{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph neural network - CaMML course"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CC-BY, blog by **[Timothée Jamin](jamint@bio.aau.dk)** and **[Ahmed Ismail](uccaais@ucl.ac.uk)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: How Graphs Are Helping Us Understand Materials Better\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Materials science is all about understanding how atoms come together to form solids—and how these atomic arrangements give rise to useful properties like conductivity, magnetism, or chemical stability.\n",
    "\n",
    "Thanks to machine learning, we now have powerful new tools to help answer that. One of the most exciting is called a Graph Neural Network (GNN).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Comparison between GNN and Traditional](./pictures/Comparison.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike traditional models that flatten this information into a vector (losing spatial relationships), GNNs preserve the structure and learn by passing messages between atoms. That means they can understand the material in a much deeper way—straight from its geometry and composition.\n",
    "\n",
    "In this blog, we’ll explore how graph theory, GNNs, and convolutions are transforming how we model materials—and why this matters for science and technology."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 📖 Graph Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply GNNs to materials, we first need to understand the graph theory that underpins the method. Graph theory provides the mathematical foundation for representing atomic structures.\n",
    "\n",
    "In Materials science, a graph $G = (V,E)$, where $V$ is the set of nodes (or vertices) and $E$ the set of edges can be used to store information about the structure as follows:\n",
    "\n",
    "Nodes (Vertices): Each atom in the material becomes a node in the graph. Node features may include atomic number, valence electrons, or electronegativity.\n",
    "\n",
    "Edges (Links): Represent the connections between atoms—these can be direct chemical bonds or neighbors within a cutoff distance (e.g., $3.0$). Edge features may include bond length, bond type, or even directionality.\n",
    "\n",
    "Graph: A complete material structure can then be modeled as a graph, where the spatial arrangement and types of atoms are preserved.\n",
    "\n",
    "Adjacency Matrix: $A$ matrix $A$ of size $N \\times N$ (where N is the number of atoms):\n",
    "\n",
    "- $𝐴_{𝑖𝑗}$ = 1 if atoms $𝑖$ and $𝑗$ are connected within the cutoff,\n",
    "- $𝐴_{𝑖𝑗}$ = 0 otherwise.\n",
    "\n",
    "When we use convolution (filter) on these graphs, like in a Crystal Graph Convolutional Neural Networks (CGCNN), we are trying to help the model learn how each atom is influenced by its neighbors.\n",
    "\n",
    "CGCNN applies the convolution operation to capture both local and global features from the crystal graph, helping predict properties like formation energy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![GNN_Convolution](./pictures/GNN.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea is simple: each atom gathers information from the atoms it's connected to. This is great because it helps the model understand how atoms interact locally. But if we do this gathering (this convolution) too many times, something happens: the atom starts getting information from too many other atoms, even ones far away. It’s like trying to understand your neighborhood by asking friends, then your friends’ friends, and so on. After a while, the information gets so mixed up that you lose the details about your own block.\n",
    "\n",
    "This is what we mean when we say you lose the specificity of location. The atom’s original, unique position and role in the structure gets blurred.\n",
    "\n",
    "So, while convolution helps capture patterns and relationships in a graph, doing it too much can make all atoms start to look the same. In materials science, that’s a problem because tiny local differences can mean very different material properties.\n",
    "\n",
    "In short: Graph convolution helps atoms learn from their neighbors—but too much of it, and you lose what makes each atom special."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🧪 Visualizing Crystal Structures as Graphs for GNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example shows how to represent the atomic structure of bismuth ferrite (BiFeO₃) from a CIF file and converts it into a graph, where:\n",
    "\n",
    "Nodes represent atoms (Bi, Fe, and O),\n",
    "\n",
    "Edges represent atomic neighbors within a cutoff distance (4 Å),\n",
    "\n",
    "We use pymatgen and ASE to parse and process the structure, and NetworkX and Matplotlib to draw the resulting graph. This graph preserves the local connectivity and atomic identities of the structure—exactly the kind of representation a GNN model like CGCNN would consume for property prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_485448/3832322004.py:11: FutureWarning: get_structures is deprecated; use parse_structures in pymatgen.io.cif instead.\n",
      "The only difference is that primitive defaults to False in the new parse_structures method.So parse_structures(primitive=True) is equivalent to the old behavior of get_structures().\n",
      "  structure = parser.get_structures()[0]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from pymatgen.io.cif import CifParser\n",
    "from pymatgen.io.ase import AseAtomsAdaptor\n",
    "from ase.neighborlist import neighbor_list\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Load BiFeO3 structure from CIF file\n",
    "parser = CifParser(\"inputs/BiFeO3.cif\")\n",
    "structure = parser.get_structures()[0]\n",
    "\n",
    "# Convert to ASE atoms object\n",
    "atoms = AseAtomsAdaptor.get_atoms(structure)\n",
    "\n",
    "# Define cutoff radius for neighbors\n",
    "cutoff_radius = 4.0  # in Angstrom\n",
    "\n",
    "# Generate edge list using ASE neighbor list\n",
    "edge_src, edge_dst, edge_len = neighbor_list(\"ijd\", atoms, cutoff=cutoff_radius, self_interaction=False)\n",
    "\n",
    "color_map = {\"Bi\": \"orange\", \"Fe\": \"red\", \"O\": \"lightblue\"}\n",
    "\n",
    "G = nx.Graph()\n",
    "for i, site in enumerate(structure.sites):\n",
    "    G.add_node(i, element=site.species_string) \n",
    "\n",
    "for s1, s2 in zip(edge_src, edge_dst):\n",
    "    G.add_edge(s1, s2)\n",
    "\n",
    "labels = {i: G.nodes[i][\"element\"] for i in G.nodes}\n",
    "colors = [color_map[G.nodes[i][\"element\"]] for i in G.nodes]\n",
    "\n",
    "\n",
    "pos = nx.spring_layout(G, seed=42)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 8), facecolor='white') \n",
