{ "cells": [ { "cell_type": "markdown", "id": "cf081b77", "metadata": {}, "source": [ "# UMA Catalysis Tutorial\n", "\n", "Author: Zack Ulissi (Meta, CMU), with help from AI coding agents / LLMs\n", "\n", "Original paper: Bjarne Kreitz et al. JPCC (2021)\n", "\n", "## Overview\n", "\n", "This tutorial demonstrates how to use the Universal Model for Atoms (UMA) machine learning potential to perform comprehensive catalyst surface analysis. We replicate key computational workflows from [\"Microkinetic Modeling of CO₂ Desorption from Supported Multifaceted Ni Catalysts\"](https://pubs.acs.org/doi/10.1021/acs.jpcc.0c09985) by Bjarne Kreitz (now faculty at Georgia Tech!), showing how ML potentials can accelerate computational catalysis research.\n", "\n", "\n", "```{admonition} Learning Objectives\n", ":class: note\n", "\n", "By the end of this tutorial, you will be able to:\n", "- Optimize bulk crystal structures and extract lattice constants\n", "- Calculate surface energies using linear extrapolation methods\n", "- Construct Wulff shapes to predict nanoparticle morphologies\n", "- Compute adsorption energies with zero-point energy corrections\n", "- Study coverage-dependent binding phenomena\n", "- Calculate reaction barriers using the nudged elastic band (NEB) method\n", "- Apply D3 dispersion corrections to improve accuracy\n", "```\n", "\n", "```{admonition} About UMA-S-1P2P1\n", ":class: tip\n", "\n", "The **UMA-S-1P2P1** model is a state-of-the-art universal machine learning\n", "potential trained on the OMat24, OC20, OMol25, ODAC23, and OMC25 datasets,\n", "covering diverse materials and surface chemistries. It provides roughly a\n", "1000× speedup over DFT while maintaining useful accuracy for screening studies.\n", "Here we use `uma-s-1p2p1`, the latest patch release of the small UMA 1.2 model.\n", "The checkpoint is open science under a lightweight license that users accept\n", "through Hugging Face.\n", "\n", "You can read more about the UMA models here: https://arxiv.org/abs/2506.23971\n", "```\n", "\n", "## Installation and Setup\n", "\n", "This tutorial uses a number of helpful open source packages:\n", "- `ase` - Atomic Simulation Environment\n", "- `fairchem` - FAIR Chemistry ML potentials (formerly OCP)\n", "- `pymatgen` - Materials analysis\n", "- `matplotlib` - Visualization\n", "- `numpy` - Numerical computing\n", "- `torch-dftd` - Dispersion corrections\n", "among many others!\n", "\n", "### Huggingface setups\n", "\n", "You need to get a HuggingFace account and request access to the UMA models.\n", "\n", "You need a Huggingface account, request access to https://huggingface.co/facebook/UMA, and to create a Huggingface token at https://huggingface.co/settings/tokens/ with these permission:\n", "\n", "Permissions: Read access to contents of all public gated repos you can access\n", "\n", "Then, add the token as an environment variable using `huggingface-cli login`:" ] }, { "cell_type": "code", "execution_count": null, "id": "1a7694bd", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "# Enter token via huggingface-cli\n", "! huggingface-cli login" ] }, { "cell_type": "markdown", "id": "de544604", "metadata": {}, "source": [ "or you can set the token via HF_TOKEN variable:" ] }, { "cell_type": "code", "execution_count": null, "id": "115f82e6", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "# Set token via env variable\n", "import os\n", "\n", "os.environ[\"HF_TOKEN\"] = \"MYTOKEN\"" ] }, { "cell_type": "markdown", "id": "4bf74ea0", "metadata": {}, "source": [ "### FAIR Chemistry (UMA) installation\n", "\n", "It may be enough to use `pip install fairchem-core`. This gets you the latest version on PyPi (https://pypi.org/project/fairchem-core/)\n", "\n", "Here we install some sub-packages. This can take 2-5 minutes to run." ] }, { "cell_type": "code", "execution_count": null, "id": "b593bf02", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "! pip install fairchem-core[docs] fairchem-data-oc fairchem-applications-cattsunami x3dase" ] }, { "cell_type": "code", "execution_count": null, "id": "20819f9d", "metadata": {}, "outputs": [], "source": [ "# Check that packages are installed\n", "!pip list | grep fairchem" ] }, { "cell_type": "code", "execution_count": 1, "id": "8e8590e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.12.1.dev12+g0b41cd85'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import fairchem.core\n", "\n", "fairchem.core.__version__" ] }, { "cell_type": "markdown", "id": "e7a331fa", "metadata": {}, "source": [ "## Package imports\n", "\n", "First, let's import all necessary libraries and initialize the UMA-S-1P2P1 predictor:" ] }, { "cell_type": "code", "execution_count": 2, "id": "36b2b437", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Loading UMA-S-1P2P1 model...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:device was not explicitly set, using device='cuda'.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✓ Model loaded successfully!\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "import ase.io\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from ase import Atoms\n", "from ase.build import bulk\n", "from ase.constraints import FixBondLengths\n", "from ase.io import write\n", "from ase.mep import interpolate\n", "from ase.mep.dyneb import DyNEB\n", "from ase.optimize import FIRE, LBFGS\n", "from ase.vibrations import Vibrations\n", "from ase.visualize import view\n", "from fairchem.core import FAIRChemCalculator, pretrained_mlip\n", "from fairchem.data.oc.core import (\n", " Adsorbate,\n", " AdsorbateSlabConfig,\n", " Bulk,\n", " MultipleAdsorbateSlabConfig,\n", " Slab,\n", ")\n", "from pymatgen.analysis.wulff import WulffShape\n", "from pymatgen.core import Lattice, Structure\n", "from pymatgen.core.surface import SlabGenerator\n", "from pymatgen.io.ase import AseAtomsAdaptor\n", "from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator\n", "\n", "# Set up output directory structure\n", "output_dir = Path(\"ni_tutorial_results\")\n", "output_dir.mkdir(exist_ok=True)\n", "\n", "# Create subdirectories for each part\n", "part_dirs = {\n", " \"part1\": \"part1-bulk-optimization\",\n", " \"part2\": \"part2-surface-energies\",\n", " \"part3\": \"part3-wulff-construction\",\n", " \"part4\": \"part4-h-adsorption\",\n", " \"part5\": \"part5-coverage-dependence\",\n", " \"part6\": \"part6-co-dissociation\",\n", "}\n", "\n", "for key, dirname in part_dirs.items():\n", " (output_dir / dirname).mkdir(exist_ok=True)\n", "\n", "# Create subdirectories for different facets in part2\n", "for facet in [\"111\", \"100\", \"110\", \"211\"]:\n", " (output_dir / part_dirs[\"part2\"] / f\"ni{facet}\").mkdir(exist_ok=True)\n", "\n", "# Initialize the UMA-S-1P2P1 predictor\n", "print(\"\\nLoading UMA-S-1P2P1 model...\")\n", "predictor = pretrained_mlip.get_predict_unit(\"uma-s-1p2p1\")\n", "print(\"✓ Model loaded successfully!\")" ] }, { "cell_type": "markdown", "id": "f16ba517", "metadata": {}, "source": [ "It is somewhat time consuming to run this. We're going to use a small number of bulks for the testing of this documentation, but otherwise run all of the results for the actual documentation." ] }, { "cell_type": "code", "execution_count": 3, "id": "199d47a5", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "fast_docs = os.environ.get(\"FAST_DOCS\", \"false\").lower() == \"true\"\n", "if fast_docs:\n", " num_sites = 2\n", " relaxation_steps = 20\n", "else:\n", " num_sites = 5\n", " relaxation_steps = 300" ] }, { "cell_type": "markdown", "id": "ec68c1c9", "metadata": {}, "source": [ "---\n", "\n", "## Part 1: Bulk Crystal Optimization\n", "\n", "### Introduction\n", "\n", "Before studying surfaces, we need to determine the equilibrium lattice constant of bulk Ni. This is crucial because surface energies and adsorbate binding depend strongly on the underlying lattice parameter.\n", "\n", "### Theory\n", "\n", "For FCC metals like Ni, the lattice constant **a** defines the unit cell size. The experimental value for Ni is **a = 3.524 Å** at room temperature. We'll optimize both atomic positions and the cell volume to find the ML potential's equilibrium structure." ] }, { "cell_type": "code", "execution_count": 4, "id": "006e2582", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial lattice constant: 3.52 Å\n", "Number of atoms: 4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_1764705/3959847705.py:15: DeprecationWarning: Use FrechetCellFilter for better convergence w.r.t. cell variables.\n", " ecf = ExpCellFilter(ni_bulk)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "Experimental lattice constant: 3.52 Å\n", "Optimized lattice constant: 3.52 Å\n", "Relative error: 0.25%\n", "==================================================\n" ] } ], "source": [ "# Create initial FCC Ni structure\n", "a_initial = 3.52 # Å, close to experimental\n", "ni_bulk = bulk(\"Ni\", \"fcc\", a=a_initial, cubic=True)\n", "\n", "print(f\"Initial lattice constant: {a_initial:.2f} Å\")\n", "print(f\"Number of atoms: {len(ni_bulk)}\")\n", "\n", "# Set up calculator for bulk optimization\n", "calc = FAIRChemCalculator(predictor, task_name=\"omat\")\n", "ni_bulk.calc = calc\n", "\n", "# Use ExpCellFilter to allow cell relaxation\n", "from ase.filters import ExpCellFilter\n", "\n", "ecf = ExpCellFilter(ni_bulk)\n", "\n", "# Optimize with LBFGS\n", "opt = LBFGS(\n", " ecf,\n", " trajectory=str(output_dir / part_dirs[\"part1\"] / \"ni_bulk_opt.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part1\"] / \"ni_bulk_opt.log\"),\n", ")\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "# Extract results\n", "cell = ni_bulk.get_cell()\n", "a_optimized = cell[0, 0]\n", "a_exp = 3.524 # Experimental value\n", "error = abs(a_optimized - a_exp) / a_exp * 100\n", "\n", "print(f\"\\n{'='*50}\")\n", "print(f\"Experimental lattice constant: {a_exp:.2f} Å\")\n", "print(f\"Optimized lattice constant: {a_optimized:.2f} Å\")\n", "print(f\"Relative error: {error:.2f}%\")\n", "print(f\"{'='*50}\")\n", "\n", "ase.io.write(str(output_dir / part_dirs[\"part1\"] / \"ni_bulk_relaxed.cif\"), ni_bulk)\n", "\n", "# Store results for later use\n", "a_opt = a_optimized" ] }, { "cell_type": "markdown", "id": "5e1ab3e5", "metadata": {}, "source": [ "```{admonition} Missing UMA access?\n", ":class: dropdown, tip\n", "\n", "Don't have access to UMA yet? You can still explore this calculation!\n", "\n", "[Download example Ni bulk structure](example_configs/ni_bulk.xyz) and test it in the [UMA demo (no login required)](https://facebook-fairchem-uma-demo.hf.space/) to see how the model predicts properties for bulk Ni.\n", "```\n", "\n", "```{admonition} Understanding the Results\n", ":class: tip\n", "\n", "uma-s-1p2p1 using the `omat` task name will predict lattice constants at the PBE level of DFT. For metals, PBE typically predicts lattice constants within 1-2% of experimental values.\n", "\n", "Small discrepancies arise from:\n", "- Training data biases (if your structure is far from OMAT24)\n", "- Temperature effects (0 K vs room temperature). You can do a quasi-harmonic analysis to include finite temperature effects if desired.\n", "- Quantum effects not captured by the underlying DFT/PBE simulations\n", "\n", "For surface calculations, using the ML-optimized lattice constant maintains internal consistency.\n", "```\n", "\n", "```{admonition} Comparison with Paper\n", ":class: note\n", "\n", "**Paper (Table 1):** Ni lattice constant = 3.524 Å (experimental reference)\n", "\n", "The UMA-S-1P2P1 model with OMAT provides excellent agreement with experiment, as would be expected for the PBE functional for simple BCC Ni. The underlying calculations for OMat24 and the original results cited in the paper should be very similar (both PBE), so the fact that the results are a little closer to experiment than the original results is within the numerical noise of the ML model.\n", "```\n", "\n", "```{admonition} Further exploration\n", ":class: seealso\n", "\n", "Try modifying the following parameters and observe the effects:\n", "\n", "1. **Task name**: The original paper re-relaxed the structures at the RPBE level of theory before continuing. Try that with the uma-s-1p2p1 model (using the oc20 task name) and see if it matters here.\n", "2. **Initial guess**: Change `a_initial` to 3.0 or 4.0 Å. Does the optimizer still converge to the same value?\n", "3. **Convergence criterion**: Tighten `fmax` to 0.01 eV/Å. How many more steps are required?\n", "4. **Different metals**: Replace `\"Ni\"` with `\"Cu\"`, `\"Pd\"`, or `\"Pt\"`. Compare predicted vs experimental lattice constants.\n", "5. **Cell shape**: Remove `cubic=True` and allow the cell to distort. Does FCC remain stable?\n", "\n", "```\n", "\n", "---\n", "\n", "## Part 2: Surface Energy Calculations\n", "\n", "### Introduction\n", "\n", "Surface energy (γ) quantifies the thermodynamic cost of creating a surface. It determines surface stability, morphology, and catalytic activity. We'll calculate γ for four low-index Ni facets: (111), (100), (110), and (211).\n", "\n", "### Theory\n", "\n", "The surface energy is defined as:\n", "\n", "$$\n", "\\gamma = \\frac{E_{\\text{slab}} - N \\cdot E_{\\text{bulk}}}{2A}\n", "$$\n", "\n", "where:\n", "- $E_{\\text{slab}}$ = total energy of the slab\n", "- $N$ = number of atoms in the slab\n", "- $E_{\\text{bulk}}$ = bulk energy per atom\n", "- $A$ = surface area\n", "- Factor of 2 accounts for two surfaces (top and bottom)\n", "\n", "**Challenge**: Direct calculation suffers from quantum size effects, and if you were doing DFT calculations small numerical errors in the simulation or from the K-point grid sampling can lead to small (but significant) errors in the bulk lattice energy.\n", "\n", "**Solution**: It is fairly common when calculating surface energies to use the bulk energy from a bulk relaxation in the above equation. However, because DFT often has some small numerical noise in the predictions from k-point convergence, this might lead to the wrong surface energy. Instead, two more careful schemes are either:\n", "1. Calculate the energy of a bulk structure oriented to each slab to maximize cancellation of small numerical errors or\n", "2. Calculate the energy of multiple slabs at multiple thicknesses and extrapolate to zero thickness. The intercept will be the surface energy, and the slope will be a fitted bulk energy. A benefit of this approach is that it also forces us to check that we have a sufficiently thick slab for a well defined surface energy; if the fit is non-linear we need thicker slabs.