FAIRChem v2 UMA for Multidomain Atomistic Simulation across Molecules, Catalysts, Materials, Vibrations, and Molecular Dynamics

In this tutorial, we explore FAIRChem v2 and the UMA universal machine-learning interatomic potential as a unified framework for atomistic simulation across molecular chemistry, catalysis, and inorganic materials. We configure an environment, authenticate with Hugging Face to access the gated UMA model weights, and initialize task-specific calculators for the omol, oc20, and omat domains. We then apply the same pretrained potential to a broad set of computational chemistry workflows, including single-point energy and force prediction, molecular geometry optimization, spin-state comparison, reaction-energy estimation, vibrational analysis, surface adsorption, crystal-cell relaxation, equation-of-state fitting, molecular dynamics, and potential-energy surface scanning. Throughout the tutorial, we integrate FAIRChem with the Atomic Simulation Environment to manage atomic structures, optimizers, constraints, thermodynamic calculations, and trajectory analysis while using GPU acceleration whenever it is available. Copy CodeCopiedUse a different Browserimport importlib.util, subprocess, sys, os def _pip(*pkgs): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *pkgs]) if importlib.util.find_spec("fairchem") is None: print(">> Installing fairchem-core, ase, and helpers (takes ~2-4 min)...") _pip("fairchem-core", "ase", "matplotlib", "huggingface_hub") print(">> Installation done.") else: print(">> fairchem already installed.") from huggingface_hub import login, whoami def hf_authenticate(): token = None try: from google.colab import userdata token = userdata.get("HF_TOKEN") except Exception: pass token = token or os.environ.get("HF_TOKEN") try: whoami() print(">> Already authenticated with Hugging Face.") return except Exception: pass if token is None: from getpass import getpass token = getpass("Paste your Hugging Face access token: ").strip() login(token=token) print(">> Hugging Face login OK.") hf_authenticate() import numpy as np import torch import matplotlib.pyplot as plt from fairchem.core import pretrained_mlip, FAIRChemCalculator DEVICE = "cuda" if torch.cuda.is_available() else "cpu" print(f">> Using device: {DEVICE}") MODEL = "uma-s-1p2" predictor = pretrained_mlip.get_predict_unit(MODEL, device=DEVICE) calc_mol = FAIRChemCalculator(predictor, task_name="omol") calc_cat = FAIRChemCalculator(predictor, task_name="oc20") calc_mat = FAIRChemCalculator(predictor, task_name="omat") print(f">> Loaded {MODEL} with omol / oc20 / omat calculators.") We install the required FAIRChem, ASE, visualization, and Hugging Face dependencies while ensuring the setup remains safe to rerun in Google Colab. We authenticate with Hugging Face to access the gated UMA model weights and automatically detect whether GPU acceleration is available. We then load the UMA predictor and create separate calculators for molecular, catalysis, and materials simulations. Copy CodeCopiedUse a different Browserfrom ase.build import molecule from ase import Atoms print("\n" + "="*70) print("SECTION 2: Single-point energetics of water (omol task)") print("="*70) h2o = molecule("H2O") h2o.info.update({"charge": 0, "spin": 1}) h2o.calc = calc_mol E_h2o = h2o.get_potential_energy() F_h2o = h2o.get_forces() print(f"E(H2O) = {E_h2o:.4f} eV") print(f"Max |force| = {np.abs(F_h2o).max():.4f} eV/A") def atom_energy(symbol, spin): a = Atoms(symbol, positions=[[0, 0, 0]]) a.info.update({"charge": 0, "spin": spin}) a.calc = calc_mol return a.get_potential_energy() E_O = atom_energy("O", spin=3) E_H = atom_energy("H", spin=2) E_atomization = -(E_h2o - E_O - 2 * E_H) print(f"Atomization energy of H2O = {E_atomization:.3f} eV " f"(experiment ~ 9.5 eV incl. ZPE effects)") from ase.optimize import LBFGS print("\n" + "="*70) print("SECTION 3: Relaxing a deliberately distorted water molecule") print("="*70) h2o_bad