    "nx.draw_networkx_nodes(G, pos, node_color=colors, edgecolors='black', node_size=1000)\n",
    "nx.draw_networkx_edges(G, pos, width=2)\n",
    "nx.draw_networkx_labels(G, pos, labels, font_size=12, font_weight=\"bold\")\n",
    "plt.title(\"Graph Representation of BiFeO₃\", fontsize=14)\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this blog, we explore how GNNs can predict the formation energy of materials—a key thermodynamic property that tells us how stable a structure is. Using data from the [Material Project](https://legacy.materialsproject.org/), we show how to convert real crystal structures into graphs, and how to train a GNN to predict energy from structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 💻 How to program and code a GNN?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to this approach we can transform the theory into an useable algorithm.\n",
    "\n",
    "To do so, we will take the time to build piece by piece our own GNN in this section. \n",
    "\n",
    "First of all, we need to have access to a sufficient amount of data to train our GNN. Here, the __[Material Project](https://legacy.materialsproject.org/)__ database will be used. Although we can train our GNN upon any property, we will only focus on the prediction of the formation energy of the solid using its structure and its composition.\n",
    "\n",
    "The extraction and handling of the data will be similar to what is shown in the [matminer tutorial](https://github.com/hackingmaterials/matminer_examples/blob/main/matminer_examples/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we will take some libraries to construct the neural-networks using torch.\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "\n",
    "# Here are some diverse modules and functions necessary.\n",
    "import random\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ASE and pymatgen modules\n",
    "from ase import Atoms\n",
    "from ase.neighborlist import neighbor_list\n",
    "from pymatgen.io.ase import AseAtomsAdaptor\n",
    "\n",
    "# matminer\n",
    "# Be mindful that the MPDataRetrieval was here only tested for the old MP database!\n",
    "\n",
    "from matminer.data_retrieval.retrieve_MP import MPDataRetrieval\n",
    "\n",
    "# Used for storing the datas. (Uses the method of Alex Ganose [a.ganose@imperial.ac.uk] for handling the datas)\n",
    "import functools\n",
    "from dataclasses import dataclass\n",
    "from torch.utils.data import Dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 💾 Data aquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To ensure a reproducible results, we put the same random seed.\n",
    "SEED = 42\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lorentyfle/test/.venv/lib/python3.10/site-packages/pymatgen/ext/matproj.py:419: FutureWarning: You are using the legacy MPRester. This version of the MPRester will no longer be updated. To access the latest data with the new MPRester, obtain a new API key from https://materialsproject.org/api and consult the docs at https://docs.materialsproject.org/ for more information.\n",
      "  return _MPResterLegacy(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "mpdr = MPDataRetrieval(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 126335/126335 [21:46<00:00, 96.66it/s] \n"
     ]
    }
   ],
   "source": [
    "# Can be long (~10min to grab all the necesary 126335 datas) if the material project legacy is here used.\n",
    "df = mpdr.get_dataframe(criteria={}, properties=['formation_energy_per_atom', 'pretty_formula',\"structure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the database is ready, with only three columns needed to train our GNN upon:\n",
    " - The composition\n",
    " - The structure\n",
    " - The formation energy (the property we wish to predict)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formation_energy_per_atom</th>\n",
       "      <th>pretty_formula</th>\n",
       "      <th>structure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>material_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mp-1001788</th>\n",
       "      <td>-0.020860</td>\n",
       "      <td>ZrB6</td>\n",
       "      <td>[[0. 0. 0.] Zr, [0.80207057 2.029387   2.02938...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp-1002206</th>\n",
       "      <td>0.537123</td>\n",
       "      <td>SiC</td>\n",
       "      <td>[[0. 0. 0.] Si, [2.0251 2.0251 2.0251] C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp-1004524</th>\n",
       "      <td>0.234287</td>\n",
       "      <td>HPbI3</td>\n",
       "      <td>[[3.1221065 3.1221065 3.1221065] H, [0. 0. 0.]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp-1008492</th>\n",
       "      <td>-0.349239</td>\n",
       "      <td>BrCl</td>\n",
       "      <td>[[0. 0. 0.] Br, [0.        0.        3.9053505...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp-1011260</th>\n",
       "      <td>-2.517135</td>\n",
       "      <td>EuFeO3</td>\n",
       "      <td>[[ 2.747224    2.72097164 -1.9501425 ] Eu, [5....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvc-5634</th>\n",
       "      <td>-2.022086</td>\n",
       "      <td>Ca2CuIrO6</td>\n",
       "      <td>[[3.15778709 8.03614231 5.77105139] Ca, [0.340...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvc-6019</th>\n",
       "      <td>-1.619846</td>\n",
       "      <td>Zn(SnO2)2</td>\n",
       "      <td>[[ 0.         11.36829898  1.84505467] Zn, [0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvc-8011</th>\n",
       "      <td>-1.584384</td>\n",
       "      <td>ZnFeAs2O7</td>\n",
       "      <td>[[0.71270026 8.84632777 7.89206318] Zn, [5.112...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvc-8692</th>\n",
       "      <td>-2.438346</td>\n",
       "      <td>CaSb2(PO5)2</td>\n",
       "      <td>[[0.0143334 3.136694  4.121839 ] Ca, [5.866293...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvc-9599</th>\n",
       "      <td>-1.458839</td>\n",
       "      <td>Ca(CuO2)2</td>\n",
       "      <td>[[0.30846523 0.         0.10485072] Ca, [9.270...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126335 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             formation_energy_per_atom pretty_formula  \\\n",
       "material_id                                             \n",
       "mp-1001788                   -0.020860           ZrB6   \n",
       "mp-1002206                    0.537123            SiC   \n",
       "mp-1004524                    0.234287          HPbI3   \n",
       "mp-1008492                   -0.349239           BrCl   \n",
       "mp-1011260                   -2.517135         EuFeO3   \n",