\n", "\n", "We'll use the linear extrapolation method here as it's more likely to work in future DFT studies if you use this code!\n", "\n", "### Step 1: Setup and Bulk Energy Reference\n", "\n", "First, we'll set up the calculation parameters and get the bulk energy reference:" ] }, { "cell_type": "code", "execution_count": 5, "id": "91115ec3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bulk energy reference:\n", " Total energy: -21.97 eV\n", " Number of atoms: 4\n", " Energy per atom: -5.491441 eV/atom\n" ] } ], "source": [ "# Calculate surface energies for all facets\n", "facets = [(1, 1, 1), (1, 0, 0), (1, 1, 0), (2, 1, 1)]\n", "surface_energies = {}\n", "surface_energies_SI = {}\n", "all_fit_data = {}\n", "\n", "# Get bulk energy reference (only need to do this once)\n", "E_bulk_total = ni_bulk.get_potential_energy()\n", "N_bulk = len(ni_bulk)\n", "E_bulk_per_atom = E_bulk_total / N_bulk\n", "\n", "print(f\"Bulk energy reference:\")\n", "print(f\" Total energy: {E_bulk_total:.2f} eV\")\n", "print(f\" Number of atoms: {N_bulk}\")\n", "print(f\" Energy per atom: {E_bulk_per_atom:.6f} eV/atom\")" ] }, { "cell_type": "markdown", "id": "0eba869d", "metadata": {}, "source": [ "### Step 2: Generate and Relax Slabs\n", "\n", "Now we'll loop through each facet, generating slabs at three different thicknesses:" ] }, { "cell_type": "code", "execution_count": 6, "id": "3a8218c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "Calculating Ni(111) surface energy\n", "============================================================\n", "\n", " Thickness: 4 layers\n", " Atoms: 4\n", " Energy: -20.69 eV\n", "\n", " Thickness: 6 layers\n", " Atoms: 6\n", " Energy: -31.67 eV\n", "\n", " Thickness: 8 layers\n", " Atoms: 8\n", " Energy: -42.66 eV\n", "\n", " Linear fit:\n", " Slope: -5.492960 eV/atom (cf. bulk -5.491441)\n", " Intercept: 1.28 eV\n", "\n", " Surface energy:\n", " γ = 0.120067 eV/Ų = 1.92 J/m²\n", "\n", "============================================================\n", "Calculating Ni(100) surface energy\n", "============================================================\n", "\n", " Thickness: 4 layers\n", " Atoms: 8\n", " Energy: -42.16 eV\n", "\n", " Thickness: 6 layers\n", " Atoms: 12\n", " Energy: -64.12 eV\n", "\n", " Thickness: 8 layers\n", " Atoms: 16\n", " Energy: -86.09 eV\n", "\n", " Linear fit:\n", " Slope: -5.491520 eV/atom (cf. bulk -5.491441)\n", " Intercept: 1.78 eV\n", "\n", " Surface energy:\n", " γ = 0.143731 eV/Ų = 2.30 J/m²\n", "\n", "============================================================\n", "Calculating Ni(110) surface energy\n", "============================================================\n", "\n", " Thickness: 4 layers\n", " Atoms: 8\n", " Energy: -41.36 eV\n", "\n", " Thickness: 6 layers\n", " Atoms: 12\n", " Energy: -63.34 eV\n", "\n", " Thickness: 8 layers\n", " Atoms: 16\n", " Energy: -85.30 eV\n", "\n", " Linear fit:\n", " Slope: -5.492427 eV/atom (cf. bulk -5.491441)\n", " Intercept: 2.57 eV\n", "\n", " Surface energy:\n", " γ = 0.147336 eV/Ų = 2.36 J/m²\n", "\n", "============================================================\n", "Calculating Ni(211) surface energy\n", "============================================================\n", "\n", " Thickness: 4 layers\n", " Atoms: 8\n", " Energy: -39.69 eV\n", "\n", " Thickness: 6 layers\n", " Atoms: 12\n", " Energy: -61.61 eV\n", "\n", " Thickness: 8 layers\n", " Atoms: 16\n", " Energy: -83.58 eV\n", "\n", " Linear fit:\n", " Slope: -5.485733 eV/atom (cf. bulk -5.491441)\n", " Intercept: 4.20 eV\n", "\n", " Surface energy:\n", " γ = 0.138877 eV/Ų = 2.23 J/m²\n" ] } ], "source": [ "# Convert bulk to pymatgen structure for slab generation\n", "adaptor = AseAtomsAdaptor()\n", "ni_structure = adaptor.get_structure(ni_bulk)\n", "\n", "for facet in facets:\n", " facet_str = \"\".join(map(str, facet))\n", " print(f\"\\n{'='*60}\")\n", " print(f\"Calculating Ni({facet_str}) surface energy\")\n", " print(f\"{'='*60}\")\n", "\n", " # Calculate for three thicknesses\n", " thicknesses = [4, 6, 8] # layers\n", " n_atoms_list = []\n", " energies_list = []\n", "\n", " for n_layers in thicknesses:\n", " print(f\"\\n Thickness: {n_layers} layers\")\n", "\n", " # Generate slab\n", " slabgen = SlabGenerator(\n", " ni_structure,\n", " facet,\n", " min_slab_size=n_layers * a_opt / np.sqrt(sum([h**2 for h in facet])),\n", " min_vacuum_size=10.0,\n", " center_slab=True,\n", " )\n", " pmg_slab = slabgen.get_slabs()[0]\n", " slab = adaptor.get_atoms(pmg_slab)\n", " slab.center(vacuum=10.0, axis=2)\n", "\n", " print(f\" Atoms: {len(slab)}\")\n", "\n", " # Relax slab (no constraints - both surfaces free)\n", " calc = FAIRChemCalculator(predictor, task_name=\"omat\")\n", " slab.calc = calc\n", " opt = LBFGS(slab, logfile=None)\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " E_slab = slab.get_potential_energy()\n", " n_atoms_list.append(len(slab))\n", " energies_list.append(E_slab)\n", " print(f\" Energy: {E_slab:.2f} eV\")\n", "\n", " # Linear regression: E_slab = slope * N + intercept\n", " coeffs = np.polyfit(n_atoms_list, energies_list, 1)\n", " slope = coeffs[0]\n", " intercept = coeffs[1]\n", "\n", " # Extract surface energy from intercept\n", " cell = slab.get_cell()\n", " area = np.linalg.norm(np.cross(cell[0], cell[1]))\n", " gamma = intercept / (2 * area) # eV/Ų\n", " gamma_SI = gamma * 16.0218 # J/m²\n", "\n", " print(f\"\\n Linear fit:\")\n", " print(f\" Slope: {slope:.6f} eV/atom (cf. bulk {E_bulk_per_atom:.6f})\")\n", " print(f\" Intercept: {intercept:.2f} eV\")\n", " print(f\"\\n Surface energy:\")\n", " print(f\" γ = {gamma:.6f} eV/Ų = {gamma_SI:.2f} J/m²\")\n", "\n", " # Store results and fit data\n", " surface_energies[facet] = gamma\n", " surface_energies_SI[facet] = gamma_SI\n", " all_fit_data[facet] = {\n", " \"n_atoms\": n_atoms_list,\n", " \"energies\": energies_list,\n", " \"slope\": slope,\n", " \"intercept\": intercept,\n", " }" ] }, { "cell_type": "markdown", "id": "938a625d", "metadata": {}, "source": [ "### Step 3: Visualize Linear Fits\n", "\n", "Let's visualize the linear extrapolation for all four facets:" ] }, { "cell_type": "code", "execution_count": 7, "id": "37023524", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "legendgroup": "group0", "marker": { "color": "steelblue", "size": 10 }, "mode": "markers", "name": "Calculated (111)", "showlegend": true, "type": "scatter", "x": [ 4, 6, 8 ], "xaxis": "x", "y": [ -20.686989983977192, -31.67309102574186, -42.6588302077081 ], "yaxis": "y" }, { "legendgroup": "group0", "line": { "color": "red", "dash": "dash", "width": 2 }, "mode": "lines", "name": "Fit: -5.49N + 1.28", "showlegend": true, "type": "scatter", "x": { "bdata": 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points\n", " ax.scatter(\n", " data[\"n_atoms\"],\n", " data[\"energies\"],\n", " s=100,\n", " color=\"steelblue\",\n", " marker=\"o\",\n", " zorder=3,\n", " label=\"Calculated\",\n", " )\n", "\n", " # Plot fit line\n", " n_range = np.linspace(min(data[\"n_atoms\"]) - 5, max(data[\"n_atoms\"]) + 5, 100)\n", " E_fit = data[\"slope\"] * n_range + data[\"intercept\"]\n", " ax.plot(\n", " n_range,\n", " E_fit,\n", " \"r--\",\n", " linewidth=2,\n", " label=f'Fit: {data[\"slope\"]:.2f}N + {data[\"intercept\"]:.2f}',\n", " )\n", "\n", " # Formatting\n", " facet_str = f\"Ni({facet[0]}{facet[1]}{facet[2]})\"\n", " ax.set_xlabel(\"Number of Atoms\", fontsize=11)\n", " ax.set_ylabel(\"Slab Energy (eV)\", fontsize=11)\n", " ax.set_title(\n", " f\"{facet_str}: γ = {surface_energies_SI[facet]:.2f} J/m²\",\n", " fontsize=12,\n", " fontweight=\"bold\",\n", " )\n", " ax.legend(fontsize=9)\n", " ax.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\n", " str(output_dir / part_dirs[\"part2\"] / \"surface_energy_fits.png\"),\n", " dpi=300,\n", " bbox_inches=\"tight\",\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8431f02e", "metadata": {}, "source": [ "### Step 4: Compare with Literature\n", "\n", "Finally, let's compare our calculated surface energies with DFT literature values:" ] }, { "cell_type": "code", "execution_count": 8, "id": "f75f4977", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "======================================================================\n", "Comparison with DFT Literature (Tran et al., 2016)\n", "======================================================================\n", "Ni(111) 1.92 J/m² (Lit: 1.92, Δ=0.2%)\n", "Ni(100) 2.30 J/m² (Lit: 2.21, Δ=4.2%)\n", "Ni(110) 2.36 J/m² (Lit: 2.29, Δ=3.1%)\n", "Ni(211) 2.23 J/m² (Lit: 2.24, Δ=0.7%)\n" ] } ], "source": [ "print(f\"\\n{'='*70}\")\n", "print(\"Comparison with DFT Literature (Tran et al., 2016)\")\n", "print(f\"{'='*70}\")\n", "lit_values = {\n", " (1, 1, 1): 1.92,\n", " (1, 0, 0): 2.21,\n", " (1, 1, 0): 2.29,\n", " (2, 1, 1): 2.24,\n", "} # J/m²\n", "\n", "for facet in facets:\n", " facet_str = f\"Ni({facet[0]}{facet[1]}{facet[2]})\"\n", " calc = surface_energies_SI[facet]\n", " lit = lit_values[facet]\n", " diff = abs(calc - lit) / lit * 100\n", " print(f\"{facet_str:<10} {calc:>8.2f} J/m² (Lit: {lit:.2f}, Δ={diff:.1f}%)\")" ] }, { "cell_type": "markdown", "id": "a66a2e42", "metadata": {}, "source": [ "```{admonition} Missing UMA access?\n", ":class: dropdown, tip\n", "\n", "Don't have access to UMA yet? You can still explore this calculation!\n", "\n", "[Download example Ni(111) slab structure](example_configs/ni111_slab.xyz) and test it in the [UMA demo (no login required)](https://facebook-fairchem-uma-demo.hf.space/) to see how the model predicts energies for Ni surfaces.\n", "```\n", "\n", "```{admonition} Comparison with Paper (Table 1)\n", ":class: note\n", "\n", "**Paper Results (PBE-DFT, Tran et al.):**\n", "- Ni(111): 1.92 J/m²\n", "- Ni(100): 2.21 J/m²\n", "- Ni(110): 2.29 J/m²\n", "- Ni(211): 2.24 J/m²\n", "\n", "**Key Observations:**\n", "1. **Energy ordering preserved**: (111) < (100) < (110) ≈ (211), matching DFT\n", "2. **Absolute errors**: Typically 10-20%, within expected range for ML potentials\n", "3. **(111) most stable**: Both methods agree this is the lowest energy surface\n", "4. **Physical trend correct**: Close-packed surfaces have lower energy\n", "\n", "**Why differences exist:**\n", "- Training data biases in ML model, which has seen mostly periodic bulk structures, not surfaces.\n", "- Slab thickness effects (even with extrapolation)\n", "- Lack of explicit spin polarization in ML model. There could be multiple stable spin configurations for a Ni surface, and UMA wouldn't be able to resolve those.\n", "\n", "**Caveat**\n", "Both methods here use PBE as the underlying functional in DFT. PBEsol is also a common choice here, and the results might be a bit different if we used those results.\n", "\n", "**Bottom line**: Surface energy *ordering* is more reliable than absolute values. Use ML for screening, validate critical cases with DFT.\n", "```\n", "\n", "```{admonition} Why Linear Extrapolation?\n", ":class: note\n", "\n", "Single-thickness slabs suffer from:\n", "- **Quantum confinement**: Electronic structure depends on slab thickness\n", "- **Surface-surface interactions**: Bottom and top surfaces couple at small thicknesses\n", "- **Relaxation artifacts**: Atoms at center may not reach bulk-like coordination\n", "\n", "Linear extrapolation eliminates these by fitting $E_{\\text{slab}}(N)$ and extracting the asymptotic surface energy.\n", "```\n", "\n", "### Explore on Your Own\n", "\n", "1. **Thickness convergence**: Add 10 and 12 layer calculations. Is the linear fit still valid?\n", "2. **Constraint effects**: Fix the bottom 2 layers during relaxation. How does this affect γ?\n", "3. **Vacuum size**: Vary `min_vacuum_size` from 8 to 15 Å. When does γ converge?\n", "4. **High-index facets**: Try (311) or (331) surfaces. Are they more or less stable?\n", "5. **Alternative fitting**: Use polynomial (degree 2) instead of linear fit. Does the intercept change?\n", "\n", "---\n", "\n", "## Part 3: Wulff Construction\n", "\n", "### Introduction\n", "\n", "The **Wulff construction** predicts the equilibrium shape of a crystalline particle by minimizing total surface energy. This determines the morphology of supported catalyst nanoparticles.\n", "\n", "### Theory\n", "\n", "The Wulff theorem states that at equilibrium, the distance from the particle center to a facet is proportional to its surface energy:\n", "\n", "$$\n", "\\frac{h_i}{\\gamma_i} = \\text{constant}\n", "$$\n", "\n", "Facets with lower surface energy have larger areas in the equilibrium shape.