FAIRChem Skill

FAIRChem Skill

This skill provides expert guidance for using FAIRChem (formerly OCP - Open Catalyst Project), Meta's FAIR Chemistry library of machine learning methods for materials science and quantum chemistry.

When to Use This Skill

Use this skill when:

• Using ML potentials for materials and molecular simulations
• Running fast geometry optimizations with pretrained models
• Performing large-scale MD simulations
• Calculating energies and forces without DFT
• Working with the UMA (Universal Materials Algebra) models
• Needing predictions for catalysis, molecules, crystals, or MOFs
• Integrating ML models with ASE workflows
• Scaling calculations across multiple GPUs

What is FAIRChem?

FAIRChem is Meta's machine learning framework for chemistry that provides:

• Pretrained UMA models (uma-s-1p1, uma-m-1p1) for universal predictions
• Domain-specific tasks: catalysis (oc20), materials (omat), molecules (omol), MOFs (odac), crystals (omc)
• ASE integration via FAIRChemCalculator
• Multi-GPU support for distributed inference
• Fast predictions: 100-1000× faster than DFT

Key Advantage

FAIRChem allows you to use the same model across different chemistry domains by simply changing the task_name parameter.

Core Concepts

1. UMA Models

Universal Materials Algebra models trained on diverse datasets:

• uma-s-1p1: Small model (~50M parameters) - faster inference
• uma-m-1p1: Medium model (~300M parameters) - higher accuracy

2. Task Names (Domains)

Specify the chemistry domain for domain-specific predictions:

• oc20: Catalysis (surfaces with adsorbates)
• omat: Inorganic materials (crystals, bulk)
• omol: Molecules (organic chemistry)
• odac: Metal-organic frameworks (MOFs)
• omc: Molecular crystals

3. FAIRChemCalculator

ASE calculator interface that wraps UMA models:

• Drop-in replacement for DFT calculators
• Supports all ASE workflows
• Provides energies, forces, and stresses
• Compatible with optimization, MD, NEB

4. Inference Settings

Performance optimization modes:

• turbo: Maximum speed, slightly reduced accuracy
• Standard: Balanced speed and accuracy
• Multi-GPU: Distributed inference with workers=N

Installation

# Install fairchem
pip install fairchem-core

# For GPU support
pip install fairchem-core[gpu]

# Hugging Face login (required for UMA models)
pip install huggingface-hub
huggingface-cli login

Note: You must have a Hugging Face account and request access to the UMA model repository.

Basic Usage Pattern

Standard Workflow

from fairchem.data.ase import FAIRChemCalculator
from fairchem.predict import load_predict_unit
from ase.build import bulk
from ase.optimize import LBFGS

# 1. Load pretrained model
predict_unit = load_predict_unit("uma-m-1p1")

# 2. Create calculator for specific domain
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="omat"  # Choose domain
)

# 3. Use with ASE
atoms = bulk("Cu", "fcc", a=3.6)
atoms.calc = calc

# 4. Calculate properties
energy = atoms.get_potential_energy()
forces = atoms.get_forces()

# 5. Optimize structure
opt = LBFGS(atoms)
opt.run(fmax=0.05)

Common Workflows

Workflow 1: Catalysis - Surface Adsorption

from fairchem.data.ase import FAIRChemCalculator
from fairchem.predict import load_predict_unit
from ase.build import fcc111, add_adsorbate
from ase.optimize import LBFGS
from ase.constraints import FixAtoms

# Load model
predict_unit = load_predict_unit("uma-m-1p1")

# Create calculator for catalysis
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="oc20"  # Catalysis domain
)

# Build slab with adsorbate
slab = fcc111("Cu", size=(4, 4, 4), vacuum=10.0)
add_adsorbate(slab, "CO", height=2.0, position="fcc")

# Fix bottom layers
n_atoms_per_layer = 16
constraint = FixAtoms(indices=range(n_atoms_per_layer * 2))
slab.set_constraint(constraint)

# Attach calculator and optimize
slab.calc = calc
opt = LBFGS(slab, trajectory="slab_opt.traj")
opt.run(fmax=0.05)

# Get results
E = slab.get_potential_energy()
forces = slab.get_forces()

Workflow 2: Bulk Materials - Lattice Optimization

from fairchem.data.ase import FAIRChemCalculator
from fairchem.predict import load_predict_unit
from ase.build import bulk
from ase.optimize import FIRE
from ase.filters import FrechetCellFilter

# Load model
predict_unit = load_predict_unit("uma-m-1p1")

# Calculator for materials
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="omat"  # Materials domain
)

# Create bulk structure
atoms = bulk("Fe", "bcc", a=2.87)
atoms.calc = calc

# Optimize both positions and cell
# FrechetCellFilter allows cell parameters to change
ucf = FrechetCellFilter(atoms)
opt = FIRE(ucf)
opt.run(fmax=0.05)

# Results
optimized_lattice = atoms.cell.cellpar()[0]
print(f"Optimized lattice constant: {optimized_lattice:.3f} Å")

Workflow 3: Molecular Dynamics

from fairchem.data.ase import FAIRChemCalculator
from fairchem.predict import load_predict_unit
from ase.build import bulk
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.md.langevin import Langevin
from ase import units

# Load with turbo settings for speed
predict_unit = load_predict_unit(
    "uma-s-1p1",  # Use small model for MD
    inference_settings="turbo"
)

# Calculator for MD
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="omat",
    workers=4  # Multi-GPU for large systems
)