       "...                                ...            ...   \n",
       "mvc-5634                     -2.022086      Ca2CuIrO6   \n",
       "mvc-6019                     -1.619846      Zn(SnO2)2   \n",
       "mvc-8011                     -1.584384      ZnFeAs2O7   \n",
       "mvc-8692                     -2.438346    CaSb2(PO5)2   \n",
       "mvc-9599                     -1.458839      Ca(CuO2)2   \n",
       "\n",
       "                                                     structure  \n",
       "material_id                                                     \n",
       "mp-1001788   [[0. 0. 0.] Zr, [0.80207057 2.029387   2.02938...  \n",
       "mp-1002206           [[0. 0. 0.] Si, [2.0251 2.0251 2.0251] C]  \n",
       "mp-1004524   [[3.1221065 3.1221065 3.1221065] H, [0. 0. 0.]...  \n",
       "mp-1008492   [[0. 0. 0.] Br, [0.        0.        3.9053505...  \n",
       "mp-1011260   [[ 2.747224    2.72097164 -1.9501425 ] Eu, [5....  \n",
       "...                                                        ...  \n",
       "mvc-5634     [[3.15778709 8.03614231 5.77105139] Ca, [0.340...  \n",
       "mvc-6019     [[ 0.         11.36829898  1.84505467] Zn, [0....  \n",
       "mvc-8011     [[0.71270026 8.84632777 7.89206318] Zn, [5.112...  \n",
       "mvc-8692     [[0.0143334 3.136694  4.121839 ] Ca, [5.866293...  \n",
       "mvc-9599     [[0.30846523 0.         0.10485072] Ca, [9.270...  \n",
       "\n",
       "[126335 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we then look at the distribution of the *formation energy per atom* as a function of the database, we have the histogram below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, '$\\\\mathrm{E}_\\\\mathrm{f}/\\\\mathrm{atom}$'),\n",
       " Text(0, 0.5, 'Number of examples')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(df[\"formation_energy_per_atom\"], bins=100)\n",
    "ax.set(xlabel=\"$\\mathrm{E}_\\mathrm{f}/\\mathrm{atom}$\", ylabel=\"Number of examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔨 Transforming the data into a graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Transforming a structure into a graph.\n",
    "\n",
    "At last we have a curated database as a function of the target property, the formation energy.\n",
    "\n",
    "Yet, we still lack a graph representation of the structure using nodes and edges. To do so, we can say that each atoms of each structures are a **node** and, their neigbhor the **edge**.\n",
    "\n",
    "    Encoding information.\n",
    "\n",
    "As the edge and the node are defined, one last step still remains before the data is ready. We need to encode some information inside the node and the edge to allow the machine learning to know the difference in between a Li next to an O and a I next to a Fe.\n",
    "\n",
    "\n",
    "    Where can we encode the information?\n",
    "\n",
    "Saving information inside the node seems very intuitive because we simply say that one node is one atom, such that the information of the atom type seems obvious.\n",
    "\n",
    "As for the interactions, we can store this information inside the edges of the node; this is the **edge embedding**.\n",
    "\n",
    "The simplest edge embedding would be the atomic connection; yet, we can also store further information like the bond length, the bond angle, etc...\n",
    "\n",
    "Here we will be modest and only keep the atomic connection and the bond length as edge features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Node: Encoding the nature of the atom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To encode the nature of the atom, one simple way would be to make an array of 118 entries (for each elements), then put a 1 where this atom is inside the periodic table of elements and all the other entries are zero.\n",
    "\n",
    "This method called *one hot encoding* is here useful because it will reduce drastically the memory needed to identify the database while being really easy to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode_z(atomic_number:int):\n",
    "    \"\"\"We return a onehot encoding of the periodic table of elements, inputting the Z number gives the good onehot encoding for the atom in question.\"\"\"\n",
    "    vector = np.zeros(118)\n",
    "    vector[atomic_number-1] = 1\n",
    "    return vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1., dtype=torch.float64)\n",
      "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "node_features = torch.tensor(np.array([one_hot_encode_z(site.species.elements[0].Z) for site in df[\"structure\"].iloc[0]]))\n",
    "# Here our atom is Zr with Z=40, so we look at the 39th place to find it back.\n",
    "print(node_features[0][39])\n",
    "print(node_features[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Edge: Keeping track of the atom connections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keeping track of the atom connection is not as hard as one may think. Indeed, we can simply use a prebuilt function inside [`ase`](https://wiki.fysik.dtu.dk/ase/) to output the list of neighboring connections and their length given a cutoff radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_radius = 4 # Angstrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cell([4.058774, 4.058774, 4.058774])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atoms = AseAtomsAdaptor.get_atoms(df[\"structure\"].iloc[0])\n",
    "edge_src, edge_dst, edge_len = neighbor_list(\n",
    "                \"ijd\", atoms, cutoff=cutoff_radius, self_interaction=False\n",
    "            )\n",
    "# Cell parameter of atoms\n",
    "atoms.cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three variables show the origin of the bond, its destination and the length.\n",
    "In fact, it becomes evident that for such a crystal structure with cell parameters almost equal to $4$ Å, each of the atoms will be connected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple approach to embbed the distances without having to create our own convolution would be to describe the edge feature as a weight on the node convolution.\n",
    "\n",
    "One can take one node (black) connected to two other nodes (red and blue). \n",
    "\n",
    "![Edge_embedding](./pictures/edge_embedding.png)![Edge_embedding_wrong_weight](./pictures/edge_embedding_no_weight.png)\n",
    "\n",
    "If no weight was involved then both of the red and blue nodes will have an equal involvment on the central node, which make this approximation inadequate.\n",
    "\n",
    "![Edge_embedding_wrong_weight](./pictures/edge_embedding_with_weight.png)\n",
    "\n",
    "If the convolution effect is decreased with the distance then it would reduce the amount of information given by far away atoms, which will add a small spatial contribution.\n",