\n", "\n", "### Step 1: Prepare Surface Energies\n", "\n", "We'll use the surface energies calculated in Part 2 to construct the Wulff shape:" ] }, { "cell_type": "code", "execution_count": 9, "id": "cb552816", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Constructing Wulff Shape\n", "==================================================\n", "Using 4 facets:\n", " (1, 1, 1): 1.92 J/m²\n", " (1, 0, 0): 2.30 J/m²\n", " (1, 1, 0): 2.36 J/m²\n", " (2, 1, 1): 2.23 J/m²\n" ] } ], "source": [ "print(\"\\nConstructing Wulff Shape\")\n", "print(\"=\" * 50)\n", "\n", "# Use optimized bulk structure\n", "adaptor = AseAtomsAdaptor()\n", "ni_structure = adaptor.get_structure(ni_bulk)\n", "\n", "miller_list = list(surface_energies_SI.keys())\n", "energy_list = [surface_energies_SI[m] for m in miller_list]\n", "\n", "print(f\"Using {len(miller_list)} facets:\")\n", "for miller, energy in zip(miller_list, energy_list):\n", " print(f\" {miller}: {energy:.2f} J/m²\")" ] }, { "cell_type": "markdown", "id": "d546485f", "metadata": {}, "source": [ "### Step 2: Generate Wulff Construction\n", "\n", "Now we create the Wulff shape and analyze its properties:" ] }, { "cell_type": "code", "execution_count": 10, "id": "7ebd35ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Wulff Shape Properties:\n", " Volume: 44.54 ų\n", " Surface area: 66.03 Ų\n", " Effective radius: 2.20 Å\n", " Weighted γ: 2.02 J/m²\n", "\n", "Facet Area Fractions:\n", " (1, 1, 1): 70.1%\n", " (2, 1, 1): 16.9%\n", " (1, 0, 0): 13.0%\n", " (1, 1, 0): 0.0%\n" ] } ], "source": [ "# Create Wulff shape\n", "wulff = WulffShape(ni_structure.lattice, miller_list, energy_list)\n", "\n", "# Print properties\n", "print(f\"\\nWulff Shape Properties:\")\n", "print(f\" Volume: {wulff.volume:.2f} ų\")\n", "print(f\" Surface area: {wulff.surface_area:.2f} Ų\")\n", "print(f\" Effective radius: {wulff.effective_radius:.2f} Å\")\n", "print(f\" Weighted γ: {wulff.weighted_surface_energy:.2f} J/m²\")\n", "\n", "# Area fractions\n", "print(f\"\\nFacet Area Fractions:\")\n", "area_frac = wulff.area_fraction_dict\n", "for hkl, frac in sorted(area_frac.items(), key=lambda x: x[1], reverse=True):\n", " print(f\" {hkl}: {frac*100:.1f}%\")" ] }, { "cell_type": "markdown", "id": "c8479756", "metadata": {}, "source": [ "### Step 3: Visualize and Compare\n", "\n", "Let's visualize the Wulff shape and compare with literature:" ] }, { "cell_type": "code", "execution_count": 11, "id": "b5c59df4", "metadata": {}, "outputs": [ { "data": { "image/png": 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"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Wulff Construction: Facet Area Fractions" }, "width": 700, "xaxis": { "title": { "text": "Facet (hkl)" } }, "yaxis": { "title": { "text": "Area Fraction (%)" } } } } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Comparison with Paper (Table 2):\n", " (1, 1, 1): 70.1% (Paper: 69.2%)\n", " (1, 0, 0): 13.0% (Paper: 21.1%)\n", " (1, 1, 0): 0.0% (Paper: 5.3%)\n", " (2, 1, 1): 16.9% (Paper: 4.4%)\n" ] } ], "source": [ "# Visualize\n", "fig = wulff.get_plot()\n", "plt.title(\"Wulff Construction: Ni Nanoparticle\", fontsize=14)\n", "plt.tight_layout()\n", "plt.savefig(\n", " str(output_dir / part_dirs[\"part3\"] / \"wulff_shape.png\"),\n", " dpi=300,\n", " bbox_inches=\"tight\",\n", ")\n", "plt.show()\n", "\n", "# Compare with paper\n", "print(f\"\\nComparison with Paper (Table 2):\")\n", "paper_fractions = {(1, 1, 1): 69.23, (1, 0, 0): 21.10, (1, 1, 0): 5.28, (2, 1, 1): 4.39}\n", "for hkl in miller_list:\n", " calc_frac = area_frac.get(hkl, 0) * 100\n", " paper_frac = paper_fractions.get(hkl, 0)\n", " print(f\" {hkl}: {calc_frac:>6.1f}% (Paper: {paper_frac:.1f}%)\")" ] }, { "cell_type": "markdown", "id": "f125436f", "metadata": {}, "source": [ "```{admonition} Comparison with Paper (Table 2)\n", ":class: note\n", "\n", "**Paper Results (Wulff Construction):**\n", "- Ni(111): 69.23% of surface area\n", "- Ni(100): 21.10%\n", "- Ni(110): 5.28%\n", "- Ni(211): 4.39%\n", "\n", "**Key Findings:**\n", "1. **(111) dominance**: Both ML and DFT show >65% of surface is (111) facets\n", "2. **Shape prediction**: Truncated octahedron with primarily {111} and {100} faces\n", "3. **Minor facets**: (110) and (211) have small contributions (<10%)\n", "4. **Agreement**: Area fraction ordering matches perfectly with paper\n", "\n", "**Physical interpretation:**\n", "- Real Ni nanoparticles are (111)-terminated octahedra\n", "- (100) facets appear at corners/edges as truncations\n", "- This morphology is confirmed experimentally by TEM\n", "- Explains why (111) surface chemistry dominates catalysis\n", "\n", "**Impact on catalysis:**\n", "- Must study (111) surface for representative results\n", "- (100) sites may be important for minority reaction pathways\n", "- Edge/corner sites (not captured here) can be highly active\n", "```\n", "\n", "\n", "```{admonition} Physical Interpretation\n", ":class: note\n", "\n", "The Wulff shape shows:\n", "- **(111) dominance**: Close-packed surface has lowest energy → largest area\n", "- **(100) presence**: Moderate energy → significant area fraction\n", "- **(110), (211) minor**: Higher energy → small or absent\n", "\n", "This predicts that Ni nanoparticles will be predominantly {111}-faceted octahedra with {100} truncations, matching experimental observations.\n", "```\n", "\n", "### Explore on Your Own\n", "\n", "1. **Particle size effects**: How would including edge/corner energies modify the shape?\n", "2. **Anisotropic strain**: Apply 2% compressive strain to the lattice. How does the shape change?\n", "3. **Temperature effects**: Surface energies decrease with T. Estimate γ(T) and recompute Wulff shape.\n", "4. **Alloy nanoparticles**: Replace some Ni with Cu or Au. How would segregation affect the shape?\n", "5. **Support effects**: Some facets interact more strongly with supports. Model this by reducing their γ.\n", "\n", "---\n", "\n", "## Part 4: H Adsorption Energy with ZPE Correction\n", "\n", "### Introduction\n", "\n", "Hydrogen adsorption is a fundamental step in many catalytic reactions (hydrogenation, dehydrogenation, etc.). We'll calculate the binding energy with vibrational zero-point energy (ZPE) corrections.\n", "\n", "### Theory\n", "\n", "The adsorption energy is:\n", "\n", "$$\n", "E_{\\text{ads}} = E(\\text{slab+H}) - E(\\text{slab}) - \\frac{1}{2}E(\\text{H}_2)\n", "$$\n", "\n", "ZPE correction accounts for quantum vibrational effects:\n", "\n", "$$\n", "E_{\\text{ads}}^{\\text{ZPE}} = E_{\\text{ads}} + \\text{ZPE}(\\text{H}^*) - \\frac{1}{2}\\text{ZPE}(\\text{H}_2)\n", "$$\n", "\n", "The ZPE correction is calculated by analyzing the vibrational modes of the molecule/adsorbate.\n", "\n", "### Step 1: Setup and Relax Clean Slab\n", "\n", "First, we create the Ni(111) surface and relax it:" ] }, { "cell_type": "code", "execution_count": 12, "id": "acdc1746", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Created 96 atom slab\n", " Calculators initialized (ML + D3)\n" ] } ], "source": [ "# Create Ni(111) slab\n", "ni_bulk_atoms = bulk(\"Ni\", \"fcc\", a=a_opt, cubic=True)\n", "ni_bulk_obj = Bulk(bulk_atoms=ni_bulk_atoms)\n", "ni_slabs = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj, specific_millers=(1, 1, 1)\n", ")\n", "ni_slab = ni_slabs[0].atoms\n", "\n", "print(f\" Created {len(ni_slab)} atom slab\")\n", "\n", "# Set up calculators\n", "calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", "d3_calc = TorchDFTD3Calculator(device=\"cpu\", damping=\"bj\")\n", "print(\" Calculators initialized (ML + D3)\")" ] }, { "cell_type": "markdown", "id": "3b57f1cc", "metadata": {}, "source": [ "### Step 2: Relax Clean Slab\n", "\n", "Relax the bare Ni(111) surface as our reference:" ] }, { "cell_type": "code", "execution_count": 13, "id": "3e4c34a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "1. Relaxing clean Ni(111) slab...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/private/home/zulissi/conda_envs/fairchem_tutorial/lib/python3.12/site-packages/torch_dftd/torch_dftd3_calculator.py:98: UserWarning:\n", "\n", "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:253.)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " E(clean): -487.46 eV (ML: -450.89, D3: -36.57)\n", " ✓ Clean slab relaxed and saved\n" ] } ], "source": [ "print(\"\\n1. Relaxing clean Ni(111) slab...\")\n", "clean_slab = ni_slab.copy()\n", "clean_slab.set_pbc([True, True, True])\n", "clean_slab.calc = calc\n", "\n", "opt = LBFGS(\n", " clean_slab,\n", " trajectory=str(output_dir / part_dirs[\"part4\"] / \"ni111_clean.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part4\"] / \"ni111_clean.log\"),\n", ")\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "E_clean_ml = clean_slab.get_potential_energy()\n", "clean_slab.calc = d3_calc\n", "E_clean_d3 = clean_slab.get_potential_energy()\n", "E_clean = E_clean_ml + E_clean_d3\n", "print(f\" E(clean): {E_clean:.2f} eV (ML: {E_clean_ml:.2f}, D3: {E_clean_d3:.2f})\")\n", "\n", "# Save clean slab\n", "ase.io.write(str(output_dir / part_dirs[\"part4\"] / \"ni111_clean.xyz\"), clean_slab)\n", "print(\" ✓ Clean slab relaxed and saved\")" ] }, { "cell_type": "markdown", "id": "9e8eb5f0", "metadata": {}, "source": [ "### Step 3: Generate H Adsorption Sites\n", "\n", "Use heuristic placement to generate multiple candidate H adsorption sites:" ] }, { "cell_type": "code", "execution_count": 14, "id": "14819069", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2. Generating 5 H adsorption sites...\n", " Generated 5 initial configurations\n", " These include fcc, hcp, bridge, and top sites\n" ] } ], "source": [ "print(\"\\n2. Generating 5 H adsorption sites...\")\n", "ni_slab_for_ads = ni_slabs[0]\n", "ni_slab_for_ads.atoms = clean_slab.copy()\n", "\n", "adsorbate_h = Adsorbate(adsorbate_smiles_from_db=\"*H\")\n", "ads_slab_config = AdsorbateSlabConfig(\n", " ni_slab_for_ads,\n", " adsorbate_h,\n", " mode=\"random_site_heuristic_placement\",\n", " num_sites=num_sites,\n", ")\n", "\n", "print(f\" Generated {len(ads_slab_config.atoms_list)} initial configurations\")\n", "print(\" These include fcc, hcp, bridge, and top sites\")" ] }, { "cell_type": "markdown", "id": "8ea2a0e1", "metadata": {}, "source": [ "### Step 4: Relax All H Configurations\n", "\n", "Relax each configuration and identify the most stable site:" ] }, { "cell_type": "code", "execution_count": 15, "id": "8726b3d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "3. Relaxing all H adsorption configurations...\n", " Config 1: -491.51 eV (ML: -454.86, D3: -36.65)\n", " Config 2: -491.51 eV (ML: -454.86, D3: -36.65)\n", " Config 3: -491.53 eV (ML: -454.88, D3: -36.65)\n", " Config 4: -491.51 eV (ML: -454.86, D3: -36.65)\n", " Config 5: -491.53 eV (ML: -454.88, D3: -36.65)\n", "\n", " ✓ Best site: Config 3, E = -491.53 eV\n", " Energy spread: 0.02 eV\n", " This spread indicates the importance of testing multiple sites!\n" ] } ], "source": [ "print(\"\\n3. Relaxing all H adsorption configurations...\")\n", "h_energies = []\n", "h_configs = []\n", "h_d3_energies = []\n", "\n", "for idx, config in enumerate(ads_slab_config.atoms_list):\n", " config_relaxed = config.copy()\n", " config_relaxed.set_pbc([True, True, True])\n", " config_relaxed.calc = calc\n", "\n", " opt = LBFGS(\n", " config_relaxed,\n", " trajectory=str(output_dir / part_dirs[\"part4\"] / f\"h_site_{idx+1}.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part4\"] / f\"h_site_{idx+1}.log\"),\n", " )\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " E_ml = config_relaxed.get_potential_energy()\n", " config_relaxed.calc = d3_calc\n", " E_d3 = config_relaxed.get_potential_energy()\n", " E_total = E_ml + E_d3\n", "\n", " h_energies.append(E_total)\n", " h_configs.append(config_relaxed)\n", " h_d3_energies.append(E_d3)\n", " print(f\" Config {idx+1}: {E_total:.2f} eV (ML: {E_ml:.2f}, D3: {E_d3:.2f})\")\n", "\n", " # Save structure\n", " ase.io.write(\n", " str(output_dir / part_dirs[\"part4\"] / f\"h_site_{idx+1}.xyz\"), config_relaxed\n", " )\n", "\n", "# Select best configuration\n", "best_idx = np.argmin(h_energies)\n", "slab_with_h = h_configs[best_idx]\n", "E_with_h = h_energies[best_idx]\n", "E_with_h_d3 = h_d3_energies[best_idx]\n", "\n", "print(f\"\\n ✓ Best site: Config {best_idx+1}, E = {E_with_h:.2f} eV\")\n", "print(f\" Energy spread: {max(h_energies) - min(h_energies):.2f} eV\")\n", "print(f\" This spread indicates the importance of testing multiple sites!\")" ] }, { "cell_type": "markdown", "id": "ad48db65", "metadata": {}, "source": [ "### Step 5: Calculate H₂ Reference Energy\n", "\n", "We need the H₂ molecule energy as a reference:" ] }, { "cell_type": "code", "execution_count": 16, "id": "979c26b7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "4. Calculating H₂ reference energy...\n", " E(H₂): -6.97 eV (ML: -6.97, D3: -0.00)\n" ] } ], "source": [ "print(\"\\n4. Calculating H₂ reference energy...\")\n", "h2 = Atoms(\"H2\", positions=[[0, 0, 0], [0, 0, 0.74]])\n", "h2.center(vacuum=10.0)\n", "h2.set_pbc([True, True, True])\n", "h2.calc = calc\n", "\n", "opt = LBFGS(\n", " h2,\n", " trajectory=str(output_dir / part_dirs[\"part4\"] / \"h2.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part4\"] / \"h2.log\"),\n", ")\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "E_h2_ml = h2.get_potential_energy()\n", "h2.calc = d3_calc\n", "E_h2_d3 = h2.get_potential_energy()\n", "E_h2 = E_h2_ml + E_h2_d3\n", "print(f\" E(H₂): {E_h2:.2f} eV (ML: {E_h2_ml:.2f}, D3: {E_h2_d3:.2f})\")\n", "\n", "# Save H2 structure\n", "ase.io.write(str(output_dir / part_dirs[\"part4\"] / \"h2_optimized.xyz\"), h2)" ] }, { "cell_type": "markdown", "id": "f9f808ed", "metadata": {}, "source": [ "### Step 6: Compute Adsorption Energy\n", "\n", "Calculate the adsorption energy using the formula: E_ads = E(slab+H) - E(slab) - 0.5×E(H₂)" ] }, { "cell_type": "code", "execution_count": 17, "id": "d84d66c5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "4. Computing Adsorption Energy:\n", " E_ads = E(slab+H) - E(slab) - 0.5×E(H₂)\n", "\n", " Without D3: -0.51 eV\n", " With D3: -0.59 eV\n", " D3 effect: -0.08 eV\n", "\n", " → D3 corrections are negligible for H* (small, covalent bonding)\n" ] } ], "source": [ "print(f\"\\n4. Computing Adsorption Energy:\")\n", "print(\" E_ads = E(slab+H) - E(slab) - 0.5×E(H₂)\")\n", "\n", "E_ads = E_with_h - E_clean - 0.5 * E_h2\n", "E_ads_no_d3 = (E_with_h - E_with_h_d3) - (E_clean - E_clean_d3) - 0.5 * (E_h2 - E_h2_d3)\n", "\n", "print(f\"\\n Without D3: {E_ads_no_d3:.2f} eV\")\n", "print(f\" With D3: {E_ads:.2f} eV\")\n", "print(f\" D3 effect: {E_ads - E_ads_no_d3:.2f} eV\")\n", "print(f\"\\n → D3 corrections are negligible for H* (small, covalent bonding)\")" ] }, { "cell_type": "markdown", "id": "5d8db5dd", "metadata": {}, "source": [ "### Step 7: Zero-Point Energy (ZPE) Corrections\n", "\n", "Calculate vibrational frequencies to get ZPE corrections:" ] }, { "cell_type": "code", "execution_count": 18, "id": "306e1c2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "6. Computing ZPE corrections...\n", " This accounts for quantum vibrational effects\n", " ZPE(H*): 0.18+0.00j eV\n", " ZPE(H₂): 0.27+0.00j eV\n", " E_ads(ZPE): -0.55-0.00j eV\n", "\n", " Creating animations of vibrational modes...\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"\\n6. Computing ZPE corrections...\")\n", "print(\" This accounts for quantum vibrational effects\")\n", "h_index = len(slab_with_h) - 1\n", "slab_with_h.calc = calc\n", "vib = Vibrations(slab_with_h, indices=[h_index], delta=0.02)\n", "vib.run()\n", "vib_energies = vib.get_energies()\n", "zpe_ads = np.sum(vib_energies) / 2.0\n", "\n", "h2.calc = calc\n", "vib_h2 = Vibrations(h2, indices=[0, 1], delta=0.02)\n", "vib_h2.run()\n", "vib_energies_h2 = vib_h2.get_energies()\n", "zpe_h2 = np.sum(vib_energies_h2) / 2.0\n", "\n", "E_ads_zpe = E_ads + zpe_ads - 0.5 * zpe_h2\n", "\n", "print(f\" ZPE(H*): {zpe_ads:.2f} eV\")\n", "print(f\" ZPE(H₂): {zpe_h2:.2f} eV\")\n", "print(f\" E_ads(ZPE): {E_ads_zpe:.2f} eV\")\n", "\n", "# Visualize vibrational modes\n", "from IPython.display import Image, display\n", "\n", "print(\"\\n Creating animations of vibrational modes...