZAZXdR+suoVCrCwsI4fvw4Op2Ofv36cfjwYU6ePOleq0uApydM5rsD7jH5Lwudp4rmoV60a+jD6NtCSc81Y5XyqvDuv84WMXnVsQru4j5IKa/aNjp0poBmTaPYvXs3CQkJpa+fPn2aTz75hA8++OCqk+g//fQTY8aMYfjw4WzatAkPDw90Oh2DBw9m6NChDBs2rMJ0omWheMKVvn37MtaiJvFsIa0auG8cltpDEBPujc0m6da8LklphSzfmcGbDzS5dObS8d1+KSVWG6WegiKjlcw8E0aLDR+dmvCAkh7hRJaBxLOFGC2SNuHeNL/kbdh4IIdDqYUYzTaG3BJCk5CSedUra09xIsuA0WzjgxHNSzM59np9HwaTDT9vNRufawfAvpRCTqTkMW7cOGJjY1m4cCEA586dY+fOnej1+qt6ucDAQNq3b49erycyMrL09ZEjRzJw4EC0Wu1Vr9v13Tr8zTmIh4cHT42bwHdr3ndrkV1GpRL0iPZn44Fsxt8ZQYCPmkmrzlBktDH3xxSMFsm43g1Kq9hN+PwoBcVWVAI+fiwagLQLRoZ+lIjJYiOmgXfp63+dLeSdH8+gVZfktRjVoySMKPW8kb2n8vFUq2h0xSq2rpeayHo6tBoVvvq/T3oN7hyM1VaSQK+O/u+f8Mcp7fBUC1RX5ODIKPRg4cIPGDRo0FWfMzY2lri4uGs+f6dOnejUqdM1rzdv3tzh7/IyQgnHaWxsrLyyEHt2djZNGzdi5Zim1HNSunElSbtg5LGlh1k/uS0eKkHS2UKOZBRRbC7ZQ+vTJgBvbcmPvvdUPhoPgU6jKj05brNJis02PNUql/o7rTbJve8ls2f/IYd7H0cRQsRLKctMaFsj+cnq1avHuGee4e31y3hzYJjDxUlrmjB/LfX9PPnv0Ty6t/CjZQNvWpbTC3coI3xIpRJ4aat+xtRZ7DySR1RUlOICq4wa28aeOWs22WYffkpwfrlkJbi3Qz1W7rQ/jt0dWZtQyDPPPudqM2pOZJ6enixb+SUfbM26tKx2b07mWDmSZVSkUH1NkJJTTHJ6EYMHD3a1KTWbOqpjx46MGfs0c37KrLCGtqvZfzqfX5KLGDVqNGv3uV9NAXtYuy+XUaMfRVdBGemaosa9vjNnzcbs3YD3tmS5ZQaaE1kGpn+Xxmefr2TSlOf5MeECmW4SSWIvmXkmNibkMnbcM642BXCByDw9Pflx0/+RlOfNku3n3EpoZ3KKmfTVGea//xH9+/cnKiqKic9O5q2fMt3Kzoq4nPf/2clTaNy4savNAVyUmNjPz4/NW39ld6aOBVvcY+g8mlHE0ytTmP36Ozz88N8HJV58aTqFHgGs33fehdbZz/p95ynyCGTqiy+52pRSXBYkFRwczK87d3HCGMTM79NcWuZmW9IFnv3yDO9+uITHn3jiqvc0Gg3LV37Fou3n3H7YvFweZ/mqr8otuuoKXBqJ5+/vz9btO4nu9i+GLz3BrzXsSM8ttDDz+zSW/mnm+x828dBDD5XZrm3btkyYOJlXNmS4bTi50cUgak4AAAWMSURBVGxj9vp0Jk6aQps2bVxtzlW4PNzTy8uLBR98xOrvNrDwv8X8Z13N9Grbki4wfOkJWnYfSMJfyXTt2rXC9i+9PIOGrW5m1rq0GktXZS8Wq2Tm92k0btOVaS+97GpzrsHlIrtMjx49OJiYTJMu9zF08TEWbs0kPdfo1GdYrJKtiRd45otUPt5l5tv1G5n/3vt2HaTw8PBg1VffoA6KZvb6msuLVhlmi43/rEvDM6QlK7/8Gg8P13sa/onbiAxK4sff/3AhO3fFU7ftfYyOO8UL36Tx+9G8ai0Ozl008cmvWQz88CgbTvkxada7JCYf49Zbb3XoPlqtlu83bERTvzVT15yl2OTaobPYZOPFb9PQNWjH9xs2Kp6/v6rUiIO8qhQWFvLll1+yYP7bnEk5Q6uGfjQLFLSoryU6zIv6da8tilVssnE0s4iktCKOZttIzjCScaGIIUMeYtwzE50yXzGbzTw2ajg7ftnEtP7BtI7wqfY9HeXQmQJe+zGTHnfcxcdxn7l8ol+Rg9ytRXYlaWlpxMfHs2f3bv78/Vf27ksg92I+Wk81Wo0aq82G0WTBapO0bB5FbKcudOrSldjYWFq3bq3IX/nXX3/NuKeeoF+MD491D6qRZCzFZhtLfz3Hz4mFfLhoyTUhPK7ihhBZWRQXF1NcXIzBYECj0aDT6dDr9TU6L8nKyuKpJx5l/+6dTO4TRIdIH0WiTKSU7D1VwDs/Z9GxSw8+WryUoKCqpaxXghtWZO7E119/zbTnJ6OyFnF/W2/6tv075qw6FBqtbEo4z9qEQqTam9fnzHWb3utKakVWQ0gp2bp1K++/O5dt27bRp3UA3ZuVVOu9HElrD3lFFg6nF7HjWBE/H7xAz563Mf7ZKdx+++1uG4tXbZEJIfoB7wEewFIp5ZsVtf9fFdmVpKam8vGSRfzfpo0cOJSIn4+W6DAfmgVCcB0NWs2l8tCXEvJlXTRzNAcOpxWQW2CkXetW3NG3P48/8STh4eGu/jiVUi2RCSE8gCNAHyAV2A0MkVImlndNrciuxmazceTIEeLj49n95x9kpKVSbCjCWGxEq9Oi03tRPyycTp27EBsbS7NmzRwqv+wOVDf8ujNwTEp54tLNvgTuA8oVWS1Xo1KpiI6OJjo6mmHDhrnanBrHnj+XBsCZK/6digPFImqpxZ6erKyZ5jVjrBDiCeByCINRCHGoOoYpSD3ATdLAlMn1al+j8i6wR2SpwJVZbcOBtH82ujKPvxBiT3njs6txZ9vgxrTPnuFyN9BMCNFYCOEJPASsq4qBtfxvYk+KdYsQ4mlgEyVbGHFSyr8Ut6yWGwa7dggvFYaotDjEFSypmjk1gjvbBjegfYrs+NdSy5VcXzt+tVyXOFVkQoh+QohkIcQxIYSyhbAdpLplrmsCIYSHEGKfEGKDq235J0IIPyHEN0KIw5e+w1vsvtZZw2VV3E81iRAiFAiVUu4VQvgC8cD97mIfgBBiEhAL1JFSDnC1PVcihFgG/CalXHppl8FLSmnXyR9n9mSl7icppQm47H5yC6pb5lpphBDhwN3AUlfb8k+EEHWAHpSUpERKabJXYOBckV037idHy1zXEO8CzwPueOYuCjgHfHppOF8qhLA7o6EzRWaX+8nVVFbm2hUIIQYAWVLKeFfbUg5qoAOwUErZHigE7J5zO1NkdrmfXIk9Za5dxK3AvUKIU5RMM3oJIVa41qSrSAVSpZSXe/5vKBGdXThTZG7tfqpOmWulkVK+KKUMl1JGUvK9bZVSPuxis0qRUmYAZ4QQlytm3IEDoV5OS+d5HbifqlzmuhYAngFWXupATgCj7L2wdse/FsWp3fGvRXFqRVaL4tSKrBbFqRVZLYpTK7JaFKdWZLUoTq3IalGcWpHVojj/D6HRh3IIH0agAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cu_cubic = bulk('Cu', 'fcc', a=3.6, cubic=True)\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(Cu_cubic,rotation='10x,20y,2z')\n",
    "#view(Cu_cubic, viewer='nglview')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building surfaces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many functions in ASE to set up common surfaces, adding vacuum layers and adsorbates at specific locations on the surface. [General crystal structures and surfaces](https://wiki.fysik.dtu.dk/ase/ase/lattice.html#general-crystal-section) can be used to set up these surfaces. There are also many utility functions that make this task easier. Let's take a look at a few examples from ASE's documentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f879984ee10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Setting up Al(111) surface slab with 10 Ang vacuum on either side \n",
    "from ase.build import fcc111\n",
    "slab = fcc111('Al', size=(2,2,3), vacuum=10.0)\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(slab,rotation='10x,20y,1z')\n",
    "#view(slab, viewer='nglview')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code block sets up an Al(111) surface with a vacuum of 10 Angstrom on each side in the z-direction. The size of the slab is 2x2x3 which means that the minimum possible size is repeated in twice in x and y-directions and thrice in the z-direction. Now if we want to adsorb a hydrogen atom in an on-top position 2 Angstrom above the top layer, we can use the method `add_adsorbate` in ase.build:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f879aa51050>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ase.build import add_adsorbate\n",
    "slab = fcc111('Al', size=(2,2,3))\n",
    "add_adsorbate(slab, 'H', 1.5, 'ontop')\n",
    "slab.center(vacuum=10.0, axis=2)\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(slab,rotation='-90x,20y,2z')\n",
    "#view(slab, viewer='nglview')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The vacuum should only be added after adding the adsorbate to the slab surface. ASE also sets tags to the layer number, for example adsorbates will have tag=0, first layer will have tag=1 and so on. To learn more about how to use ASE's utility functions, go through [Surfaces](https://wiki.fysik.dtu.dk/ase/ase/build/surface.html) where all the information has been summarised. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up constraints "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When doing quantum mechanical calculations like geometry optimization or dynamics, we may have to fix some degrees of freedom in a system. ASE has constraint object(s) that can be attached directly to atoms object. When we set constraints to atoms object, it will freeze the corresponding atom positions. This can be done by using **atoms_obj.set_constraint()** where **atoms_obj** is an ase atoms object. An example of this is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ase.constraints import FixAtoms\n",
    "\n",
    "c = FixAtoms(mask=[atom.symbol == 'Cu' for atom in Cu_cubic])\n",
    "\n",
    "fig, axs = plt.subplots(figsize=[5,2])\n",
    "plot_atoms(Cu_cubic,rotation='10x,20y,2z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The constraints won't show up if we plot the atoms using **plot_atoms()**. You can use **view(Cu_cubic)** to verify if the constraints have been put on the atoms. There are different classes of constraints in ASE similar to FixAtoms for example: FixBongLengths, FixedLine, FixedPlane etc. To learn more about ASE constraints, go through [Constraints](https://wiki.fysik.dtu.dk/ase/ase/constraints.html). This information will come handy while performing geometry optimization of slabs with adsorbed species. In these calculations, we fix the bottom two layers of slab and relax the top layers and adsorbate while doing a DFT calculation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "ASE is an excellent tool which can be used to build structures and set up atomistic total energy calculations and molecular dynamics simulations. It can be used via graphical user interface, command line and python. Covering all the different utility functions in the ASE package is beyond the scope of this lesson. For more information or help with the utility functions, read through the [ASE documentation](https://wiki.fysik.dtu.dk/ase/ase/ase.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}