    "\n",
    "\n",
    "Thanks to a value of the distance that cannot excess a cutoff radius, then a normalisation of the distance followed by an inversion would be enough to create the weigths.\n",
    "\n",
    "$$w_i = 1-\\frac{d}{r_c}$$\n",
    "\n",
    "Where $w_i$ is the weight of the edge, $d$ is its length and $r_c$ is the cutoff radius of the edge (no edge can have a distance superior to this cutoff)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🧳 Storing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we did all of this, we can try to store all this information inside a Data class that will possess all the information of our database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Classes of datas (these classes show how the data will be stored).\n",
    "@dataclass\n",
    "class Data:\n",
    "    \"\"\"\n",
    "    Class to contain graph attributes.\n",
    "\n",
    "    N and M are the number of nodes and edges in the graph, respectively.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : Tensor\n",
    "        The node features as a (N, n_node_feats) Tensor.\n",
    "    edge_len : Tensor\n",
    "        The edge length as a (M, ) Tensor.\n",
    "    edge_index:\n",
    "        [0] The index of the central node for each edge.\n",
    "        [1] The index of the destination node for each edge.\n",
    "    y : Tensor\n",
    "        The target property to learn.\n",
    "    atoms : Atoms\n",
    "        An ase atoms object.\n",
    "    \"\"\"\n",
    "\n",
    "    x: torch.Tensor\n",
    "    y: torch.Tensor\n",
    "    edge_len: torch.Tensor\n",
    "    edge_index: torch.LongTensor\n",
    "    atoms: Atoms\n",
    "\n",
    "@dataclass\n",
    "class Batch:\n",
    "    \"\"\"\n",
    "    Class to contain batched graph attributes.\n",
    "\n",
    "    N and M are the number of nodes and edges across all batched graphs,\n",
    "    respectively.\n",
    "\n",
    "    G is the number of graphs in the batch.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    node_feat : Tensor\n",
    "        The node features as a (N, n_node_feats) Tensor.\n",
    "    edge_len : Tensor\n",
    "        The edge length as a (M, ) Tensor\n",
    "    edge_src : LongTensor\n",
    "        The index of the central node for each edge.\n",
    "    edge_dst : LongTensor\n",
    "        The index of the destination node for each edge.\n",
    "    edge_index:\n",
    "        [0] The index of the central node for each edge.\n",
    "        [1] The index of the destination node for each edge.\n",
    "    y : Tensor\n",
    "        The target property to learn, as a (G, 1) Tensor.\n",
    "    batch : LongTensor\n",
    "        The graph to which each node belongs, as a (N, ) Tensor.\n",
    "    \"\"\"\n",
    "\n",
    "    x: torch.Tensor\n",
    "    y: torch.Tensor\n",
    "    edge_len: torch.Tensor\n",
    "    edge_src: torch.LongTensor\n",
    "    edge_dst: torch.LongTensor\n",
    "    edge_index: torch.LongTensor\n",
    "    batch: torch.LongTensor\n",
    "\n",
    "    def to(self, device, non_blocking=False):\n",
    "        for k, v in self.__dict__.items():\n",
    "            self.__dict__[k] = v.to(device=device, non_blocking=non_blocking)\n",
    "\n",
    "# First we take the data and store it inside a MaterialsDataSet (function created by Alex Ganose [a.ganose@imperial.ac.uk], adapted for this application.)\n",
    "\n",
    "class MaterialsDataset_MP(Dataset):\n",
    "    def __init__(self, df:pd.DataFrame, target_column:str, cutoff:float=4):\n",
    "        \"\"\"\n",
    "        A dataset of materials properties extracted from Material Project.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        df : pd.DataFrame\n",
    "            DataFrame from Material Project.\n",
    "        target_column : str\n",
    "            Column of df used as the target. \n",
    "        cutoff : float\n",
    "            The cutoff radius for searching for neighbors.\n",
    "        \"\"\"\n",
    "\n",
    "        self.dataframe      = df\n",
    "        self.data           = {i: {} for i in range(df.shape[0])}\n",
    "        self.target_column  = target_column\n",
    "        self.cutoff         = cutoff\n",
    "        #\n",
    "        for entry in range(self.dataframe.shape[0]):\n",
    "            atoms = AseAtomsAdaptor.get_atoms(self.dataframe[\"structure\"].iloc[entry])\n",
    "            edge_src, edge_dst, edge_len = neighbor_list(\n",
    "                \"ijd\", atoms, cutoff=self.cutoff, self_interaction=False\n",
    "            )\n",
    "            self.data[entry].update(\n",
    "                {\n",
    "                    \"atoms\": atoms,\n",
    "                    \"edge_src\": edge_src,\n",
    "                    \"edge_dst\": edge_dst,\n",
    "                    \"edge_len\": edge_len,\n",
    "                }\n",
    "            )\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    @functools.lru_cache(maxsize=None)\n",
    "    def __getitem__(self, idx):\n",
    "        entry = self.data[idx]\n",
    "\n",
    "        # one hot encode element type\n",
    "        node_feat = torch.Tensor([one_hot_encode_z(el) for el in entry[\"atoms\"].get_atomic_numbers()])\n",
    "\n",
    "        # Setup edge_distance to weight the edges.\n",
    "        edge_distance=torch.tensor(1.0 - np.array(entry[\"edge_len\"])/self.cutoff,dtype=torch.float32) # \n",
    "\n",
    "        return Data(\n",
    "            x=torch.Tensor(node_feat),\n",
    "            y=torch.Tensor([ self.dataframe[self.target_column].iloc[idx] ]),\n",
    "            edge_len=torch.Tensor(edge_distance),\n",
    "            edge_index=torch.Tensor(np.array([entry[\"edge_src\"],entry[\"edge_dst\"]])),\n",
    "            atoms=entry[\"atoms\"],\n",
    "        )\n",
    "\n",
    "\n",
    "# Then we use the collate_fn for creating the batches (function created by Alex Ganose [a.ganose@imperial.ac.uk], adapted for this application.)\n",
    "def collate_fn(dataset):\n",
    "    \"\"\"\n",
    "    Collate a list of Data objects and return a Batch.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    dataset : MaterialsDataset_MP\n",
    "        The dataset to batch.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    Batch\n",
    "        A batched dataset.\n",
    "    \"\"\"\n",
    "    batch = Batch([], [], [], [], [], [], [])\n",
    "    base_idx = 0\n",
    "    for i, data in enumerate(dataset):\n",
    "        batch.x.append(data.x)\n",
    "        batch.y.append(data.y)\n",
    "        batch.edge_len.append(data.edge_len)\n",
    "        #\n",
    "        batch.edge_index.append(data.edge_index + base_idx)\n",
    "        batch.edge_src.append(data.edge_index[0] + base_idx)\n",
    "        batch.edge_dst.append(data.edge_index[1] + base_idx)\n",
    "        batch.batch.extend([i] * len(data.x))\n",
    "        base_idx += len(data.x)\n",
    "    return Batch(\n",
    "        x=torch.cat(batch.x),\n",
    "        y=torch.stack(batch.y),\n",
    "        edge_len=torch.cat(batch.edge_len),\n",
    "        edge_src=torch.cat(batch.edge_src).to(torch.long),\n",
    "        edge_dst=torch.cat(batch.edge_dst).to(torch.long),\n",
    "        edge_index=torch.LongTensor(np.array([torch.cat(batch.edge_src),torch.cat(batch.edge_dst)],dtype=np.int64)),\n",
    "        batch=torch.LongTensor(batch.batch),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_radius = 4.0\n",
    "sample_size   = 10000\n",
    "\n",
    "sampled_df = df.sample(n=sample_size, random_state=SEED)\n",
    "\n",
    "dataset = MaterialsDataset_MP(\n",
    "    sampled_df,\n",
    "    \"formation_energy_per_atom\",\n",
    "    cutoff=cutoff_radius,  # cutoff radius for finding neighbours\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our database is created, we can start to split it up in between a **training set**, a **testing set** and a **validation set**.\n",
    "\n",
    " - The training set is the set of data that will be used for training the model.\n",
    " - The validation set is the set of data that will be used to help the training of the model by showing verifications on untrained data, giving a score to train once again.\n",
    " - The testing set is the set of data that will be used to know how well the model can predict on unseen datas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE       = 32\n",
    "sampling_factors = [0.8, 0.1, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 8000\n",
      "Number of validation examples: 1000\n",
      "Number of testing examples: 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_485448/2958961760.py:115: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /pytorch/torch/csrc/utils/tensor_new.cpp:254.)\n",
      "  node_feat = torch.Tensor([one_hot_encode_z(el) for el in entry[\"atoms\"].get_atomic_numbers()])\n"
     ]
    }
   ],
   "source": [
    "train_set, valid_set, test_set = random_split(dataset, sampling_factors)\n",
    "\n",
    "print(f'Number of training examples: {len(train_set)}')\n",
    "print(f'Number of validation examples: {len(valid_set)}')\n",
    "print(f'Number of testing examples: {len(test_set)}')\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    train_set, batch_size=BATCH_SIZE, collate_fn=collate_fn, shuffle=True,\n",
    ")\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    valid_set, batch_size=BATCH_SIZE, collate_fn=collate_fn,\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    test_set, batch_size=BATCH_SIZE, collate_fn=collate_fn,\n",
    ")\n",
    "\n",
    "## Correct all the dtypes.\n",
    "for batch in train_loader:\n",
    "    batch.edge_index = batch.edge_index.to(torch.int64)\n",
    "    batch.edge_src   = batch.edge_src.to(torch.int64)\n",
    "    batch.edge_dst   = batch.edge_dst.to(torch.int64)\n",
    "    batch.edge_len   = batch.edge_len.to(torch.float32)\n",
    "\n",
    "for batch in val_loader:\n",
    "    batch.edge_index = batch.edge_index.to(torch.int64)\n",
    "    batch.edge_src   = batch.edge_src.to(torch.int64)\n",
    "    batch.edge_dst   = batch.edge_dst.to(torch.int64)\n",
    "    batch.edge_len   = batch.edge_len.to(torch.float32)\n",
    "\n",
    "for batch in test_loader:\n",
    "    batch.edge_index = batch.edge_index.to(torch.int64)\n",
    "    batch.edge_src   = batch.edge_src.to(torch.int64)\n",
    "    batch.edge_dst   = batch.edge_dst.to(torch.int64)\n",
    "    batch.edge_len   = batch.edge_len.to(torch.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the batches have two purposes, splitting the datas to increase the computational time and handeling multiple graphs simultaneously, thanks to the `collate_fn` that would flatten the data accordingly.\n",
    "\n",
    "Here the **batch** property in the flattened database allows to keep track which graph each atom belongs to inside each batches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🤖 Making the GNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To construct the GNN, we can split the task into two parts:\n",
    " - GNN class\n",
    " - Training and evaluation function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GNN class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the data is in the form of a graph that can be processed.\n",
    "Indeed, to make it go through a neural network we can think of three main steps that can be applied.\n",
    "1) The Message.\n",
    "\n",
    "> The message is the information passed from one node to another.\n",
    "\n",
    "Here, the message that needs to be passed to the node $i$ is a vector of all the features surrounding said node, such as the neighboring node information and its edge information.\n",
    "\n",
    "To improve the efficiency of our GNN and to train our model on the atom nature, we can embed our *one-hot* node encoding by representing it into a continuous vector space of lower dimension, using `nn.Linear`.\n",
    "\n",
    "![Picture_node](./pictures/node_embedding.png)\n",
    "\n",
    "2) The Pooling\n",
    "\n",
    "> The pooling is the way the messages from all neighbors are combined.\n",
    "\n",
    "As it was discussed in the first part, we can pool the messages by performing a convolution on the neigbhors and edges embeddings.\n",
    "For the sake of simplicity, a built in convolution class will here be used `nn.conv.GraphConv` that will perform a graph convolution on the only the node features. To add a small amount of information from the edges, the convolution from neighboring nodes will be weighted by the distance as it was discussed previously.\n",
    "\n",
    "3) The Update\n",
    "\n",
    "> The update is how the node `i` is updated given the pooled message.\n",
    "\n",
    "The results of the convolution will here update the node `i` vector of features with the information from its neighbors.\n",
    "Yet, we are interested in a **graph level property** prediction—the **formation energy** of the structure.\n",
    "\n",
    "To do so, another pooling will be done over all the nodes using `torch_scatter.scatter_mean`, following this formula:\n",
    "\n",
    "$$\\mathbf{u}_c = \\sum_{i\\in G} \\frac{\\mathbf{v}_{i}^{(T)}}{|G|}$$\n",
    "\n",
    "Where $\\mathbf{u}_c$ is the vector for final pooling of the nodes, $G$ is the number of atoms and $\\mathbf{v}_{i}^{(T)}$ is the vector of features of the node $i$ after the convolution from the neigbhors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to these three steps we have the vector that describes the graph of the structure.\n",