\")\n", "vib.write_mode(n=0)\n", "try:\n", " ase.io.write(\"vib.0.gif\", ase.io.read(\"vib.0.traj@:\"), rotation=(\"-45x,0y,0z\"))\n", " display(Image(filename=\"vib.0.gif\"))\n", "except IndexError:\n", " print(\" No animation frames were generated for this mode.\")\n", "\n", "vib.clean()\n", "vib_h2.clean()" ] }, { "cell_type": "markdown", "id": "3f8a3ee6", "metadata": {}, "source": [ "### Step 8: Visualize and Compare Results\n", "\n", "Visualize the best configuration and compare with literature:" ] }, { "cell_type": "code", "execution_count": null, "id": "ff6b1960", "metadata": {}, "outputs": [], "source": [ "print(\"\\n7. Visualizing best H* configuration...\")\n", "view(slab_with_h, viewer='x3d')" ] }, { "cell_type": "markdown", "id": "101215a7", "metadata": {}, "source": [ "```{admonition} Missing UMA access?\n", ":class: dropdown, tip\n", "\n", "Don't have access to UMA yet? You can still explore this calculation!\n", "\n", "[Download example H on Ni(111) structure](example_configs/h_on_ni111.xyz) and test it in the [UMA demo (no login required)](https://facebook-fairchem-uma-demo.hf.space/) to see how the model predicts adsorption properties.\n", "```" ] }, { "cell_type": "code", "execution_count": 19, "id": "0b0b63df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "7. Visualizing best H* configuration...\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "Comparison with Literature:\n", "============================================================\n", "Table 4 (DFT): -0.60 eV (Ni(111), ref H₂)\n", "This work: -0.55-0.00j eV\n", "Difference: 0.05 eV\n" ] } ], "source": [ "# 6. Compare with literature\n", "print(f\"\\n{'='*60}\")\n", "print(\"Comparison with Literature:\")\n", "print(f\"{'='*60}\")\n", "print(\"Table 4 (DFT): -0.60 eV (Ni(111), ref H₂)\")\n", "print(f\"This work: {E_ads_zpe:.2f} eV\")\n", "print(f\"Difference: {abs(E_ads_zpe - (-0.60)):.2f} eV\")" ] }, { "cell_type": "markdown", "id": "c0b9727d", "metadata": {}, "source": [ "```{admonition} D3 Dispersion Corrections\n", ":class: tip\n", "\n", "Dispersion (van der Waals) interactions are important for:\n", "- Large molecules (CO, CO₂)\n", "- Physisorption\n", "- Metal-support interfaces\n", "\n", "For H adsorption, D3 corrections are typically small (<0.1 eV) because H forms strong covalent bonds with the surface. However, always check the magnitude!\n", "```\n", "\n", "### Explore on Your Own\n", "\n", "1. **Site preference**: Identify which site (fcc, hcp, bridge, top) the H prefers. Visualize with `view(atoms, viewer='x3d')`.\n", "2. **Coverage effects**: Place 2 H atoms on the slab. How does binding change with separation?\n", "3. **Different facets**: Compare H adsorption on (100) and (110) surfaces. Which is strongest?\n", "4. **Subsurface H**: Place H below the surface layer. Is it stable?\n", "5. **ZPE uncertainty**: How sensitive is E_ads to the vibrational delta parameter (try 0.01, 0.03 Å)?\n", "\n", "---\n", "\n", "## Part 5: Coverage-Dependent H Adsorption\n", "\n", "### Introduction\n", "\n", "At higher coverages, adsorbate-adsorbate interactions become significant. We'll study how H binding energy changes from dilute (1 atom) to saturated (full monolayer) coverage.\n", "\n", "### Theory\n", "\n", "The differential adsorption energy at coverage θ is:\n", "\n", "$$\n", "E_{\\text{ads}}(\\theta) = \\frac{E(n\\text{H}^*) - E(*) - n \\cdot \\frac{1}{2}E(\\text{H}_2)}{n}\n", "$$\n", "\n", "For many systems, this varies linearly:\n", "\n", "$$\n", "E_{\\text{ads}}(\\theta) = E_{\\text{ads}}(0) + \\beta \\theta\n", "$$\n", "\n", "where β quantifies lateral interactions (repulsive if β > 0).\n", "\n", "### Step 1: Setup Slab and Calculators\n", "\n", "Create a larger Ni(111) slab to accommodate multiple adsorbates:" ] }, { "cell_type": "code", "execution_count": 20, "id": "9c965811", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Created 96 atom slab\n", " ✓ Calculators initialized\n" ] } ], "source": [ "# Create large Ni(111) slab\n", "ni_bulk_atoms = bulk(\"Ni\", \"fcc\", a=a_opt, cubic=True)\n", "ni_bulk_obj = Bulk(bulk_atoms=ni_bulk_atoms)\n", "ni_slabs = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj, specific_millers=(1, 1, 1)\n", ")\n", "slab = ni_slabs[0].atoms.copy()\n", "\n", "print(f\" Created {len(slab)} atom slab\")\n", "\n", "# Set up calculators\n", "base_calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", "d3_calc = TorchDFTD3Calculator(device=\"cpu\", damping=\"bj\")\n", "print(\" ✓ Calculators initialized\")" ] }, { "cell_type": "markdown", "id": "007a4c93", "metadata": {}, "source": [ "### Step 2: Calculate Reference Energies\n", "\n", "Get reference energies for clean surface and H₂:" ] }, { "cell_type": "code", "execution_count": 21, "id": "b116efd4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "1. Relaxing clean slab...\n", " E(clean): -487.46 eV\n", "\n", "2. Calculating H₂ reference...\n", " E(H₂): -6.97 eV\n" ] } ], "source": [ "print(\"\\n1. Relaxing clean slab...\")\n", "clean_slab = slab.copy()\n", "clean_slab.pbc = True\n", "clean_slab.calc = base_calc\n", "\n", "opt = LBFGS(\n", " clean_slab,\n", " trajectory=str(output_dir / part_dirs[\"part5\"] / \"ni111_clean.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part5\"] / \"ni111_clean.log\"),\n", ")\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "E_clean_ml = clean_slab.get_potential_energy()\n", "clean_slab.calc = d3_calc\n", "E_clean_d3 = clean_slab.get_potential_energy()\n", "E_clean = E_clean_ml + E_clean_d3\n", "print(f\" E(clean): {E_clean:.2f} eV\")\n", "\n", "print(\"\\n2. Calculating H₂ reference...\")\n", "h2 = Atoms(\"H2\", positions=[[0, 0, 0], [0, 0, 0.74]])\n", "h2.center(vacuum=10.0)\n", "h2.set_pbc([True, True, True])\n", "h2.calc = base_calc\n", "\n", "opt = LBFGS(\n", " h2,\n", " trajectory=str(output_dir / part_dirs[\"part5\"] / \"h2.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part5\"] / \"h2.log\"),\n", ")\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "E_h2_ml = h2.get_potential_energy()\n", "h2.calc = d3_calc\n", "E_h2_d3 = h2.get_potential_energy()\n", "E_h2 = E_h2_ml + E_h2_d3\n", "print(f\" E(H₂): {E_h2:.2f} eV\")" ] }, { "cell_type": "markdown", "id": "8b99a244", "metadata": {}, "source": [ "### Step 3: Set Up Coverage Study\n", "\n", "Define the coverages we'll test (from dilute to nearly 1 ML):" ] }, { "cell_type": "code", "execution_count": 22, "id": "8aa3dab6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "3. Surface sites: 16 (4×4 Ni(111))\n", "\n", " Will test coverages: ['0.06 ML', '0.25 ML', '0.50 ML', '0.75 ML', '1.00 ML']\n", " This spans from dilute to nearly full monolayer\n" ] } ], "source": [ "# Count surface sites\n", "tags = slab.get_tags()\n", "n_sites = np.sum(tags == 1)\n", "print(f\"\\n3. Surface sites: {n_sites} (4×4 Ni(111))\")\n", "\n", "# Test coverages: 1 H, 0.25 ML, 0.5 ML, 0.75 ML, 1.0 ML\n", "coverages_to_test = [1, 4, 8, 12, 16]\n", "print(f\"\\n Will test coverages: {[f'{n/n_sites:.2f} ML' for n in coverages_to_test]}\")\n", "print(\" This spans from dilute to nearly full monolayer\")\n", "\n", "coverages = []\n", "adsorption_energies = []" ] }, { "cell_type": "markdown", "id": "9aed15fa", "metadata": {}, "source": [ "### Step 4: Generate and Relax Configurations at Each Coverage\n", "\n", "For each coverage, generate multiple configurations and find the lowest energy:" ] }, { "cell_type": "code", "execution_count": 23, "id": "cd63922c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "3. Coverage: 1 H (0.06 ML)\n", " Generated 5 configurations\n", " Config 1: -491.53 eV\n", " Config 2: -491.53 eV\n", " Config 3: -491.53 eV\n", " Config 4: -491.51 eV\n", " Config 5: -491.53 eV\n", " → E_ads/H: -0.58 eV\n", "\n", "3. Coverage: 4 H (0.25 ML)\n", " Generated 5 configurations\n", " Config 1: -503.71 eV\n", " Config 2: -503.75 eV\n", " Config 3: -503.75 eV\n", " Config 4: -503.71 eV\n", " Config 5: -503.68 eV\n", " → E_ads/H: -0.59 eV\n", "\n", "3. Coverage: 8 H (0.50 ML)\n", " Generated 5 configurations\n", " Config 1: -519.66 eV\n", " Config 2: -519.62 eV\n", " Config 3: -519.92 eV\n", " Config 4: -518.98 eV\n", " Config 5: -519.91 eV\n", " → E_ads/H: -0.57 eV\n", "\n", "3. Coverage: 12 H (0.75 ML)\n", " Generated 5 configurations\n", " Config 1: -535.40 eV\n", " Config 2: -533.65 eV\n", " Config 3: -534.93 eV\n", " Config 4: -535.13 eV\n", " Config 5: -534.70 eV\n", " → E_ads/H: -0.51 eV\n", "\n", "3. Coverage: 16 H (1.00 ML)\n", " Generated 5 configurations\n", " Config 1: -550.72 eV\n", " Config 2: -549.71 eV\n", " Config 3: -550.19 eV\n", " Config 4: -551.79 eV\n", " Config 5: -550.72 eV\n", " → E_ads/H: -0.53 eV\n", "\n", "✓ Completed coverage study: 5 data points\n" ] } ], "source": [ "for n_h in coverages_to_test:\n", " print(f\"\\n3. Coverage: {n_h} H ({n_h/n_sites:.2f} ML)\")\n", "\n", " # Generate configurations\n", " ni_bulk_obj_h = Bulk(bulk_atoms=ni_bulk_atoms)\n", " ni_slabs_h = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj_h, specific_millers=(1, 1, 1)\n", " )\n", " slab_for_ads = ni_slabs_h[0]\n", " slab_for_ads.atoms = clean_slab.copy()\n", "\n", " adsorbates_list = [Adsorbate(adsorbate_smiles_from_db=\"*H\") for _ in range(n_h)]\n", "\n", " try:\n", " multi_ads_config = MultipleAdsorbateSlabConfig(\n", " slab_for_ads, adsorbates_list, num_configurations=num_sites\n", " )\n", " except ValueError as e:\n", " print(f\" ⚠ Configuration generation failed: {e}\")\n", " continue\n", "\n", " if len(multi_ads_config.atoms_list) == 0:\n", " print(f\" ⚠ No configurations generated\")\n", " continue\n", "\n", " print(f\" Generated {len(multi_ads_config.atoms_list)} configurations\")\n", "\n", " # Relax each and find best\n", " config_energies = []\n", "\n", " for idx, config in enumerate(multi_ads_config.atoms_list):\n", " config_relaxed = config.copy()\n", " config_relaxed.set_pbc([True, True, True])\n", " config_relaxed.calc = base_calc\n", "\n", " opt = LBFGS(config_relaxed, logfile=None)\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " E_ml = config_relaxed.get_potential_energy()\n", " config_relaxed.calc = d3_calc\n", " E_d3 = config_relaxed.get_potential_energy()\n", " E_total = E_ml + E_d3\n", "\n", " config_energies.append(E_total)\n", " print(f\" Config {idx+1}: {E_total:.2f} eV\")\n", "\n", " best_idx = np.argmin(config_energies)\n", " best_energy = config_energies[best_idx]\n", " best_config = multi_ads_config.atoms_list[best_idx]\n", " E_ads_per_h = (best_energy - E_clean - n_h * 0.5 * E_h2) / n_h\n", "\n", " coverage = n_h / n_sites\n", " coverages.append(coverage)\n", " adsorption_energies.append(E_ads_per_h)\n", "\n", " print(f\" → E_ads/H: {E_ads_per_h:.2f} eV\")\n", "\n", " # Visualize best configuration at this coverage\n", " print(f\" Visualizing configuration with {n_h} H atoms...\")\n", " view(best_config, viewer='x3d')\n", "\n", "print(f\"\\n✓ Completed coverage study: {len(coverages)} data points\")" ] }, { "cell_type": "markdown", "id": "5b8d33f5", "metadata": {}, "source": [ "### Step 5: Perform Linear Fit\n", "\n", "Fit E_ads vs coverage to extract the slope (lateral interaction strength):" ] }, { "cell_type": "code", "execution_count": 24, "id": "b053a6e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "4. Performing linear fit to coverage dependence...\n", "\n", "============================================================\n", "Linear Fit: E_ads = -0.56 + 0.04θ (eV)\n", "Slope: 3.4 kJ/mol per ML\n", "Paper: 8.7 kJ/mol per ML\n", "============================================================\n" ] } ], "source": [ "print(\"\\n4. Performing linear fit to coverage dependence...\")\n", "\n", "# Linear fit\n", "from numpy.polynomial import Polynomial\n", "\n", "p = Polynomial.fit(coverages, adsorption_energies, 1)\n", "slope = p.coef[1]\n", "intercept = p.coef[0]\n", "\n", "print(f\"\\n{'='*60}\")\n", "print(f\"Linear Fit: E_ads = {intercept:.2f} + {slope:.2f}θ (eV)\")\n", "print(f\"Slope: {slope * 96.485:.1f} kJ/mol per ML\")\n", "print(f\"Paper: 8.7 kJ/mol per ML\")\n", "print(f\"{'='*60}\")" ] }, { "cell_type": "markdown", "id": "9b4afc81", "metadata": {}, "source": [ "### Step 6: Visualize Coverage Dependence\n", "\n", "Create a plot showing how adsorption energy changes with coverage:" ] }, { "cell_type": "code", "execution_count": 25, "id": "64235a21", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "5. 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"title": { "text": "Adsorption Energy (eV/H)" } } } } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✓ Coverage dependence analysis complete!\n" ] } ], "source": [ "print(\"\\n5. Plotting coverage dependence...\")\n", "\n", "# Plot\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "ax.scatter(\n", " coverages,\n", " adsorption_energies,\n", " s=100,\n", " marker=\"o\",\n", " label=\"Calculated\",\n", " zorder=3,\n", " color=\"steelblue\",\n", ")\n", "\n", "cov_fit = np.linspace(0, max(coverages), 100)\n", "ads_fit = p(cov_fit)\n", "ax.plot(\n", " cov_fit, ads_fit, \"r--\", label=f\"Fit: {intercept:.2f} + {slope:.2f}θ\", linewidth=2\n", ")\n", "\n", "ax.set_xlabel(\"H Coverage (ML)\", fontsize=12)\n", "ax.set_ylabel(\"Adsorption Energy (eV/H)\", fontsize=12)\n", "ax.set_title(\"Coverage-Dependent H Adsorption on Ni(111)\", fontsize=14)\n", "ax.legend(fontsize=11)\n", "ax.grid(True, alpha=0.3)\n", "plt.tight_layout()\n", "plt.savefig(str(output_dir / part_dirs[\"part5\"] / \"coverage_dependence.png\"), dpi=300)\n", "plt.show()\n", "\n", "print(\"\\n✓ Coverage dependence analysis complete!\")" ] }, { "cell_type": "markdown", "id": "95cfc198", "metadata": {}, "source": [ "```{admonition} Missing UMA access?\n", ":class: dropdown, tip\n", "\n", "Don't have access to UMA yet? You can still explore this calculation!\n", "\n", "[Download example multiple H on Ni(111) structure](example_configs/4h_on_ni111.xyz) and test it in the [UMA demo (no login required)](https://facebook-fairchem-uma-demo.hf.space/) to see how the model handles coverage-dependent binding.\n", "```\n", "\n", "```{admonition} Comparison with Paper\n", ":class: note\n", "\n", "**Expected Results from Paper:**\n", "- **Slope**: 8.7 kJ/mol per ML (indicating repulsive lateral H-H interactions)\n", "- **Physical interpretation**: H atoms repel weakly due to electrostatic and Pauli effects\n", "\n", "**What to Check:**\n", "- Your fitted slope should be close to 8.7 kJ/mol per ML\n", "- The relationship should be approximately linear for θ < 1 ML\n", "- Intercept (E_ads at θ → 0) should match the single-H result from Part 4 (~-0.60 eV)\n", "\n", "**Typical Variations:**\n", "- Slope can vary by ±2-3 kJ/mol depending on slab size and configuration sampling\n", "- Non-linearity may appear at very high coverage (θ > 0.75 ML)\n", "- Model differences can affect lateral interactions more than adsorption energies\n", "```\n", "\n", "```{admonition} Physical Insights\n", ":class: note\n", "\n", "**Positive slope** (repulsive interactions):\n", "- Electrostatic: H atoms accumulate negative charge from Ni\n", "- Pauli repulsion: Overlapping electron clouds\n", "- Strain: Lattice distortions propagate\n", "\n", "**Magnitude**:\n", "- Weak (~10 kJ/mol/ML) → isolated adsorbates\n", "- Strong (>50 kJ/mol/ML) → clustering or phase separation likely\n", "\n", "The paper reports 8.7 kJ/mol/ML, indicating relatively weak lateral interactions for H on Ni(111).\n", "```\n", "\n", "### Explore on Your Own\n", "\n", "1. **Non-linear behavior**: Use polynomial (degree 2) fit. Is there curvature at high coverage?\n", "2. **Temperature effects**: Estimate configurational entropy at each coverage. How does this affect free energy?\n", "3. **Pattern formation**: Visualize the lowest-energy configuration at 0.5 ML. Are H atoms ordered?\n", "4. **Other adsorbates**: Repeat for O or N. How do lateral interactions compare?\n", "5. **Phase diagrams**: At what coverage do you expect phase separation (islands vs uniform)?\n", "\n", "---\n", "\n", "## Part 6: CO Formation/Dissociation Thermochemistry and Barrier\n", "\n", "### Introduction\n", "\n", "CO dissociation (CO* → C* + O*) is the rate-limiting step in many catalytic processes (Fischer-Tropsch, CO oxidation, etc.). We'll calculate the reaction energy for C* + O* → CO* and the activation barriers in both directions using the nudged elastic band (NEB) method.\n", "\n", "### Theory\n", "\n", "* **Forward Reaction**: C* + O* → CO* + * (recombination)\n", "* **Reverse Reaction**: CO* + *→ C* + O* (dissociation)\n", "* **Thermochemistry**: $\\Delta E_{\\text{rxn}} = E(\\text{C}^* + \\text{O}^*) - E(\\text{CO}^*)$\n", "* **Barrier**: NEB finds the minimum energy path (MEP) and transition state: $E_a = E^{\\ddagger} - E_{\\text{initial}}$\n", "\n", "### Step 1: Setup Slab and Calculators\n", "\n", "Initialize the Ni(111) surface and calculators:" ] }, { "cell_type": "code", "execution_count": 26, "id": "9841c5de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Created 96 atom slab\n", " ✓ Calculators initialized\n" ] } ], "source": [ "# Create slab\n", "ni_bulk_atoms = bulk(\"Ni\", \"fcc\", a=a_opt, cubic=True)\n", "ni_bulk_obj = Bulk(bulk_atoms=ni_bulk_atoms)\n", "ni_slabs = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj, specific_millers=(1, 1, 1)\n", ")\n", "slab = ni_slabs[0].atoms\n", "\n", "print(f\" Created {len(slab)} atom slab\")\n", "\n", "base_calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", "d3_calc = TorchDFTD3Calculator(device=\"cpu\", damping=\"bj\")\n", "print(\" \\u2713 Calculators initialized\")" ] }, { "cell_type": "markdown", "id": "5a01e2d9", "metadata": {}, "source": [ "### Step 2: Generate and Relax Final State (CO*)\n", "\n", "Find the most stable CO adsorption configuration (this is the product of C+O recombination):" ] }, { "cell_type": "code", "execution_count": 27, "id": "591687a6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "1. Final State: CO* on Ni(111)\n", " Generating CO adsorption configurations...\n", " Generated 5 configurations\n", " Config 1: E_total = -503.96 eV (RPBE: -467.14, D3: -36.82)\n", " Config 2: E_total = -503.96 eV (RPBE: -467.14, D3: -36.83)\n", " Config 3: E_total = -503.58 eV (RPBE: -466.73, D3: -36.85)\n", " Config 4: E_total = -503.58 eV (RPBE: -466.73, D3: -36.85)\n", " Config 5: E_total = -503.97 eV (RPBE: -467.14, D3: -36.82)\n", "\n", " → Best CO* (Config 5):\n", " RPBE: -467.14 eV\n", " D3: -36.82 eV\n", " Total: -503.97 eV\n", " ✓ Best CO* structure saved\n" ] } ], "source": [ "print(\"\\n1. Final State: CO* on Ni(111)\")\n", "print(\" Generating CO adsorption configurations...\")\n", "\n", "ni_bulk_obj_co = Bulk(bulk_atoms=ni_bulk_atoms)\n", "ni_slab_co = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj_co, specific_millers=(1, 1, 1)\n", ")[0]\n", "ni_slab_co.atoms = slab.copy()\n", "\n", "adsorbate_co = Adsorbate(adsorbate_smiles_from_db=\"*CO\")\n", "multi_ads_config_co = MultipleAdsorbateSlabConfig(\n", " ni_slab_co, [adsorbate_co], num_configurations=num_sites\n", ")\n", "\n", "print(f\" Generated {len(multi_ads_config_co.atoms_list)} configurations\")\n", "\n", "# Relax and find best\n", "co_energies = []\n", "co_energies_ml = []\n", "co_energies_d3 = []\n", "co_configs = []\n", "\n", "for idx, config in enumerate(multi_ads_config_co.atoms_list):\n", " config_relaxed = config.copy()\n", " config_relaxed.set_pbc([True, True, True])\n", " config_relaxed.calc = base_calc\n", " opt = LBFGS(config_relaxed, logfile=None)\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " E_ml = config_relaxed.get_potential_energy()\n", " config_relaxed.calc = d3_calc\n", " E_d3 = config_relaxed.get_potential_energy()\n", " E_total = E_ml + E_d3\n", "\n", " co_energies.append(E_total)\n", " co_energies_ml.append(E_ml)\n", " co_energies_d3.append(E_d3)\n", " co_configs.append(config_relaxed)\n", " print(\n", " f\" Config {idx+1}: E_total = {E_total:.2f} eV (RPBE: {E_ml:.2f}, D3: {E_d3:.2f})\"\n", " )\n", "\n", "best_co_idx = np.argmin(co_energies)\n", "final_co = co_configs[best_co_idx]\n", "E_final_co = co_energies[best_co_idx]\n", "E_final_co_ml = co_energies_ml[best_co_idx]\n", "E_final_co_d3 = co_energies_d3[best_co_idx]\n", "\n", "print(f\"\\n → Best CO* (Config {best_co_idx+1}):\")\n", "print(f\" RPBE: {E_final_co_ml:.2f} eV\")\n", "print(f\" D3: {E_final_co_d3:.2f} eV\")\n", "print(f\" Total: {E_final_co:.2f} eV\")\n", "\n", "# Save best CO state\n", "ase.io.write(str(output_dir / part_dirs[\"part6\"] / \"co_final_best.traj\"), final_co)\n", "print(\" ✓ Best CO* structure saved\")\n", "\n", "# Visualize best CO* structure\n", "print(\"\\n Visualizing best CO* structure...\")\n", "view(final_co, viewer='x3d')" ] }, { "cell_type": "markdown", "id": "ee509fd0", "metadata": {}, "source": [ "### Step 3: Generate and Relax Initial State (C* + O*)\n", "\n", "Find the most stable configuration for dissociated C and O (reactants):" ] }, { "cell_type": "code", "execution_count": 28, "id": "0e9f35d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2. Initial State: C* + O* on Ni(111)\n", " Generating C+O configurations...\n", " Generated 5 configurations\n", " Config 1: E_total = -502.79 eV (RPBE: -465.89, D3: -36.90, C-O dist: 4.33 Å)\n", " Config 2: E_total = -502.72 eV (RPBE: -465.83, D3: -36.89, C-O dist: 5.73 Å)\n", " Config 3: E_total = -502.55 eV (RPBE: -465.67, D3: -36.88, C-O dist: 3.25 Å)\n", " Config 4: E_total = -502.48 eV (RPBE: -465.60, D3: -36.89, C-O dist: 2.96 Å)\n", " Config 5: E_total = -502.82 eV (RPBE: -465.93, D3: -36.89, C-O dist: 4.35 Å)\n", "\n", " → Best C*+O* (Config 5):\n", " RPBE: -465.93 eV\n", " D3: -36.89 eV\n", " Total: -502.82 eV\n", " ✓ Best C*+O* structure saved\n" ] } ], "source": [ "print(\"\\n2. Initial State: C* + O* on Ni(111)\")\n", "print(\" Generating C+O configurations...\")\n", "\n", "ni_bulk_obj_c_o = Bulk(bulk_atoms=ni_bulk_atoms)\n", "ni_slab_c_o = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj_c_o, specific_millers=(1, 1, 1)\n", ")[0]\n", "\n", "adsorbate_c = Adsorbate(adsorbate_smiles_from_db=\"*C\")\n", "adsorbate_o = Adsorbate(adsorbate_smiles_from_db=\"*O\")\n", "\n", "multi_ads_config_c_o = MultipleAdsorbateSlabConfig(\n", " ni_slab_c_o, [adsorbate_c, adsorbate_o], num_configurations=num_sites\n", ")\n", "\n", "print(f\" Generated {len(multi_ads_config_c_o.atoms_list)} configurations\")\n", "\n", "c_o_energies = []\n", "c_o_energies_ml = []\n", "c_o_energies_d3 = []\n", "c_o_configs = []\n", "\n", "for idx, config in enumerate(multi_ads_config_c_o.atoms_list):\n", " config_relaxed = config.copy()\n", " config_relaxed.set_pbc([True, True, True])\n", " config_relaxed.calc = base_calc\n", " opt = LBFGS(config_relaxed, logfile=None)\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " # Check C-O bond distance to ensure they haven't formed CO molecule\n", " c_o_dist = config_relaxed[config_relaxed.get_tags() == 2].get_distance(\n", " 0, 1, mic=True\n", " )\n", "\n", " # CO bond length is ~1.15 Å, so if distance < 1.5 Å, they've formed a molecule\n", " if c_o_dist < 1.5:\n", " print(\n", " f\" Config {idx+1}: ⚠ REJECTED - C and O formed CO molecule (d = {c_o_dist:.2f} Å)\"\n", " )\n", " continue\n", "\n", " E_ml = config_relaxed.get_potential_energy()\n", " config_relaxed.calc = d3_calc\n", " E_d3 = config_relaxed.get_potential_energy()\n", " E_total = E_ml + E_d3\n", "\n", " c_o_energies.append(E_total)\n", " c_o_energies_ml.append(E_ml)\n", " c_o_energies_d3.append(E_d3)\n", " c_o_configs.append(config_relaxed)\n", " print(\n", " f\" Config {idx+1}: E_total = {E_total:.2f} eV (RPBE: {E_ml:.2f}, D3: {E_d3:.2f}, C-O dist: {c_o_dist:.2f} Å)\"\n", " )\n", "\n", "best_c_o_idx = np.argmin(c_o_energies)\n", "initial_c_o = c_o_configs[best_c_o_idx]\n", "E_initial_c_o = c_o_energies[best_c_o_idx]\n", "E_initial_c_o_ml = c_o_energies_ml[best_c_o_idx]\n", "E_initial_c_o_d3 = c_o_energies_d3[best_c_o_idx]\n", "\n", "print(f\"\\n → Best C*+O* (Config {best_c_o_idx+1}):\")\n", "print(f\" RPBE: {E_initial_c_o_ml:.2f} eV\")\n", "print(f\" D3: {E_initial_c_o_d3:.2f} eV\")\n", "print(f\" Total: {E_initial_c_o:.2f} eV\")\n", "\n", "# Save best C+O state\n", "ase.io.write(str(output_dir / part_dirs[\"part6\"] / \"co_initial_best.traj\"), initial_c_o)\n", "print(\" ✓ Best C*+O* structure saved\")\n", "\n", "# Visualize best C*+O* structure\n", "print(\"\\n Visualizing best C*+O* structure...\")\n", "view(initial_c_o, viewer='x3d')" ] }, { "cell_type": "markdown", "id": "05ed4dda", "metadata": {}, "source": [ "### Step 3b: Calculate C* and O* Energies Separately\n", "\n", "Another strategy to calculate the initial energies for *C and *O at very low coverage (without interactions between the two reactants) is to do two separate relaxations." ] }, { "cell_type": "code", "execution_count": 29, "id": "910d05cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Clean slab: E_total = -487.46 eV (RPBE: -450.89, D3: -36.57)\n" ] } ], "source": [ "# Clean slab\n", "ni_bulk_obj = Bulk(bulk_atoms=ni_bulk_atoms)\n", "clean_slab = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj_c_o, specific_millers=(1, 1, 1)\n", ")[0].atoms\n", "clean_slab.set_pbc([True, True, True])\n", "clean_slab.calc = base_calc\n", "opt = LBFGS(clean_slab, logfile=None)\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "E_clean_ml = clean_slab.get_potential_energy()\n", "clean_slab.calc = d3_calc\n", "E_clean_d3 = clean_slab.get_potential_energy()\n", "E_clean = E_clean_ml + E_clean_d3\n", "\n", "print(\n", " f\"\\n Clean slab: E_total = {E_clean:.2f} eV (RPBE: {E_clean_ml:.2f}, D3: {E_clean_d3:.2f})\"\n", ")" ] }, { "cell_type": "code", "execution_count": 30, "id": "77989c2f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2b. Separate C* and O* Energies:\n", " Calculating energies in separate unit cells to avoid interactions\n", "\n", " Generating C* configurations...\n", " Config 1: E_total = -495.67 eV (RPBE: -458.89, D3: -36.77)\n", " Config 2: E_total = -495.73 eV (RPBE: -458.94, D3: -36.78)\n", " Config 3: E_total = -495.72 eV (RPBE: -458.94, D3: -36.78)\n", " Config 4: E_total = -495.67 eV (RPBE: -458.89, D3: -36.78)\n", " Config 5: E_total = -495.72 eV (RPBE: -458.94, D3: -36.78)\n", "\n", " → Best C* (Config 2):\n", " RPBE: -458.94 eV\n", " D3: -36.78 eV\n", " Total: -495.73 eV\n", "\n", " Generating O* configurations...\n", " Config 1: E_total = -494.66 eV (RPBE: -457.97, D3: -36.69)\n", " Config 2: E_total = -494.66 eV (RPBE: -457.97, D3: -36.69)\n", " Config 3: E_total = -494.13 eV (RPBE: -457.44, D3: -36.69)\n", " Config 4: E_total = -494.66 eV (RPBE: -457.97, D3: -36.69)\n", " Config 5: E_total = -494.56 eV (RPBE: -457.87, D3: -36.68)\n", "\n", " → Best O* (Config 2):\n", " RPBE: -457.97 eV\n", " D3: -36.69 eV\n", " Total: -494.66 eV\n" ] } ], "source": [ "print(f\"\\n2b. Separate C* and O* Energies:\")\n", "print(\" Calculating energies in separate unit cells to avoid interactions\")\n", "\n", "ni_bulk_obj_c_o = Bulk(bulk_atoms=ni_bulk_atoms)\n", "ni_slab_c_o = Slab.from_bulk_get_specific_millers(\n", " bulk=ni_bulk_obj_c_o, specific_millers=(1, 1, 1)\n", ")[0]\n", "\n", "print(\"\\n Generating C* configurations...\")\n", "multi_ads_config_c = MultipleAdsorbateSlabConfig(\n", " ni_slab_c_o,\n", " adsorbates=[Adsorbate(adsorbate_smiles_from_db=\"*C\")],\n", " num_configurations=num_sites,\n", ")\n", "\n", "c_energies = []\n", "c_energies_ml = []\n", "c_energies_d3 = []\n", "c_configs = []\n", "\n", "for idx, config in enumerate(multi_ads_config_c.atoms_list):\n", " config_relaxed = config.copy()\n", " config_relaxed.set_pbc([True, True, True])\n", " config_relaxed.calc = base_calc\n", " opt = LBFGS(config_relaxed, logfile=None)\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " E_ml = config_relaxed.get_potential_energy()\n", " config_relaxed.calc = d3_calc\n", " E_d3 = config_relaxed.get_potential_energy()\n", " E_total = E_ml + E_d3\n", "\n", " c_energies.append(E_total)\n", " c_energies_ml.append(E_ml)\n", " c_energies_d3.append(E_d3)\n", " c_configs.append(config_relaxed)\n", " print(\n", " f\" Config {idx+1}: E_total = {E_total:.2f} eV (RPBE: {E_ml:.2f}, D3: {E_d3:.2f})\"\n", " )\n", "\n", "best_c_idx = np.argmin(c_energies)\n", "c_ads = c_configs[best_c_idx]\n", "E_c = c_energies[best_c_idx]\n", "E_c_ml = c_energies_ml[best_c_idx]\n", "E_c_d3 = c_energies_d3[best_c_idx]\n", "\n", "print(f\"\\n → Best C* (Config {best_c_idx+1}):\")\n", "print(f\" RPBE: {E_c_ml:.2f} eV\")\n", "print(f\" D3: {E_c_d3:.2f} eV\")\n", "print(f\" Total: {E_c:.2f} eV\")\n", "\n", "# Save best C state\n", "ase.io.write(str(output_dir / part_dirs[\"part6\"] / \"c_best.traj\"), c_ads)\n", "\n", "# Visualize best C* structure\n", "print(\"\\n Visualizing best C* structure...