# Large system
atoms = bulk("C", "fcc", a=3.57) * (20, 20, 20)  # 8000 atoms
atoms.calc = calc

# Initialize velocities
MaxwellBoltzmannDistribution(atoms, temperature_K=300)

# Run NVT dynamics
dyn = Langevin(
    atoms,
    timestep=1.0 * units.fs,
    temperature_K=300,
    friction=0.002
)

# Run
from ase.io.trajectory import Trajectory
traj = Trajectory("md.traj", "w", atoms)
dyn.attach(traj.write, interval=10)
dyn.run(5000)

Workflow 4: Molecular Systems

from fairchem.data.ase import FAIRChemCalculator
from fairchem.predict import load_predict_unit
from ase.build import molecule
from ase.optimize import LBFGS

# Load model
predict_unit = load_predict_unit("uma-m-1p1")

# Calculator for molecules
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="omol"  # Molecular domain
)

# Build molecule
mol = molecule("H2O")
mol.center(vacuum=10.0)
mol.calc = calc

# Optimize
opt = LBFGS(mol, trajectory="mol_opt.traj")
opt.run(fmax=0.05)

# Get properties
E = mol.get_potential_energy()
forces = mol.get_forces()

Workflow 5: NEB Calculations

from fairchem.data.ase import FAIRChemCalculator
from fairchem.predict import load_predict_unit
from ase.neb import NEB
from ase.optimize import BFGS
from ase.io import read

# Load model
predict_unit = load_predict_unit("uma-m-1p1")

# Calculator
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="oc20"
)

# Load initial and final states
initial = read("initial.traj")
final = read("final.traj")

# Create NEB
images = [initial]
images += [initial.copy() for i in range(5)]
images += [final]

neb = NEB(images)
neb.interpolate()

# Attach calculator to intermediate images
for image in images[1:-1]:
    image.calc = calc

# Optimize
opt = BFGS(neb, trajectory="neb.traj")
opt.run(fmax=0.05)

# Analyze
energies = [img.get_potential_energy() for img in images]
barrier = max(energies) - energies[0]
print(f"Barrier: {barrier:.3f} eV")

Model Loading Options

Load Pretrained UMA Model

from fairchem.predict import load_predict_unit

# Standard loading
predict_unit = load_predict_unit("uma-m-1p1")

# With turbo mode (faster, slight accuracy trade-off)
predict_unit = load_predict_unit(
    "uma-m-1p1",
    inference_settings="turbo"
)

# Specify device
predict_unit = load_predict_unit(
    "uma-m-1p1",
    device="cuda:0"
)

# Load local checkpoint
predict_unit = load_predict_unit(
    "/​path/​to/​checkpoint.pt",
    device="cuda"
)

Available Models

• uma-s-1p1: Small, fast (~50M params)
• uma-m-1p1: Medium, accurate (~300M params)

Task Selection Guide

Performance Optimization

Multi-GPU Inference

# Use multiple GPUs automatically
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="omat",
    workers=8  # Use 8 GPUs
)

# Achieves ~10× speedup on 8× H100 GPUs

Turbo Mode

# Trade slight accuracy for speed
predict_unit = load_predict_unit(
    "uma-s-1p1",  # Small model
    inference_settings="turbo"
)

# Good for:
# - MD simulations
# - Large systems
# - Initial screening

Batch Predictions

# For multiple similar calculations
from fairchem.data.ase import batch_predict

structures = [atoms1, atoms2, atoms3, ...]

results = batch_predict(
    structures,
    predict_unit=predict_unit,
    task_name="omat"
)

Best Practices

1. Task Selection

Always choose the appropriate task for your system:

• Surfaces with adsorbates → oc20
• Bulk materials → omat
• Isolated molecules → omol
• MOFs → odac
• Molecular crystals → omc

2. Model Selection

• Initial screening: Use uma-s-1p1 + turbo
• Production calculations: Use uma-m-1p1
• Very large systems: Use uma-s-1p1 + workers

3. Validation

ML models have different error characteristics than DFT:

# Always validate critical results
# Compare ML prediction with DFT for representative cases
ml_energy = atoms.get_potential_energy()  # FAIRChem
atoms.calc = Vasp(...)  # Switch to DFT
dft_energy = atoms.get_potential_energy()
error = abs(ml_energy - dft_energy)

4. Uncertainty Quantification

FAIRChem models provide predictions but not uncertainty:

• Test on similar known systems first
• Validate against DFT for critical results
• Use ensemble of predictions if available

5. Memory Management

For large systems:

# Use turbo mode
predict_unit = load_predict_unit(
    "uma-s-1p1",
    inference_settings="turbo"
)

# Distribute across GPUs
calc = FAIRChemCalculator(
    predict_unit=predict_unit,
    task_name="omat",
    workers=4
)

Common Patterns

Energy Calculation

atoms.calc = calc
energy = atoms.get_potential_energy()  # eV
forces = atoms.get_forces()  # eV/Å
stress = atoms.get_stress()  # eV/ų

Geometry Optimization

from ase.optimize import LBFGS, FIRE, BFGS

# Fast convergence
opt = LBFGS(atoms, trajectory="opt.traj")
opt.run(fmax=0.05)

# For difficult systems
opt = FIRE(atoms)
opt.run(fmax=0.05)

Cell Optimization

from ase.filters import FrechetCellFilter

# Optimize both atoms and cell
ucf = FrechetCellFilter(atoms)
opt = FIRE(ucf)
opt.run(fmax=0.05)

Integration with ASE

FAIRChemCalculator is a full ASE calculator:

# All ASE functionality works
from ase.vibrations import Vibrations
from ase.thermochemistry import IdealGasThermo
from ase.eos import calculate_eos

# Vibrational analysis
vib = Vibrations(atoms)
vib.run()

# Thermochemistry
thermo = IdealGasThermo(...)

# Equation of state
eos = calculate_eos(atoms)

Troubleshooting

Hugging Face Authentication

# Login to Hugging Face
huggingface-cli login

# