experiment/sevennet

config.yml

@@ -142,3 +142,22 @@ notebooks:
       - ssl
       - h5py
       - lmdb
+  - name: sevennet
+    packages_pyodide:
+      # Packages with dependencies
+      - opt_einsum
+      - orjson
+      - pyyaml
+      - setuptools
+      # Packages without dependencies
+      - nodeps:opt_einsum_fx
+      - nodeps:e3nn>=0.5
+      - nodeps:ase
+      - nodeps:monty
+      - scikit-learn
+      # SevenNet slim wheel (stripped to 7net-0 model only)
+      - emfs:/drive/packages/sevenn-0.12.1-py3-none-any.whl
+      # Stubbed packages (patched by torch_pyodide with include_sevennet=True)
+      - ssl
+      - h5py
+      - lmdb

other/experiments/jupyterlite/relax_structure_with_sevennet.ipynb

@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "33da1875",
+   "metadata": {},
+   "source": [
+    "# Relax Structure with SevenNet \u2014 E(3)-Equivariant GNN Potential\n",
+    "\n",
+    "This notebook demonstrates structural relaxation using **SevenNet** (7net-0),\n",
+    "an E(3)-equivariant graph neural network interatomic potential based on the NequIP architecture.\n",
+    "\n",
+    "The model supports all elements up to Z=89 and predicts energies, forces, and stresses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3ea9252",
+   "metadata": {},
+   "source": [
+    "## 1. Set Input Parameters\n",
+    "### 1.1. Structure and Relaxation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d9ef36f6",
+   "metadata": {},
+   "source": [
+    "FOLDER = \"uploads\"\n",
+    "STRUCTURE_NAME = \"Interface\"  # Name of the structure to load from local file\n",
+    "\n",
+    "RELAXATION_PARAMETERS = {\n",
+    "    \"FMAX\": 0.05,\n",
+    "}"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5709f12",
+   "metadata": {},
+   "source": [
+    "## 2. Install Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "766bc77a",
+   "metadata": {},
+   "source": [
+    "from mat3ra.notebooks_utils.packages import install_packages\n",
+    "\n",
+    "await install_packages(\"made|api_examples|torch|sevennet\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "0cedf7f5",
+   "metadata": {},
+   "source": [
+    "from mat3ra.notebooks_utils.pyodide.packages.torch import apply_all_patches\n",
+    "\n",
+    "apply_all_patches(include_sevennet=True)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8624f00",
+   "metadata": {},
+   "source": [
+    "## 3. Load Materials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b6c6efa8",
+   "metadata": {},
+   "source": [
+    "from mat3ra.made.material import Material\n",
+    "from mat3ra.notebooks_utils.material import load_material_from_folder\n",
+    "from mat3ra.standata.materials import Materials\n",
+    "\n",
+    "structure = load_material_from_folder(FOLDER, STRUCTURE_NAME) or Material.create(\n",
+    "    Materials.get_by_name_first_match(STRUCTURE_NAME))\n",
+    "\n",
+    "print(f\"INFO: Found: '{structure.name}'\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0491324",
+   "metadata": {},
+   "source": [
+    "### 3.1. Visualize Input Structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "630c1c78",
+   "metadata": {},
+   "source": [
+    "from mat3ra.notebooks_utils.ipython.entity.material.visualize import ViewersEnum, visualize_materials as visualize\n",
+    "\n",
+    "visualize(structure, repetitions=[1, 1, 1], rotation=\"-90x\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00170e3a",
+   "metadata": {},
+   "source": [
+    "## 4. Apply Relaxation\n",
+    "### 4.1. Load SevenNet Model and Create Calculator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "4c06be2b",
+   "metadata": {},
+   "source": [
+    "from sevenn.calculator import SevenNetCalculator\n",
+    "\n",
+    "# Use the bundled pretrained 7net-0 model\n",
+    "calculator = SevenNetCalculator(model=\"7net-0\", device=\"cpu\")\n",
+    "\n",
+    "print(\"SevenNet 7net-0 model loaded\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebaa0f39",
+   "metadata": {},
+   "source": [
+    "### 4.2. Relax with SevenNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f17d5d6d",
+   "metadata": {},
+   "source": [
+    "import plotly.graph_objs as go\n",
+    "from IPython.display import display\n",
+    "from plotly.subplots import make_subplots\n",
+    "\n",
+    "from mat3ra.made.tools.convert import to_ase\n",
+    "from ase.optimize import BFGS\n",
+    "\n",
+    "ase_structure = to_ase(structure)\n",
+    "ase_structure.calc = calculator\n",
+    "dyn = BFGS(ase_structure)\n",
+    "\n",
+    "steps = []\n",
+    "energies = []\n",
+    "\n",
+    "fig = make_subplots(rows=1, cols=1, specs=[[{\"type\": \"scatter\"}]])\n",
+    "scatter = go.Scatter(x=[], y=[], mode=\"lines+markers\", name=\"Energy\")\n",
+    "fig.add_trace(scatter)\n",
+    "fig.update_layout(title_text=\"Real-time Optimization Progress\", xaxis_title=\"Step\", yaxis_title=\"Energy (eV)\")\n",
+    "\n",
+    "try:\n",
+    "    f = go.FigureWidget(fig)\n",
+    "except ImportError:\n",
+    "    f = go.Figure(fig)\n",
+    "display(f)\n",
+    "\n",
+    "\n",
+    "def plotly_callback():\n",
+    "    step = dyn.nsteps\n",
+    "    energy = ase_structure.get_total_energy()\n",
+    "    steps.append(step)\n",
+    "    energies.append(energy)\n",
+    "    print(f\"Step: {step}, Energy: {energy:.4f} eV\")\n",
+    "    if hasattr(f, \"batch_update\"):\n",
+    "        with f.batch_update():\n",
+    "            f.data[0].x = steps\n",
+    "            f.data[0].y = energies\n",
+    "    else:\n",
+    "        f.data[0].x = steps\n",
+    "        f.data[0].y = energies\n",
+    "\n",
+    "\n",
+    "dyn.attach(plotly_callback, interval=1)\n",
+    "dyn.run(fmax=RELAXATION_PARAMETERS[\"FMAX\"])\n",
+    "\n",
+    "ase_original_structure = to_ase(structure)\n",
+    "ase_original_structure.calc = calculator\n",
+    "ase_final_structure = ase_structure\n",
+    "\n",
+    "original_energy = ase_original_structure.get_total_energy()\n",
+    "relaxed_energy = ase_structure.get_total_energy()\n",
+    "\n",
+    "print(f\"The final energy is {float(relaxed_energy):.3f} eV.\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e788e0e3",
+   "metadata": {},
+   "source": [
+    "## 5. Analyze Results\n",
+    "### 5.1. View Structure Before and After Relaxation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0391a838",
+   "metadata": {},
+   "source": [
+    "from mat3ra.made.tools.convert import from_ase\n",
+    "\n",
+    "material_original = Material.create(from_ase(ase_original_structure))\n",
+    "material_relaxed = Material.create(from_ase(ase_final_structure))\n",
+    "material_original.name = structure.name\n",
+    "material_relaxed.name = structure.name + \" (SevenNet Relaxed)\"\n",
+    "\n",
+    "visualize([material_original, material_relaxed], viewer=ViewersEnum.wave)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41bba468",
+   "metadata": {},
+   "source": [
+    "### 5.2. Output interlayer distance before and after relaxation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3f19487a",
+   "metadata": {},
+   "source": [
+    "from mat3ra.made.tools.analyze.other import get_average_interlayer_distance\n",
+    "\n",
+    "SUBSTRATE_TAG = 0\n",
+    "FILM_TAG = 1\n",
+    "\n",
+    "print(\n",
+    "    f\"Interlayer distance before relaxation: {get_average_interlayer_distance(material_original, SUBSTRATE_TAG, FILM_TAG):.4f} \u00c5\")\n",
+    "print(\n",
+    "    f\"Interlayer distance after relaxation:  {get_average_interlayer_distance(material_relaxed, SUBSTRATE_TAG, FILM_TAG):.4f} \u00c5\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c7ae982",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "[1] SevenNet: https://github.com/MDIL-SNU/SevenNet\n",
+    "\n",
+    "[2] Yutack Park et al., \"Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials,\" J. Chem. Theory Comput. (2024)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (Pyodide)",
+   "language": "python",
+   "name": "python"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "python",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}