\")\n", "view(c_ads, viewer='x3d')\n", "\n", "# Generate O* configuration\n", "print(\"\\n Generating O* configurations...\")\n", "multi_ads_config_o = MultipleAdsorbateSlabConfig(\n", " ni_slab_c_o,\n", " adsorbates=[Adsorbate(adsorbate_smiles_from_db=\"*O\")],\n", " num_configurations=num_sites,\n", ")\n", "o_energies = []\n", "o_energies_ml = []\n", "o_energies_d3 = []\n", "o_configs = []\n", "\n", "for idx, config in enumerate(multi_ads_config_o.atoms_list):\n", " config_relaxed = config.copy()\n", " config_relaxed.set_pbc([True, True, True])\n", " config_relaxed.calc = base_calc\n", " opt = LBFGS(config_relaxed, logfile=None)\n", " opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", " E_ml = config_relaxed.get_potential_energy()\n", " config_relaxed.calc = d3_calc\n", " E_d3 = config_relaxed.get_potential_energy()\n", " E_total = E_ml + E_d3\n", "\n", " o_energies.append(E_total)\n", " o_energies_ml.append(E_ml)\n", " o_energies_d3.append(E_d3)\n", " o_configs.append(config_relaxed)\n", " print(\n", " f\" Config {idx+1}: E_total = {E_total:.2f} eV (RPBE: {E_ml:.2f}, D3: {E_d3:.2f})\"\n", " )\n", "\n", "best_o_idx = np.argmin(o_energies)\n", "o_ads = o_configs[best_o_idx]\n", "E_o = o_energies[best_o_idx]\n", "E_o_ml = o_energies_ml[best_o_idx]\n", "E_o_d3 = o_energies_d3[best_o_idx]\n", "\n", "print(f\"\\n → Best O* (Config {best_o_idx+1}):\")\n", "print(f\" RPBE: {E_o_ml:.2f} eV\")\n", "print(f\" D3: {E_o_d3:.2f} eV\")\n", "print(f\" Total: {E_o:.2f} eV\")\n", "\n", "# Save best O state\n", "ase.io.write(str(output_dir / part_dirs[\"part6\"] / \"o_best.traj\"), o_ads)\n", "\n", "# Visualize best O* structure\n", "print(\"\\n Visualizing best O* structure...\")\n", "view(o_ads, viewer='x3d')\n", "\n", "# Calculate combined energy for separate C* and O*\n", "E_initial_c_o_separate = E_c + E_o\n", "E_initial_c_o_separate_ml = E_c_ml + E_o_ml\n", "E_initial_c_o_separate_d3 = E_c_d3 + E_o_d3" ] }, { "cell_type": "code", "execution_count": 31, "id": "921a3eff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Combined C* + O* (separate calculations):\n", " RPBE: -916.92 eV\n", " D3: -73.47 eV\n", " Total: -990.39 eV\n", "\n", " Comparison:\n", " C*+O* (same cell): -15.36 eV\n", " C* + O* (separate): -15.46 eV\n", " Difference: 0.10 eV\n", " ✓ Separate C* and O* energies calculated\n" ] } ], "source": [ "print(f\"\\n Combined C* + O* (separate calculations):\")\n", "print(f\" RPBE: {E_initial_c_o_separate_ml:.2f} eV\")\n", "print(f\" D3: {E_initial_c_o_separate_d3:.2f} eV\")\n", "print(f\" Total: {E_initial_c_o_separate:.2f} eV\")\n", "\n", "print(f\"\\n Comparison:\")\n", "print(f\" C*+O* (same cell): {E_initial_c_o - E_clean:.2f} eV\")\n", "print(f\" C* + O* (separate): {E_initial_c_o_separate - 2*E_clean:.2f} eV\")\n", "print(\n", " f\" Difference: {(E_initial_c_o - E_clean) - (E_initial_c_o_separate - 2*E_clean):.2f} eV\"\n", ")\n", "print(\" ✓ Separate C* and O* energies calculated\")" ] }, { "cell_type": "markdown", "id": "1677a425", "metadata": {}, "source": [ "### Step 4: Calculate Reaction Energy with ZPE\n", "\n", "Compute the thermochemistry for C* + O* → CO* with ZPE corrections:" ] }, { "cell_type": "code", "execution_count": 32, "id": "6595e9c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "3. Reaction Energy (C* + O* → CO*):\n", " ============================================================\n", "\n", " Electronic Energies:\n", " Initial (C*+O*): RPBE = -465.93 eV, D3 = -36.89 eV, Total = -502.82 eV\n", " Final (CO*): RPBE = -467.14 eV, D3 = -36.82 eV, Total = -503.97 eV\n", "\n", " Reaction Energies (without ZPE):\n", " ΔE(RPBE only): -1.21 eV = -117.1 kJ/mol\n", " ΔE(D3 contrib): 0.07 eV = 6.5 kJ/mol\n", " ΔE(RPBE+D3): -1.15 eV = -110.6 kJ/mol\n", "\n", " Computing ZPE for CO*...\n", " ZPE(CO*): 0.18+0.00j eV (182.5+0.0j meV)\n", "\n", " Computing ZPE for C* and O*...\n", " ZPE(C*+O*): 0.17+0.00j eV (174.9+0.0j meV)\n", "\n", " Reaction Energy (with ZPE):\n", " ΔE(electronic): -1.15 eV = -110.6 kJ/mol\n", " ΔZPE: 0.01+0.00j eV = 0.7+0.0j kJ/mol (7.7+0.0j meV)\n", " ΔE(total): -1.14+0.00j eV = -109.8+0.0j kJ/mol\n", "\n", " Summary:\n", " Without D3, without ZPE: -1.21 eV = -117.1 kJ/mol\n", " With D3, without ZPE: -1.15 eV = -110.6 kJ/mol\n", " With D3, with ZPE: -1.14+0.00j eV = -109.8+0.0j kJ/mol\n", "\n", " ============================================================\n", "\n", " Comparison with Paper (Table 5):\n", " Paper (DFT-D3): -142.7 kJ/mol = -1.48 eV\n", " This work: -109.8+0.0j kJ/mol = -1.14+0.00j eV\n", " Difference: 0.34 eV\n", "\n", " ✓ Reaction is exothermic (C+O recombination favorable)\n" ] } ], "source": [ "print(f\"\\n3. Reaction Energy (C* + O* → CO*):\")\n", "print(f\" \" + \"=\" * 60)\n", "\n", "# Electronic energies\n", "print(f\"\\n Electronic Energies:\")\n", "print(\n", " f\" Initial (C*+O*): RPBE = {E_initial_c_o_ml:.2f} eV, D3 = {E_initial_c_o_d3:.2f} eV, Total = {E_initial_c_o:.2f} eV\"\n", ")\n", "print(\n", " f\" Final (CO*): RPBE = {E_final_co_ml:.2f} eV, D3 = {E_final_co_d3:.2f} eV, Total = {E_final_co:.2f} eV\"\n", ")\n", "\n", "# Reaction energies without ZPE\n", "delta_E_rpbe = E_final_co_ml - E_initial_c_o_ml\n", "delta_E_d3_contrib = E_final_co_d3 - E_initial_c_o_d3\n", "delta_E_elec = E_final_co - E_initial_c_o\n", "\n", "print(f\"\\n Reaction Energies (without ZPE):\")\n", "print(f\" ΔE(RPBE only): {delta_E_rpbe:.2f} eV = {delta_E_rpbe*96.485:.1f} kJ/mol\")\n", "print(\n", " f\" ΔE(D3 contrib): {delta_E_d3_contrib:.2f} eV = {delta_E_d3_contrib*96.485:.1f} kJ/mol\"\n", ")\n", "print(f\" ΔE(RPBE+D3): {delta_E_elec:.2f} eV = {delta_E_elec*96.485:.1f} kJ/mol\")\n", "\n", "# Calculate ZPE for CO* (final state)\n", "print(f\"\\n Computing ZPE for CO*...\")\n", "final_co.calc = base_calc\n", "\n", "co_indices = np.where(final_co.get_tags() == 2)[0]\n", "vib_co = Vibrations(final_co, indices=co_indices, delta=0.02, name=\"vib_co\")\n", "vib_co.run()\n", "vib_energies_co = vib_co.get_energies()\n", "zpe_co = np.sum(vib_energies_co[vib_energies_co > 0]) / 2.0\n", "vib_co.clean()\n", "print(f\" ZPE(CO*): {zpe_co:.2f} eV ({zpe_co*1000:.1f} meV)\")\n", "\n", "\n", "# Calculate ZPE for C* and O* (initial state)\n", "print(f\"\\n Computing ZPE for C* and O*...\")\n", "initial_c_o.calc = base_calc\n", "c_o_indices = np.where(initial_c_o.get_tags() == 2)[0]\n", "vib_c_o = Vibrations(initial_c_o, indices=c_o_indices, delta=0.02, name=\"vib_c_o\")\n", "vib_c_o.run()\n", "vib_energies_c_o = vib_c_o.get_energies()\n", "zpe_c_o = np.sum(vib_energies_c_o[vib_energies_c_o > 0]) / 2.0\n", "vib_c_o.clean()\n", "print(f\" ZPE(C*+O*): {zpe_c_o:.2f} eV ({zpe_c_o*1000:.1f} meV)\")\n", "\n", "\n", "# Total reaction energy with ZPE\n", "delta_zpe = zpe_co - zpe_c_o\n", "delta_E_zpe = delta_E_elec + delta_zpe\n", "\n", "print(f\"\\n Reaction Energy (with ZPE):\")\n", "print(f\" ΔE(electronic): {delta_E_elec:.2f} eV = {delta_E_elec*96.485:.1f} kJ/mol\")\n", "print(\n", " f\" ΔZPE: {delta_zpe:.2f} eV = {delta_zpe*96.485:.1f} kJ/mol ({delta_zpe*1000:.1f} meV)\"\n", ")\n", "print(f\" ΔE(total): {delta_E_zpe:.2f} eV = {delta_E_zpe*96.485:.1f} kJ/mol\")\n", "\n", "print(f\"\\n Summary:\")\n", "print(\n", " f\" Without D3, without ZPE: {delta_E_rpbe:.2f} eV = {delta_E_rpbe*96.485:.1f} kJ/mol\"\n", ")\n", "print(\n", " f\" With D3, without ZPE: {delta_E_elec:.2f} eV = {delta_E_elec*96.485:.1f} kJ/mol\"\n", ")\n", "print(\n", " f\" With D3, with ZPE: {delta_E_zpe:.2f} eV = {delta_E_zpe*96.485:.1f} kJ/mol\"\n", ")\n", "\n", "print(f\"\\n \" + \"=\" * 60)\n", "print(f\"\\n Comparison with Paper (Table 5):\")\n", "print(f\" Paper (DFT-D3): -142.7 kJ/mol = -1.48 eV\")\n", "print(f\" This work: {delta_E_zpe*96.485:.1f} kJ/mol = {delta_E_zpe:.2f} eV\")\n", "print(f\" Difference: {abs(delta_E_zpe - (-1.48)):.2f} eV\")\n", "\n", "if delta_E_zpe < 0:\n", " print(f\"\\n ✓ Reaction is exothermic (C+O recombination favorable)\")\n", "else:\n", " print(f\"\\n ⚠ Reaction is endothermic (dissociation favorable)\")" ] }, { "cell_type": "markdown", "id": "80048d52", "metadata": {}, "source": [ "### Step 5: Calculate CO Adsorption Energy (Bonus)\n", "\n", "Calculate how strongly CO binds to the surface:" ] }, { "cell_type": "code", "execution_count": 33, "id": "313c8df1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "4. CO Adsorption Energy ( CO(g) + * → CO*):\n", " This helps us understand CO binding strength\n", " CO(g): E_total = -14.43 eV (RPBE: -14.42, D3: -0.01)\n", " ZPE(CO(g)): 0.13+0.00j eV\n", " ZPE(CO*): 0.18+0.00j eV (from Step 4 calculation)\n", "\n", " Electronic Energy Breakdown:\n", " ΔE(RPBE only) = -1.84 eV\n", " ΔE(D3 contrib) = -0.24 eV\n", " ΔE(RPBE+D3) = -2.08 eV\n", "\n", " ZPE Contribution:\n", " ΔZPE = 0.05-0.00j eV\n", "\n", " Total Adsorption Energy:\n", " ΔE(total) = -2.03-0.00j eV = -195.6-0.0j kJ/mol\n", "\n", " Summary:\n", " E_ads(CO) without ZPE = 2.08 eV = 200.5 kJ/mol\n", " E_ads(CO) with ZPE = 2.03+0.00j eV = 195.6+0.0j kJ/mol\n", " → CO binds 2.03 eV stronger than H (3.4x)\n" ] } ], "source": [ "print(f\"\\n4. CO Adsorption Energy ( CO(g) + * → CO*):\")\n", "print(\" This helps us understand CO binding strength\")\n", "\n", "# CO(g)\n", "co_gas = Atoms(\"CO\", positions=[[0, 0, 0], [0, 0, 1.15]])\n", "co_gas.center(vacuum=10.0)\n", "co_gas.set_pbc([True, True, True])\n", "co_gas.calc = base_calc\n", "opt = LBFGS(co_gas, logfile=None)\n", "opt.run(fmax=0.05, steps=relaxation_steps)\n", "\n", "E_co_gas_ml = co_gas.get_potential_energy()\n", "co_gas.calc = d3_calc\n", "E_co_gas_d3 = co_gas.get_potential_energy()\n", "E_co_gas = E_co_gas_ml + E_co_gas_d3\n", "\n", "print(\n", " f\" CO(g): E_total = {E_co_gas:.2f} eV (RPBE: {E_co_gas_ml:.2f}, D3: {E_co_gas_d3:.2f})\"\n", ")\n", "\n", "# Calculate ZPE for CO(g)\n", "co_gas.calc = base_calc\n", "vib_co_gas = Vibrations(co_gas, indices=[0, 1], delta=0.01, nfree=2)\n", "vib_co_gas.clean()\n", "vib_co_gas.run()\n", "vib_energies_co_gas = vib_co_gas.get_energies()\n", "zpe_co_gas = 0.5 * np.sum(vib_energies_co_gas[vib_energies_co_gas > 0])\n", "vib_co_gas.clean()\n", "\n", "print(f\" ZPE(CO(g)): {zpe_co_gas:.2f} eV\")\n", "print(f\" ZPE(CO*): {zpe_co:.2f} eV (from Step 4 calculation)\")\n", "\n", "# Electronic adsorption energy\n", "E_ads_co_elec = E_final_co - E_clean - E_co_gas\n", "\n", "# ZPE contribution to adsorption energy\n", "delta_zpe_ads = zpe_co - zpe_co_gas\n", "\n", "# Total adsorption energy with ZPE\n", "E_ads_co_total = E_ads_co_elec + delta_zpe_ads\n", "\n", "print(f\"\\n Electronic Energy Breakdown:\")\n", "print(f\" ΔE(RPBE only) = {(E_final_co_ml - E_clean_ml - E_co_gas_ml):.2f} eV\")\n", "print(f\" ΔE(D3 contrib) = {((E_final_co_d3 - E_clean_d3 - E_co_gas_d3)):.2f} eV\")\n", "print(f\" ΔE(RPBE+D3) = {E_ads_co_elec:.2f} eV\")\n", "print(f\"\\n ZPE Contribution:\")\n", "print(f\" ΔZPE = {delta_zpe_ads:.2f} eV\")\n", "print(f\"\\n Total Adsorption Energy:\")\n", "print(f\" ΔE(total) = {E_ads_co_total:.2f} eV = {E_ads_co_total*96.485:.1f} kJ/mol\")\n", "print(f\"\\n Summary:\")\n", "print(\n", " f\" E_ads(CO) without ZPE = {-E_ads_co_elec:.2f} eV = {-E_ads_co_elec*96.485:.1f} kJ/mol\"\n", ")\n", "print(\n", " f\" E_ads(CO) with ZPE = {-E_ads_co_total:.2f} eV = {-E_ads_co_total*96.485:.1f} kJ/mol\"\n", ")\n", "print(\n", " f\" → CO binds {abs(E_ads_co_total):.2f} eV stronger than H ({abs(E_ads_co_total)/0.60:.1f}x)\"\n", ")" ] }, { "cell_type": "markdown", "id": "6ff75e73", "metadata": {}, "source": [ "```{admonition} Comparison with Paper Results\n", ":class: tip\n", "\n", "The paper reports a CO adsorption energy of **1.82 eV (175.6 kJ/mol)** in Table 4, calculated using DFT (RPBE functional).\n", "\n", "These results show:\n", "- **Without ZPE**: The electronic binding energy matches well with DFT predictions\n", "- **With ZPE**: The zero-point energy correction reduces the binding strength slightly\n", "- **D3 Dispersion**: Contributes to stronger binding due to van der Waals interactions\n", "\n", "```\n", "\n", "### Step 6: Find guesses for nearby initial and final states for the reaction\n", "\n", "Now that we have an estimate on the reaction energy from the best possible initial and final states, we want to find a transition state (barrier) for this reaction. There are MANY possible ways that we could do this. In this case, we'll start with the *CO final state and then try and find a nearby local minimal of *C and *O, by fixing the C-O bond distance and finding a nearby local minima. Note that this approach required some insight into what the transition state might look like, and could be considerably more complicated for a reaction that did not involve breaking a single bond." ] }, { "cell_type": "code", "execution_count": 34, "id": "fa0d267a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finding Transition State Initial and Final States\n", " Creating initial guess with stretched C-O bond...\n", " Starting from CO* and stretching the C-O bond...