    "\n",
    "The $\\mathbf{u}_c$ vector can then update the GNN over the target property, the formation energy of the structure. It would only require the addition of a Multilayer Perceptron (MLP) readout using the `nn.Linear` class and a non-linear activation function like a softplus.\n",
    "It can be written as the following formula:\n",
    "\n",
    "$$E_{f_c} = \\sigma \\left( \\mathbf{W}_{r} \\mathbf{u}_{c} + \\mathbf{b}_{r} \\right)$$\n",
    "\n",
    "Where $E_{f_c}$ is the readout of the MLP for the fully connected feature dimension $f_c$ across all structures, $\\mathbf{W}_{r}$ is the weight matrix, $\\mathbf{b}_{r}$ is the bias vector, and $\\sigma$ the softplus function.\n",
    "\n",
    "![Softplus](./pictures/softplus.png)\n",
    "\n",
    "Since $E_{f_c}$ is in between 0 and 1, only using a linear transformation over all the fully connected feature vector would allow to obtain a scalar that is fitted by the formation energy.\n",
    "\n",
    "\n",
    "The skeleton of the GNN created can be visualised below:\n",
    "\n",
    "![Plan_full_GNN](./pictures/full_GNN_algorithm.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric.nn.conv import GraphConv\n",
    "from utils import scatter_mean \n",
    "class GNN(torch.nn.Module):\n",
    "    def __init__(self,\n",
    "        node_feat_dim,\n",
    "        node_hidden_dim=64,\n",
    "        num_graph_conv_layers:int=3,\n",
    "        fc_feat_dim:int=128,\n",
    "        ):\n",
    "        # Using the super() class we can then say that our own GNN class is an extension of the nn.Module, stating that we have a Neural Network.\n",
    "        super(GNN, self).__init__()\n",
    "\n",
    "        # dense layer to transform one-hot encoded node features to embedding\n",
    "        self.embedding = nn.Linear(node_feat_dim, node_hidden_dim)\n",
    "\n",
    "        # set up the convolutions of GraphConv\n",
    "        convs = []\n",
    "        for _ in range(num_graph_conv_layers):\n",
    "            convs.append(GraphConv(node_hidden_dim, node_hidden_dim,aggr=\"mean\")) \n",
    "        self.convs = nn.ModuleList(convs)\n",
    "\n",
    "        # dense layer to turn final node embeddings to the crystal features\n",
    "        self.conv_to_fc = nn.Sequential(\n",
    "           nn.Linear(node_hidden_dim, fc_feat_dim), nn.Softplus()\n",
    "        )\n",
    "\n",
    "        # dense layer to get the final target value\n",
    "        self.fc_out = nn.Linear(fc_feat_dim, 1)\n",
    "\n",
    "    def forward(self, batch):\n",
    "        \"\"\"\n",
    "        Predict the target property given a batch of data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        batch : Batch\n",
    "            The data to pass through the network.\n",
    "        \"\"\"\n",
    "        # get initial node embedding\n",
    "        node_feat = self.embedding(batch.x) ## Here x is the one-hot encoding of the nodes\n",
    "\n",
    "        # apply convolutions\n",
    "        for conv_func in self.convs:\n",
    "        #            # forward( x = node_features, edge_index, edge_weight:Optional) -> Tensor\n",
    "            node_feat = conv_func(node_feat, batch.edge_index , batch.edge_len) # \n",
    "\n",
    "        # pool node vectors\n",
    "        crys_feat = scatter_mean(node_feat, batch.batch, dim=0, dim_size=batch.batch.max() + 1)\n",
    "\n",
    "        # pass pooled vector through Fully Connected layer with activation\n",
    "        crys_feat = self.conv_to_fc(crys_feat)\n",
    "\n",
    "        # pass crystal features through final fully-connected layer        \n",
    "        return self.fc_out(crys_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and evaluation function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the class for the model is done, we need to train and evaluate it, for the validation and the testing of the model.\n",
    "\n",
    "\n",
    "To do so, the training and evaluation functions are created. Both of the functions are here using a criterion function to determine the losses and compute the Mean Absolute Error (MAE) using the `nn.L1loss()` function.\n",
    "\n",
    "Here the loss measure the difference in between the predicted value and the actual target value, while the MAE measures the average magnitude of errors between the predicted and actual values.\n",
    "\n",
    "Thanks to these tools of prediction, it would then be possible to identify the accuracy of a model during and after its training.\n",
    "\n",
    "Furthermore, the training function will then **backpropagate** the losses to allow to compute the gradient of the losses as a function of the model's parameters, and propagate the losses backward through each layer of the network. Finally, these gradient losses will then be interpreted by a **Stochastic Gradient Descent** algorithm that will update the model parameters from the previous layers of the network accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, optimizer, criterion):\n",
    "    epoch_loss = 0\n",
    "    epoch_mae = 0\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    for i, batch in enumerate(dataloader):\n",
    "        # move the data onto the GPU if available\n",
    "        # compute output\n",
    "        y_pred = model(batch)\n",
    "        loss = criterion(y_pred, batch.y)\n",
    "        mae = nn.L1Loss()(y_pred, batch.y)\n",
    "        \n",
    "        # compute gradient\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # update metrics\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_mae += mae.item()\n",
    "        \n",
    "    return epoch_loss / len(dataloader), epoch_mae / len(dataloader)\n",
    "\n",
    "def evaluate(dataloader, model, criterion):\n",
    "    epoch_loss = 0\n",
    "    epoch_mae = 0\n",
    "    \n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, batch in enumerate(dataloader):\n",
    "            \n",
    "            # compute output\n",
    "            y_pred = model(batch)\n",
    "            loss = criterion(y_pred, batch.y)\n",
    "            mae = nn.L1Loss()(y_pred, batch.y)\n",
    "                   \n",
    "            # update metrics\n",
    "            epoch_loss += loss.item()\n",
    "            epoch_mae += mae.item()\n",
    "        \n",
    "    return epoch_loss / len(dataloader), epoch_mae / len(dataloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the GNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Train MAE:   0.5562 | Valid MAE:   0.4870 |   Elapsed time: 00 min 07 s\n",
      "Epoch: 02 | Train MAE:   0.4718 | Valid MAE:   0.4586 |   Elapsed time: 00 min 14 s\n",
      "Epoch: 03 | Train MAE:   0.4668 | Valid MAE:   0.4604 |   Elapsed time: 00 min 21 s\n",
      "Epoch: 04 | Train MAE:   0.4335 | Valid MAE:   0.4168 |   Elapsed time: 00 min 27 s\n",
      "Epoch: 05 | Train MAE:   0.3991 | Valid MAE:   0.3978 |   Elapsed time: 00 min 34 s\n",
      "Epoch: 06 | Train MAE:   0.3331 | Valid MAE:   0.2777 |   Elapsed time: 00 min 41 s\n",
      "Epoch: 07 | Train MAE:   0.2835 | Valid MAE:   0.2694 |   Elapsed time: 00 min 48 s\n",
      "Epoch: 08 | Train MAE:   0.2736 | Valid MAE:   0.3448 |   Elapsed time: 00 min 56 s\n",
      "Epoch: 09 | Train MAE:   0.2615 | Valid MAE:   0.2808 |   Elapsed time: 01 min 03 s\n",
      "Epoch: 10 | Train MAE:   0.2734 | Valid MAE:   0.2443 |   Elapsed time: 01 min 10 s\n",
      "Epoch: 11 | Train MAE:   0.2626 | Valid MAE:   0.3196 |   Elapsed time: 01 min 16 s\n",
      "Epoch: 12 | Train MAE:   0.2618 | Valid MAE:   0.2819 |   Elapsed time: 01 min 23 s\n",
      "Epoch: 13 | Train MAE:   0.2539 | Valid MAE:   0.2533 |   Elapsed time: 01 min 30 s\n",
      "Epoch: 14 | Train MAE:   0.2590 | Valid MAE:   0.2622 |   Elapsed time: 01 min 37 s\n",
      "Epoch: 15 | Train MAE:   0.2588 | Valid MAE:   0.2886 |   Elapsed time: 01 min 44 s\n",
      "Epoch: 16 | Train MAE:   0.2522 | Valid MAE:   0.2437 |   Elapsed time: 01 min 51 s\n",
      "Epoch: 17 | Train MAE:   0.2507 | Valid MAE:   0.2561 |   Elapsed time: 01 min 59 s\n",
      "Epoch: 18 | Train MAE:   0.2581 | Valid MAE:   0.2609 |   Elapsed time: 02 min 06 s\n",
      "Epoch: 19 | Train MAE:   0.2529 | Valid MAE:   0.3104 |   Elapsed time: 02 min 12 s\n",
      "Epoch: 20 | Train MAE:   0.2476 | Valid MAE:   0.2370 |   Elapsed time: 02 min 19 s\n",
      "Epoch: 21 | Train MAE:   0.2438 | Valid MAE:   0.2638 |   Elapsed time: 02 min 27 s\n",
      "Epoch: 22 | Train MAE:   0.2499 | Valid MAE:   0.2359 |   Elapsed time: 02 min 34 s\n",
      "Epoch: 23 | Train MAE:   0.2390 | Valid MAE:   0.2447 |   Elapsed time: 02 min 42 s\n",
      "Epoch: 24 | Train MAE:   0.2397 | Valid MAE:   0.2328 |   Elapsed time: 02 min 49 s\n",
      "Epoch: 25 | Train MAE:   0.2386 | Valid MAE:   0.2371 |   Elapsed time: 02 min 56 s\n",
      "Epoch: 26 | Train MAE:   0.2414 | Valid MAE:   0.2326 |   Elapsed time: 03 min 04 s\n",
      "Epoch: 27 | Train MAE:   0.2342 | Valid MAE:   0.2534 |   Elapsed time: 03 min 11 s\n",
      "Epoch: 28 | Train MAE:   0.2373 | Valid MAE:   0.2556 |   Elapsed time: 03 min 17 s\n",
      "Epoch: 29 | Train MAE:   0.2336 | Valid MAE:   0.2481 |   Elapsed time: 03 min 24 s\n",
      "Epoch: 30 | Train MAE:   0.2377 | Valid MAE:   0.2296 |   Elapsed time: 03 min 31 s\n",
      "Epoch: 31 | Train MAE:   0.2326 | Valid MAE:   0.2345 |   Elapsed time: 03 min 39 s\n",
      "Epoch: 32 | Train MAE:   0.2319 | Valid MAE:   0.2236 |   Elapsed time: 03 min 45 s\n",
      "Epoch: 33 | Train MAE:   0.2300 | Valid MAE:   0.2430 |   Elapsed time: 03 min 53 s\n",
      "Epoch: 34 | Train MAE:   0.2352 | Valid MAE:   0.2217 |   Elapsed time: 04 min 01 s\n",
      "Epoch: 35 | Train MAE:   0.2246 | Valid MAE:   0.2364 |   Elapsed time: 04 min 08 s\n",
      "Epoch: 36 | Train MAE:   0.2309 | Valid MAE:   0.2274 |   Elapsed time: 04 min 16 s\n",
      "Epoch: 37 | Train MAE:   0.2273 | Valid MAE:   0.2242 |   Elapsed time: 04 min 24 s\n",
      "Epoch: 38 | Train MAE:   0.2239 | Valid MAE:   0.2175 |   Elapsed time: 04 min 32 s\n",
      "Epoch: 39 | Train MAE:   0.2230 | Valid MAE:   0.2176 |   Elapsed time: 04 min 38 s\n",
      "Epoch: 40 | Train MAE:   0.2240 | Valid MAE:   0.2384 |   Elapsed time: 04 min 45 s\n",
      "Epoch: 41 | Train MAE:   0.2270 | Valid MAE:   0.2282 |   Elapsed time: 04 min 53 s\n",
      "Epoch: 42 | Train MAE:   0.2158 | Valid MAE:   0.2392 |   Elapsed time: 05 min 00 s\n",
      "Epoch: 43 | Train MAE:   0.2105 | Valid MAE:   0.2515 |   Elapsed time: 05 min 07 s\n",
      "Epoch: 44 | Train MAE:   0.2168 | Valid MAE:   0.2380 |   Elapsed time: 05 min 14 s\n",
      "Epoch: 45 | Train MAE:   0.2093 | Valid MAE:   0.2175 |   Elapsed time: 05 min 21 s\n",
      "Epoch: 46 | Train MAE:   0.2057 | Valid MAE:   0.2206 |   Elapsed time: 05 min 29 s\n",
      "Epoch: 47 | Train MAE:   0.2051 | Valid MAE:   0.2367 |   Elapsed time: 05 min 36 s\n",
      "Epoch: 48 | Train MAE:   0.1991 | Valid MAE:   0.2036 |   Elapsed time: 05 min 43 s\n",
      "Epoch: 49 | Train MAE:   0.1906 | Valid MAE:   0.2064 |   Elapsed time: 05 min 50 s\n",
      "Epoch: 50 | Train MAE:   0.1916 | Valid MAE:   0.2136 |   Elapsed time: 05 min 57 s\n",
      "Epoch: 51 | Train MAE:   0.1868 | Valid MAE:   0.2150 |   Elapsed time: 06 min 04 s\n",
      "Epoch: 52 | Train MAE:   0.1872 | Valid MAE:   0.1930 |   Elapsed time: 06 min 11 s\n",
      "Epoch: 53 | Train MAE:   0.1796 | Valid MAE:   0.1902 |   Elapsed time: 06 min 18 s\n",
      "Epoch: 54 | Train MAE:   0.1806 | Valid MAE:   0.1876 |   Elapsed time: 06 min 25 s\n",
      "Epoch: 55 | Train MAE:   0.1804 | Valid MAE:   0.2075 |   Elapsed time: 06 min 32 s\n",
      "Epoch: 56 | Train MAE:   0.1789 | Valid MAE:   0.1803 |   Elapsed time: 06 min 39 s\n",
      "Epoch: 57 | Train MAE:   0.1794 | Valid MAE:   0.2084 |   Elapsed time: 06 min 46 s\n",
      "Epoch: 58 | Train MAE:   0.1768 | Valid MAE:   0.1834 |   Elapsed time: 06 min 54 s\n",
      "Epoch: 59 | Train MAE:   0.1759 | Valid MAE:   0.1835 |   Elapsed time: 07 min 01 s\n",
      "Epoch: 60 | Train MAE:   0.1721 | Valid MAE:   0.1823 |   Elapsed time: 07 min 08 s\n",
      "Test Loss: 0.0756 | Test MAE: 0.1617\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "hidden_channels         = 64\n",
    "num_graph_conv_layers   = 3\n",
    "fc_feat_dim             = 128\n",
    "max_epoch               = 60\n",
    "\n",
    "\n",
    "in_node_features = dataset[0].x.size()[1]\n",
    "model = GNN(in_node_features, hidden_channels,num_graph_conv_layers=num_graph_conv_layers,fc_feat_dim=fc_feat_dim)\n",
    "\n",
    "# Optimizer and loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # We use a basic Adam optimizer.\n",
    "criterion = torch.nn.MSELoss()  # Use CrossEntropyLoss for classification tasks\n",
    "\n",
    "start_time = time.time()\n",
    "save_losses_train = []\n",
    "save_losses_valid = []\n",
    "save_mae_train = []\n",
    "save_mae_valid = []\n",
    "# Using the DataLoaders for training and validation\n",
    "for epoch in range(0,max_epoch):\n",
    "    train_loss,train_mae = train(train_loader,model,optimizer,criterion)\n",
    "    val_loss,valid_mae = evaluate(val_loader,model,criterion)\n",
    "    # Save validation and losses.\n",
    "    save_losses_train.append(train_loss)\n",
    "    save_losses_valid.append(val_loss)\n",
    "    save_mae_train.append(train_mae)\n",
    "    save_mae_valid.append(valid_mae)\n",
    "    #\n",
    "    epoch_time = time.time() - start_time\n",
    "    print(f'Epoch: {epoch+1:02} | Train MAE: {train_mae:8.4f} | Valid MAE: {valid_mae:8.4f}'\n",
    "         f' |   Elapsed time: {time.strftime(\"%M min %S s\", time.gmtime(epoch_time))}')\n",
    "# Test the model\n",
    "test_loss,test_mae = evaluate(test_loader,model,criterion)\n",
    "print(f'Test Loss: {test_loss:.4f} | Test MAE: {test_mae:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f958b80e170>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(range(1,max_epoch+1), save_losses_train, label=\"train\")\n",
    "ax.plot(range(1,max_epoch+1), save_losses_valid, label=\"valid\")\n",
    "ax.set(xlabel=\"Epoch\", ylabel=\"Losses\")\n",
    "ax.set_yscale(\"log\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f957a7cf910>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(range(1,max_epoch+1), save_mae_train, label=\"train\")\n",
    "ax.plot(range(1,max_epoch+1), save_mae_valid, label=\"valid\")\n",
    "ax.set(xlabel=\"Epoch\", ylabel=\"MAE\")\n",
    "ax.set_yscale(\"log\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f958dec9f60>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "actual = []\n",
    "predicted = []\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for i, batch in enumerate(test_loader):\n",
    "        y_pred = model(batch)\n",
    "        predicted.extend(y_pred.data.cpu().tolist())\n",
    "        actual.extend(batch.y.tolist())\n",
    "#\n",
    "min_val, max_val = np.min(actual), np.max(actual)\n",
    "#\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(np.array(actual), np.array(predicted), label=\"test\")\n",
    "ax.plot([min_val, max_val], [min_val, max_val], color=\"orange\", linestyle=\"--\", label=\"actual\")\n",
    "ax.set(ylabel=\"Predicted E$_{f}$\", xlabel=\"Actual E$_{f}$\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the GNN fits $E_f$ with a large error in between the prediction and the actual $E_f$ value. However, this result can be explained by the fact the model created is not that expressive, with no descriptor of the surroundings that could have been done using Gaussians embedding of the distances or a [Smooth Overlap of Atomic Positions (SOAP)](https://doi.org/10.1103/PhysRevB.87.184115) descriptor for example. Also, the amount of data was too small for the diversity of different structures that can be found in material project legacy.\n",
    "\n",
    "The construction of a simple GNN was indeed successful. However, we can easily see the limits of our approximations by taking such a diverse amount of datas. This shortcomings can be tackled by using a GNN that is more complex and that possesses more degrees of freedom, leading to Universal Machine Learning Force Fields such as NequIP, or MACE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🔬 Chemical applications of GNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MACE for Molecular Dynamic computation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most well known examples of GNN is [MACE](https://doi.org/10.48550/arXiv.2401.00096), a universal machine learning force field (uMLFF). Using some pretrained models, it would be possible to run a Molecular Dynamic (MD) computation of diverse systems.\n",
    "\n",
    "This model uses material project database with DFT (PBE+U) structures (here we are using MACE-MP-0, but other databases are available as pretrained models).\n",
    "\n",
    "One way to use this uMLFF would be the computation of an MD calculation using [pretrained MACE MP models (the tutorial was used as a basis for the code below)](https://mace-docs.readthedocs.io/en/latest/guide/foundation_models.html). Using ase an example of MD is here shown with NaCl above its fusion temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lorentyfle/test/.venv/lib/python3.10/site-packages/e3nn/o3/_wigner.py:10: UserWarning: Environment variable TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD detected, since the`weights_only` argument was not explicitly passed to `torch.load`, forcing weights_only=False.\n",
      "  _Jd, _W3j_flat, _W3j_indices = torch.load(os.path.join(os.path.dirname(__file__), 'constants.pt'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuequivariance or cuequivariance_torch is not available. Cuequivariance acceleration will be disabled.\n",
      "Using medium MPA-0 model as default MACE-MP model, to use previous (before 3.10) default model please specify 'medium' as model argument\n",
      "Using Materials Project MACE for MACECalculator with /home/lorentyfle/.cache/mace/macempa0mediummodel\n",
      "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lorentyfle/test/.venv/lib/python3.10/site-packages/mace/calculators/mace.py:139: UserWarning: Environment variable TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD detected, since the`weights_only` argument was not explicitly passed to `torch.load`, forcing weights_only=False.\n",
      "  torch.load(f=model_path, map_location=device)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default dtype float32 does not match model dtype float64, converting models to float32.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lorentyfle/test/.venv/lib/python3.10/site-packages/ase/md/md.py:53: FutureWarning: Specify the temperature in K using the 'temperature_K' argument\n",
      "  warnings.warn(FutureWarning(w))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mace.calculators import mace_mp\n",
    "from ase import build\n",
    "from ase.md import Langevin\n",
    "from ase.md.velocitydistribution import MaxwellBoltzmannDistribution\n",
    "from ase import units\n",
    "\n",
    "macemp = mace_mp() # return the default medium ASE calculator equivalent to mace_mp(model=\"medium\") in MACE < 0.3.10 and mace_mp(model=\"medium-mpa-0\") in MACE >= 0.3.10\n",
    "#macemp = mace_mp(model=\"large\") # return a larger model\n",
    "#macemp = mace_mp(model=\"https://tinyurl.com/y7uhwpje\") # downlaod the model at the given url\n",
    "#macemp = mace_mp(dispersion=True) # return a model with D3 dispersion correction\n",
    "atoms = build.bulk('NaCl', crystalstructure='rocksalt', a=5.64)\n",
    "atoms.calc = macemp\n",
    "\n",
    "# Initialize velocities.\n",
    "T_init = 1300  # Initial temperature in K\n",
    "MaxwellBoltzmannDistribution(atoms, T_init * units.kB)\n",
    "\n",
    "# Set up the Langevin dynamics engine for NVT ensemble.\n",
    "dyn = Langevin(atoms, 0.5 * units.fs, T_init * units.kB, 0.001)\n",
    "n_steps = 200 # Number of steps to run\n",
    "dyn.run(n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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