\n", " Step Time Energy fmax\n", "LBFGS: 0 23:01:13 -466.833577 0.918959\n", "LBFGS: 1 23:01:13 -461.461189 4.272469\n", "LBFGS: 2 23:01:13 -461.527428 3.888420\n", "LBFGS: 3 23:01:13 -461.860048 0.989471\n", "LBFGS: 4 23:01:13 -461.929334 0.960760\n", "LBFGS: 5 23:01:13 -461.954202 0.966197\n", "LBFGS: 6 23:01:13 -461.944542 0.794667\n", "LBFGS: 7 23:01:13 -462.001009 0.459983\n", "LBFGS: 8 23:01:13 -461.957601 0.384228\n", "LBFGS: 9 23:01:14 -462.017381 0.912861\n", "LBFGS: 10 23:01:14 -462.023708 0.393469\n", "LBFGS: 11 23:01:14 -462.038057 0.431428\n", "LBFGS: 12 23:01:14 -461.988872 0.701479\n", "LBFGS: 13 23:01:14 -462.125147 1.497231\n", "LBFGS: 14 23:01:14 -462.253622 2.860863\n", "LBFGS: 15 23:01:14 -462.268728 3.897449\n", "LBFGS: 16 23:01:14 -462.269325 4.025552\n", "LBFGS: 17 23:01:14 -462.304123 3.669888\n", "LBFGS: 18 23:01:14 -462.336016 3.002295\n", "LBFGS: 19 23:01:15 -462.360727 2.772722\n", "LBFGS: 20 23:01:15 -462.387863 2.783965\n", "LBFGS: 21 23:01:15 -462.423538 2.712058\n", "LBFGS: 22 23:01:15 -462.501573 2.395967\n", "LBFGS: 23 23:01:15 -462.606571 1.797762\n", "LBFGS: 24 23:01:15 -462.710941 0.970545\n", "LBFGS: 25 23:01:15 -462.753509 0.917854\n", "LBFGS: 26 23:01:15 -462.808001 0.746607\n", "LBFGS: 27 23:01:15 -462.873653 0.621084\n", "LBFGS: 28 23:01:16 -462.917577 0.394380\n", "LBFGS: 29 23:01:16 -462.928809 0.283608\n", "LBFGS: 30 23:01:16 -462.943311 0.279580\n", "LBFGS: 31 23:01:16 -462.955008 0.403911\n", "LBFGS: 32 23:01:16 -462.971107 0.562919\n", "LBFGS: 33 23:01:16 -462.961360 0.464164\n", "LBFGS: 34 23:01:16 -462.992650 0.377839\n", "LBFGS: 35 23:01:16 -463.004065 0.584843\n", "LBFGS: 36 23:01:16 -463.021248 0.458266\n", "LBFGS: 37 23:01:17 -463.055692 0.388972\n", "LBFGS: 38 23:01:17 -463.073581 0.343069\n", "LBFGS: 39 23:01:17 -463.110835 0.415179\n", "LBFGS: 40 23:01:17 -463.094048 0.617218\n", "LBFGS: 41 23:01:17 -463.134575 0.652458\n", "LBFGS: 42 23:01:17 -463.140784 0.591121\n", "LBFGS: 43 23:01:17 -463.162195 0.472879\n", "LBFGS: 44 23:01:17 -463.193716 0.428669\n", "LBFGS: 45 23:01:17 -463.239892 0.734359\n", "LBFGS: 46 23:01:17 -463.259375 0.516402\n", "LBFGS: 47 23:01:18 -463.285862 0.513865\n", "LBFGS: 48 23:01:18 -463.296052 0.438266\n", "LBFGS: 49 23:01:18 -463.302194 0.722335\n", "LBFGS: 50 23:01:18 -463.341009 1.114078\n", "LBFGS: 51 23:01:18 -463.369828 1.232349\n", "LBFGS: 52 23:01:18 -463.397904 1.354343\n", "LBFGS: 53 23:01:18 -463.395402 1.529313\n", "LBFGS: 54 23:01:18 -463.500019 0.999610\n", "LBFGS: 55 23:01:18 -463.563548 0.718620\n", "LBFGS: 56 23:01:18 -463.607946 0.569425\n", "LBFGS: 57 23:01:19 -463.701097 0.718225\n", "LBFGS: 58 23:01:19 -463.733467 0.625528\n", "LBFGS: 59 23:01:19 -463.786436 0.506107\n", "LBFGS: 60 23:01:19 -463.795030 0.707968\n", "LBFGS: 61 23:01:19 -463.814845 0.678639\n", "LBFGS: 62 23:01:19 -463.853423 0.582421\n", "LBFGS: 63 23:01:19 -463.897269 0.624327\n", "LBFGS: 64 23:01:19 -463.940940 0.700137\n", "LBFGS: 65 23:01:19 -463.973876 0.644491\n", "LBFGS: 66 23:01:20 -463.988981 0.630788\n", "LBFGS: 67 23:01:20 -463.991712 0.899301\n", "LBFGS: 68 23:01:20 -464.002938 0.619645\n", "LBFGS: 69 23:01:20 -464.021711 0.507639\n", "LBFGS: 70 23:01:20 -464.034460 0.447858\n", "LBFGS: 71 23:01:20 -463.995193 0.743962\n", "LBFGS: 72 23:01:20 -464.033023 0.487690\n", "LBFGS: 73 23:01:20 -464.016678 0.645788\n", "LBFGS: 74 23:01:20 -464.033890 0.541655\n", "LBFGS: 75 23:01:20 -464.033086 0.450336\n", "LBFGS: 76 23:01:21 -464.016805 0.586558\n", "LBFGS: 77 23:01:21 -464.020680 0.635983\n", "LBFGS: 78 23:01:21 -464.049292 0.458329\n", "LBFGS: 79 23:01:21 -464.054611 0.710292\n", "LBFGS: 80 23:01:21 -464.071123 0.278892\n", "LBFGS: 81 23:01:21 -464.082689 0.344447\n", "LBFGS: 82 23:01:21 -464.092376 0.207469\n", "LBFGS: 83 23:01:21 -464.102794 0.192677\n", "LBFGS: 84 23:01:21 -464.106544 0.156024\n", "LBFGS: 85 23:01:22 -464.112385 0.162942\n", "LBFGS: 86 23:01:22 -464.118338 0.206146\n", "LBFGS: 87 23:01:22 -464.119901 0.222053\n", "LBFGS: 88 23:01:22 -464.143780 0.278044\n", "LBFGS: 89 23:01:22 -464.159231 0.390568\n", "LBFGS: 90 23:01:23 -464.162734 0.318419\n", "LBFGS: 91 23:01:23 -464.174285 0.284007\n", "LBFGS: 92 23:01:23 -464.204907 0.387464\n", "LBFGS: 93 23:01:23 -464.204989 0.393145\n", "LBFGS: 94 23:01:23 -464.213991 0.291387\n", "LBFGS: 95 23:01:23 -464.225447 0.319222\n", "LBFGS: 96 23:01:23 -464.234004 0.295562\n", "LBFGS: 97 23:01:23 -464.248727 0.331747\n", "LBFGS: 98 23:01:23 -464.278592 0.381604\n", "LBFGS: 99 23:01:24 -464.324102 0.419363\n", "LBFGS: 100 23:01:24 -464.328870 0.352436\n", "LBFGS: 101 23:01:24 -464.303059 0.315515\n", "LBFGS: 102 23:01:24 -464.324776 0.381670\n", "LBFGS: 103 23:01:24 -464.328927 0.213708\n", "LBFGS: 104 23:01:24 -464.329363 0.231921\n", "LBFGS: 105 23:01:24 -464.369675 0.186349\n", "LBFGS: 106 23:01:24 -464.340838 0.127838\n", "LBFGS: 107 23:01:24 -464.336171 0.115805\n", "LBFGS: 108 23:01:24 -464.387544 0.238554\n", "LBFGS: 109 23:01:25 -464.382687 0.162814\n", "LBFGS: 110 23:01:25 -464.354106 0.117650\n", "LBFGS: 111 23:01:25 -464.367604 0.072687\n", "LBFGS: 112 23:01:25 -464.390068 0.059878\n", "LBFGS: 113 23:01:25 -464.378423 0.044048\n", "LBFGS: 114 23:01:25 -464.356922 0.094799\n", "LBFGS: 115 23:01:25 -464.359221 0.064651\n", "LBFGS: 116 23:01:25 -464.379848 0.067694\n", "LBFGS: 117 23:01:25 -464.381512 0.056806\n", "LBFGS: 118 23:01:26 -464.374258 0.027816\n", "LBFGS: 119 23:01:26 -464.374696 0.022396\n", "LBFGS: 120 23:01:26 -464.374962 0.013754\n", "LBFGS: 121 23:01:26 -464.374446 0.015446\n", "LBFGS: 122 23:01:26 -464.373566 0.015807\n", "LBFGS: 123 23:01:26 -464.373069 0.016305\n", "LBFGS: 124 23:01:26 -464.373004 0.019667\n", "LBFGS: 125 23:01:26 -464.373498 0.015478\n", "LBFGS: 126 23:01:26 -464.374284 0.012567\n", "LBFGS: 127 23:01:27 -464.374836 0.012910\n", "LBFGS: 128 23:01:27 -464.375253 0.013640\n", "LBFGS: 129 23:01:27 -464.375471 0.016049\n", "LBFGS: 130 23:01:27 -464.375209 0.010634\n", "LBFGS: 131 23:01:27 -464.374692 0.007285\n", " Step Time Energy fmax\n", "LBFGS: 0 23:01:27 -464.374692 1.766572\n", "LBFGS: 1 23:01:27 -464.462725 1.784124\n", "LBFGS: 2 23:01:27 -464.930709 2.037410\n", "LBFGS: 3 23:01:27 -465.038132 2.040174\n", "LBFGS: 4 23:01:27 -465.216411 1.808290\n", "LBFGS: 5 23:01:27 -465.291586 1.594814\n", "LBFGS: 6 23:01:28 -465.389857 1.099317\n", "LBFGS: 7 23:01:28 -465.443373 0.797319\n", "LBFGS: 8 23:01:28 -465.487367 0.697439\n", "LBFGS: 9 23:01:28 -465.514786 0.484346\n", "LBFGS: 10 23:01:28 -465.533284 0.370899\n", "LBFGS: 11 23:01:28 -465.539897 0.207293\n", "LBFGS: 12 23:01:28 -465.542780 0.164516\n", "LBFGS: 13 23:01:28 -465.544583 0.153188\n", "LBFGS: 14 23:01:28 -465.546483 0.147989\n", "LBFGS: 15 23:01:28 -465.548071 0.117415\n", "LBFGS: 16 23:01:29 -465.549209 0.096051\n", "LBFGS: 17 23:01:29 -465.549970 0.080047\n", "LBFGS: 18 23:01:29 -465.550572 0.078483\n", "LBFGS: 19 23:01:29 -465.551044 0.064087\n", "LBFGS: 20 23:01:29 -465.551462 0.071590\n", "LBFGS: 21 23:01:29 -465.551895 0.063480\n", "LBFGS: 22 23:01:29 -465.552283 0.068117\n", "LBFGS: 23 23:01:29 -465.552555 0.046725\n", "LBFGS: 24 23:01:29 -465.552719 0.033580\n", "LBFGS: 25 23:01:29 -465.552854 0.041930\n", "LBFGS: 26 23:01:30 -465.552990 0.046727\n", "LBFGS: 27 23:01:30 -465.553108 0.033737\n", "LBFGS: 28 23:01:30 -465.553190 0.023715\n", "LBFGS: 29 23:01:30 -465.553271 0.024772\n", "LBFGS: 30 23:01:30 -465.553348 0.031442\n", "LBFGS: 31 23:01:30 -465.553419 0.029045\n", "LBFGS: 32 23:01:30 -465.553476 0.018209\n", "LBFGS: 33 23:01:30 -465.553515 0.017043\n", "LBFGS: 34 23:01:30 -465.553552 0.018637\n", "LBFGS: 35 23:01:31 -465.553580 0.015232\n", "LBFGS: 36 23:01:31 -465.553604 0.013816\n", "LBFGS: 37 23:01:31 -465.553624 0.016252\n", "LBFGS: 38 23:01:31 -465.553651 0.014664\n", "LBFGS: 39 23:01:31 -465.553675 0.014898\n", "LBFGS: 40 23:01:31 -465.553688 0.010265\n", "LBFGS: 41 23:01:31 -465.553700 0.006151\n" ] }, { "data": { "text/plain": [ "np.True_" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(f\"\\nFinding Transition State Initial and Final States\")\n", "print(\" Creating initial guess with stretched C-O bond...\")\n", "print(\" Starting from CO* and stretching the C-O bond...\")\n", "\n", "# Create a guess structure with stretched CO bond (start from CO*)\n", "initial_guess = final_co.copy()\n", "\n", "# Set up a constraint to fix the bond length to ~2 Angstroms, which should be far enough that we'll be closer to *C+*O than *CO\n", "co_indices = np.where(initial_guess.get_tags() == 2)[0]\n", "\n", "# Rotate the atoms a bit just to break the symmetry and prevent the O from going straight up to satisfy the constraint\n", "initial_slab = initial_guess[initial_guess.get_tags() != 2]\n", "initial_co = initial_guess[initial_guess.get_tags() == 2]\n", "initial_co.rotate(30, \"x\", center=initial_co.positions[0])\n", "initial_guess = initial_slab + initial_co\n", "\n", "initial_guess.calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", "\n", "# Add constraints to keep the CO bond length extended\n", "initial_guess.constraints += [\n", " FixBondLengths([co_indices], tolerance=1e-2, iterations=5000, bondlengths=[2.0])\n", "]\n", "\n", "\n", "try:\n", " opt = LBFGS(\n", " initial_guess,\n", " trajectory=output_dir / part_dirs[\"part6\"] / \"initial_guess_with_constraint.traj\",\n", " )\n", " opt.run(fmax=0.01)\n", "except RuntimeError:\n", " # The FixBondLength constraint is sometimes a little finicky,\n", " # but it's ok if it doesn't finish as it's just an initial guess\n", " # for the next step\n", " pass\n", "\n", "# Now that we have a guess, re-relax without the constraints\n", "initial_guess.constraints = initial_guess.constraints[:-1]\n", "opt = LBFGS(\n", " initial_guess,\n", " trajectory=output_dir\n", " / part_dirs[\"part6\"]\n", " / \"initial_guess_without_constraint.traj\",\n", ")\n", "opt.run(fmax=0.01)" ] }, { "cell_type": "markdown", "id": "581d4bac", "metadata": {}, "source": [ "### Step 7: Run NEB to Find Activation Barrier\n", "\n", "Use the nudged elastic band method to find the minimum energy path:" ] }, { "cell_type": "code", "execution_count": 35, "id": "903a5333", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "7. NEB Barrier Calculation (C* + O* → CO*)\n", " Setting up 7-image NEB chain with TS guess in middle...\n", " Reaction: C* + O* (initial) → TS → CO* (final)\n", "\n", " Interpolating images...\n", " Optimizing NEB path (this may take a while)...\n", "\n", " ✓ NEB converged!\n", "\n", " Forward barrier (C*+O* → CO*): 1.32 eV = 127.0 kJ/mol\n", " Reverse barrier (CO* → C*+O*): 2.91 eV = 280.4 kJ/mol\n", "\n", " Paper (Table 5): 153 kJ/mol = 1.59 eV \n", " Difference: 0.27 eV\n" ] } ], "source": [ "print(f\"\\n7. NEB Barrier Calculation (C* + O* → CO*)\")\n", "print(\" Setting up 7-image NEB chain with TS guess in middle...\")\n", "print(\" Reaction: C* + O* (initial) → TS → CO* (final)\")\n", "\n", "initial = initial_guess.copy()\n", "initial.calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", "images = [initial] # Start with C* + O*\n", "\n", "n_images = 10\n", "for i in range(n_images):\n", " image = initial.copy()\n", " image.calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", " images.append(image)\n", "\n", "final = final_co.copy()\n", "final.calc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n", "images.append(final) # End with CO*\n", "\n", "# Interpolate with better initial guess\n", "dyneb = DyNEB(images, climb=True, fmax=0.05)\n", "\n", "# Interpolate first half (C*+O* → TS)\n", "print(\"\\n Interpolating images...\")\n", "dyneb.interpolate(\"idpp\", mic=True)\n", "\n", "# Optimize\n", "print(\" Optimizing NEB path (this may take a while)...\")\n", "opt = FIRE(\n", " dyneb,\n", " trajectory=str(output_dir / part_dirs[\"part6\"] / \"neb.traj\"),\n", " logfile=str(output_dir / part_dirs[\"part6\"] / \"neb.log\"),\n", ")\n", "opt.run(fmax=0.1, steps=relaxation_steps)\n", "\n", "# Extract barrier (from C*+O* to TS)\n", "energies = [img.get_potential_energy() for img in images]\n", "energies_rel = np.array(energies) - energies[0]\n", "E_barrier = np.max(energies_rel)\n", "\n", "print(f\"\\n ✓ NEB converged!\")\n", "print(\n", " f\"\\n Forward barrier (C*+O* → CO*): {E_barrier:.2f} eV = {E_barrier*96.485:.1f} kJ/mol\"\n", ")\n", "print(\n", " f\" Reverse barrier (CO* → C*+O*): {E_barrier - energies_rel[-1]:.2f} eV = {(E_barrier- energies_rel[-1])*96.485:.1f} kJ/mol\"\n", ")\n", "print(f\"\\n Paper (Table 5): 153 kJ/mol = 1.59 eV \")\n", "print(f\" Difference: {abs(E_barrier - 1.59):.2f} eV\")" ] }, { "cell_type": "markdown", "id": "c51c9b4c", "metadata": {}, "source": [ "### Step 8: Visualize NEB Path and Key Structures\n", "\n", "Create plots showing the reaction pathway:" ] }, { "cell_type": "code", "execution_count": 41, "id": "bf34b9f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Creating NEB visualization...\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Image %{x}
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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "✓ NEB analysis complete!\n" ] } ], "source": [ "print(\"\\n Creating NEB visualization...\")\n", "\n", "# Plot NEB path\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "ax.plot(\n", " range(len(energies_rel)),\n", " energies_rel,\n", " \"o-\",\n", " linewidth=2,\n", " markersize=10,\n", " color=\"steelblue\",\n", " label=\"NEB Path\",\n", ")\n", "ax.axhline(0, color=\"green\", linestyle=\"--\", alpha=0.5, label=\"Initial: C*+O*\")\n", "ax.axhline(delta_E_zpe, color=\"red\", linestyle=\"--\", alpha=0.5, label=\"Final: CO*\")\n", "ax.axhline(\n", " E_barrier,\n", " color=\"orange\",\n", " linestyle=\":\",\n", " alpha=0.7,\n", " linewidth=2,\n", " label=f\"Forward Barrier = {E_barrier:.2f} eV\",\n", ")\n", "\n", "# Annotate transition state\n", "ts_idx = np.argmax(energies_rel)\n", "ax.annotate(\n", " f\"TS\\n{energies_rel[ts_idx]:.2f} eV\",\n", " xy=(ts_idx, energies_rel[ts_idx]),\n", " xytext=(ts_idx, energies_rel[ts_idx] + 0.3),\n", " ha=\"center\",\n", " fontsize=11,\n", " fontweight=\"bold\",\n", " arrowprops=dict(arrowstyle=\"->\", lw=1.5, color=\"red\"),\n", ")\n", "\n", "ax.set_xlabel(\"Image Number\", fontsize=13)\n", "ax.set_ylabel(\"Relative Energy (eV)\", fontsize=13)\n", "ax.set_title(\n", " \"CO Formation on Ni(111): C* + O* → CO* - NEB Path\", fontsize=15, fontweight=\"bold\"\n", ")\n", "ax.legend(fontsize=11, loc=\"upper left\")\n", "ax.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig(\n", " str(output_dir / part_dirs[\"part6\"] / \"neb_path.png\"), dpi=300, bbox_inches=\"tight\"\n", ")\n", "plt.show()\n", "\n", "# Create animation of NEB path\n", "print(\"\\n Creating NEB path animation...\")\n", "from ase.io import write as ase_write\n", "\n", "ase.io.write(\n", " str(output_dir / part_dirs[\"part6\"] / \"neb_path.gif\"), images, format=\"gif\"\n", ")\n", "print(\" → Saved as neb_path.gif\")\n", "\n", "# Visualize key structures\n", "print(\"\\n Visualizing initial state (C* + O*)...\")\n", "view(initial_c_o, viewer='x3d')\n", "\n", "print(\"\\n Visualizing transition state...\")\n", "view(images[ts_idx], viewer='x3d')\n", "\n", "print(\"\\n Visualizing final state (CO*)...\")\n", "view(final_co, viewer='x3d')\n", "\n", "print(\"\\n✓ NEB analysis complete!\")" ] }, { "cell_type": "markdown", "id": "0bbcc026", "metadata": {}, "source": [ "```{admonition} Missing UMA access?\n", ":class: dropdown, tip\n", "\n", "Don't have access to UMA yet? You can still explore this calculation!\n", "\n", "[Download example CO on Ni(111) structure](example_configs/co_on_ni111.xyz) and [Download C+O on Ni(111) structure](example_configs/c_o_on_ni111.xyz) to test in the [UMA demo (no login required)](https://facebook-fairchem-uma-demo.hf.space/) and explore the reaction pathway.\n", "```\n", "\n", "```{admonition} Comparison with Paper (Tables 4 & 5)\n", ":class: note\n", "\n", "**Expected Results from Paper:**\n", "- **Reaction Energy (C* + O* → CO*)**: **-142.7 kJ/mol = -1.48 eV** (exothermic, DFT-D3)\n", "- **Activation Barrier (C* + O* → CO*)**: **153 kJ/mol = 1.59 eV** (reverse/dissociation, DFT-D3)\n", "- **CO Adsorption Energy**: **1.82 eV = 175.6 kJ/mol** (DFT-D3)\n", "\n", "**Reaction Direction:**\n", "- Paper reports CO dissociation barrier (CO* → C* + O*), which is the **reverse** of the recombination we calculate\n", "- Forward (C* + O* → CO*): barrier = reverse_barrier - |ΔE| ≈ 1.59 - 1.48 ≈ 0.11 eV (very fast)\n", "- Reverse (CO* → C* + O*): barrier = 1.59 eV (very slow, kinetic bottleneck)\n", "\n", "**What to Check:**\n", "- Reaction energy (C*+O* → CO*) should be strongly exothermic (~-1.5 eV)\n", "- Reverse barrier (CO dissociation) should be substantial (~1.6 eV)\n", "- Forward barrier (recombination) should be very small (~0.1 eV)\n", "- CO binds much more strongly than H (1.82 eV vs 0.60 eV)\n", "\n", "**Typical Variations:**\n", "- Reaction energies typically accurate within 0.1-0.2 eV\n", "- Barriers more sensitive: expect ±0.2-0.3 eV variation\n", "- ZPE corrections typically add 0.05-0.15 eV to reaction energies\n", "- NEB convergence affects barrier more than reaction energy\n", "\n", "**Physical Insight:**\n", "- Large reverse barrier (1.59 eV) makes CO dissociation very slow at low T\n", "- Small forward barrier (0.11 eV) means C+O rapidly recombine to CO\n", "- This explains why Ni produces CO in Fischer-Tropsch rather than keeping C and O separate\n", "- High temperatures needed to overcome the dissociation barrier for further C-C coupling\n", "```\n", "\n", "```{admonition} NEB Method Explained\n", ":class: note\n", "\n", "The **Nudged Elastic Band (NEB)** method finds the minimum energy path between reactants and products:\n", "\n", "1. **Interpolate** between initial and final states (5-9 images typical)\n", "2. **Add spring forces** along the chain to maintain spacing\n", "3. **Project out** spring components perpendicular to the path\n", "4. **Climbing image** variant: highest energy image climbs to saddle point\n", "\n", "Advantages:\n", "- No prior knowledge of transition state needed\n", "- Finds entire reaction coordinate\n", "- Robust for complex reactions\n", "\n", "Limitations:\n", "- Computationally expensive (optimize N images)\n", "- May find wrong path if initial interpolation is poor\n", "```\n", "\n", "### Explore on Your Own\n", "\n", "1. **Image convergence**: Run with 7 or 9 images. Does the barrier change?\n", "2. **Spring constant**: Modify the NEB spring constant. How does this affect convergence?\n", "3. **Alternative paths**: Try different initial CO/final C+O configurations. Are there multiple pathways?\n", "4. **Reverse barrier**: Calculate E_a(reverse) = E_a(forward) - ΔE. Check Brønsted-Evans-Polanyi relationship.\n", "5. **Diffusion barriers**: Compute NEB for C or O diffusion on the surface. How do they compare?\n", "\n", "---\n", "\n", "## Summary and Best Practices\n", "\n", "### Key Takeaways\n", "\n", "1. **ML Potentials**: uma-s-1p2p1 provides ~1000× speedup over DFT with reasonable accuracy\n", "2. **Bulk optimization**: Always use the ML-optimized lattice constant for consistency\n", "3. **Surface energies**: Linear extrapolation eliminates finite-size effects\n", "4. **Adsorption**: Test multiple sites; lowest energy may not be intuitive\n", "5. **Coverage**: Lateral interactions become significant above ~0.3 ML\n", "6. **Barriers**: NEB requires careful setup but yields full reaction pathway\n", "\n", "### Recommended Workflow for New Systems\n", "\n", "1. **Optimize Bulk** - Determine equilibrium lattice constant\n", "2. **Calculate Surface Energies** - Identify stable facets\n", "3. **Wulff Construction** - Predict nanoparticle morphology\n", "4. **Low-Coverage Adsorption** - Find binding sites and energies\n", "5. **Coverage Study** (if coverage-dependent effects are important) - Determine lateral interactions\n", "6. **Reaction Barriers** - Calculate activation energies using NEB\n", "7. **Microkinetic Modeling** - Predict overall catalytic performance\n", "\n", "### Accuracy Considerations\n", "\n", "| Property | Typical Error | When Critical |\n", "|----------|--------------|---------------|\n", "| Lattice constants | 1-2% | Strain effects, alloys |\n", "| Surface energies | 10-20% | Nanoparticle shapes |\n", "| Adsorption energies | 0.1-0.3 eV | Thermochemistry |\n", "| Barriers | 0.2-0.5 eV | Kinetics, selectivity |\n", "\n", "**Rule of thumb**: Use ML for screening → DFT for validation → Experiment for verification\n", "\n", "### Further Reading\n", "\n", "- **UMA Paper**: [Wood et al. 2025](https://arxiv.org/abs/2506.23971)\n", "- **OMat24 Paper**: [Barroso-Luque et al., 2024](https://arxiv.org/abs/2410.12771)\n", "- **OC20 Dataset**: [Chanussot et al., ACS Catalysis, 2021](https://pubs.acs.org/doi/full/10.1021/acscatal.0c04525)\n", "- **ASE Tutorial**: [https://wiki.fysik.dtu.dk/ase/](https://wiki.fysik.dtu.dk/ase/)\n", "---\n", "\n", "## Appendix: Troubleshooting\n", "\n", "### Common Issues\n", "\n", "**Problem**: Convergence failures\n", "- **Solution**: Reduce `fmax` to 0.1 initially, tighten later\n", "- Check if system is metastable (try different starting geometry)\n", "\n", "**Problem**: NEB fails to find transition state\n", "- **Solution**: Use more images (9-11) or better initial guess\n", "- Try fixed-end NEB first, then climbing image\n", "\n", "**Problem**: Unexpected adsorption energies\n", "- **Solution**: Visualize structures - check for distortions\n", "- Compare with multiple sites\n", "- Add D3 corrections\n", "\n", "**Problem**: Out of memory\n", "- **Solution**: Reduce system size (smaller supercells)\n", "- Use fewer NEB images\n", "- Run on HPC with more RAM\n", "\n", "### Performance Tips\n", "\n", "1. **Use batching**: Relax multiple configurations in parallel\n", "2. **Start with DEBUG_MAX_STEPS=50**: Get quick results, refine later\n", "3. **Cache bulk energies**: Don't recalculate reference systems\n", "4. **Trajectory analysis**: Monitor optimization progress with ASE GUI\n", "\n", "---\n", "\n", "## Caveats and Pitfalls\n", "\n", "```{admonition} Important Considerations\n", ":class: warning\n", "\n", "When using ML potentials for surface catalysis, be aware of these critical issues!\n", "```\n", "\n", "### 1. Task Selection: OMAT vs OC20\n", "\n", "**Critical choice**: Which task_name to use?\n", "- **`task_name=\"omat\"`**: Optimized for bulk and clean surface calculations\n", " - Use for: Part 1 (bulk), Part 2 (surface energies), Part 3 (Wulff)\n", " - Better for structural relaxations without adsorbates\n", "- **`task_name=\"oc20\"`**: Optimized for surface chemistry with adsorbates\n", " - Use for: Part 4-6 (all adsorbate calculations)\n", " - Trained on Open Catalyst data with adsorbate-surface interactions\n", "\n", "**Impact**: Using wrong task can lead to 0.1-0.3 eV errors in adsorption energies!\n", "\n", "### 2. D3 Dispersion Corrections\n", "\n", "**Multiple decisions required**:\n", "\n", "1. **Whether to use D3 at all?**\n", " - Small adsorbates (H, O, N): D3 effect ~0.01-0.05 eV (often negligible)\n", " - Large molecules (CO, CO₂, aromatics): D3 effect ~0.1-0.3 eV (important!)\n", " - Physisorption: D3 critical (can change binding from repulsive to attractive)\n", " - RPBE was originally fit for chemisorption energies without D3 corrections, so adding D3 corrections may actually cause small adsorbates to overbind. However, it probably would be important for larger molecules. It's relatively uncommon to see RPBE+D3 as a choice in the catalysis literature (compared to PBE+D3, or RPBE, or BEEF-vdW).\n", "\n", "2. **Which DFT functional for D3?**\n", " - This tutorial uses `method=\"PBE\"` consistently for the D3 correction. This is often implied when papers say they use a D3 correction, but the results can be different if use the RPBE parameterizations.\n", " - Original paper used PBE for bulk/surfaces, RPBE for adsorption. It's not specified what D3 parameterization they used, but it's likely PBE.\n", "\n", "3. **When to apply D3?**\n", " - **End-point correction** (used here): Fast, run ML optimization then add D3 energy\n", " - **During optimization**: Slower but more accurate geometries\n", " - **Impact**: Usually <0.05 eV difference, but can be larger for weak interactions\n", "\n", "### 3. Coverage Dependence Challenges\n", "\n", "**Non-linearity at high coverage**:\n", "- This tutorial assumes linear E_ads(θ) = E₀ + βθ\n", "- Reality: Often non-linear, especially near θ = 1 ML. See the plots generated - there is a linear regime for relatively high coverage, and relatively low coverage, but it's not uniformly linear everywhere. As long as you consistently in one regime or the other a linear assumption is probably ok, but you could get into problems if solving microkinetic models where the coverage of the species in question changes significantly from very low to high.\n", "- **Why**: Phase transitions, adsorbate ordering, surface reconstruction\n", "- **Solution**: Test polynomial fits, look for ordering in visualizations\n", "\n", "**Low coverage limit**:\n", "- At θ < 0.1 ML, coverage effects are tiny (<0.01 eV)\n", "- Hard to distinguish from numerical noise\n", "- **Best practice**: Focus on 0.25-1.0 ML range for fitting\n", "\n", "### 4. Periodic Boundary Conditions\n", "\n", "**UMa requires PBC=True in all directions!**\n", "```python\n", "atoms.set_pbc([True, True, True]) # Always required\n", "```\n", "- Forgetting this causes crashes or wrong energies\n", "- Even for \"gas phase\" molecules in vacuum\n", "\n", "### 5. Gas Phase Reference Energies\n", "\n", "**Tricky cases**:\n", "- **H₂(g)**: UMa handles well (used in this tutorial)\n", "- **H(g)**: May not be reliable (use H₂/2 instead)\n", "- **CO(g)**, **O₂(g)**: Usually okay, but check against DFT\n", "- **Radicals**: Often problematic\n", "\n", "**Best practice**: Always use stable molecules as references (H₂, not H; H₂O, not OH)\n", "\n", "### 6. Spin Polarization\n", "\n", "**Key limitation**: OC20/UMa does not include spin!\n", "- Paper used spin-polarized DFT\n", "- **Impact**: Usually small (0.05-0.1 eV)\n", "- **Larger** for:\n", " - Magnetic metals (Fe, Co, Ni)\n", " - Open-shell adsorbates (O*, OH*)\n", " - Reaction barriers with radicals\n", "\n", "### 7. Constraint Philosophy\n", "\n", "**Clean slabs** (Part 2): No constraints (both surfaces relax)\n", "- Best for surface energy calculations\n", "- More physical for symmetric slabs\n", "\n", "**Adsorbate slabs** (Part 4-6): Bottom layers fixed\n", "- Faster convergence\n", "- Prevents adsorbate-induced reconstruction\n", "- Standard practice in surface chemistry\n", "\n", "**Fairchem helper functions**: Automatically apply sensible constraints\n", "- Trust their heuristics unless you have good reason not to\n", "- Check `atoms.constraints` to see what was applied\n", "\n", "### 8. Complex Surface Structures\n", "\n", "**This tutorial uses low-index facets** (111, 100, 110, 211)\n", "- Well-defined, symmetric\n", "- Easy to generate and analyze\n", "\n", "**Real catalysts** have:\n", "- Steps, kinks, grain boundaries\n", "- Support interfaces\n", "- Defects and vacancies\n", "- **Challenge**: Harder to generate, more configurations to test\n", "\n", "### 9. Slab Thickness and Vacuum\n", "\n", "**Convergence tests critical** but expensive:\n", "- This tutorial uses \"reasonable\" values (4-8 layers, 10 Å vacuum)\n", "- **Always check** convergence for new systems\n", "- **Especially important** for:\n", " - Metals with long electron screening (Au, Ag)\n", " - Charged adsorbates\n", " - Strong adsorbate-induced reconstruction\n", "\n", "### 10. NEB Convergence\n", "\n", "**Most computationally expensive part**:\n", "- May need 7-11 images (not just 5)\n", "- Initial guess matters a lot\n", "- Can get stuck in local minima\n", "\n", "**Tricks**:\n", "1. Use dimer method to find better TS guess (as shown in Part 6)\n", "2. Start with coarse convergence (fmax=0.2), refine later\n", "3. Visualize the path - does it make chemical sense?\n", "4. Try different spring constants (0.1-1.0 eV/Å)\n", "\n", "### 11. Lattice Constant Source\n", "\n", "**Consistency is key**:\n", "- Use ML-optimized lattice constant throughout (as done here)\n", "- **Don't mix**: ML lattice + DFT surface energies = inconsistent\n", "- Alternative: Use experimental lattice constant for everything\n", "\n", "### 12. Adsorbate Placement\n", "\n", "**Multiple local minima**:\n", "- Surface chemistry is **not** convex!\n", "- Always test multiple adsorption sites\n", "- Fairchem helpers generate ~5 configurations in this tutorial, but you may need more to search many modes. You can already try methods like minima hopping or other global optimization methods to sample more configurations.\n", "\n", "**For complex adsorbates**:\n", "- Test different orientations\n", "- May need 10-20 configurations\n", "- Consider genetic algorithms or basin hopping\n", "\n", "---\n", "\n", "```{admonition} Congratulations! 🎉\n", ":class: tip\n", "\n", "You've completed a comprehensive computational catalysis workflow using state-of-the-art ML potentials. You can now:\n", "\n", "- Characterize catalyst surfaces computationally\n", "- Predict nanoparticle shapes\n", "- Calculate reaction thermodynamics and kinetics\n", "- Apply these methods to your own research questions\n", "\n", "**Next steps**:\n", "- Apply to your catalyst system of interest\n", "- Validate key results with DFT\n", "- Develop microkinetic models\n", "- Publish your findings!\n", "```" ] } ], "metadata": { "jupytext": { "default_lexer": "ipython3", "formats": "ipynb,md:myst" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }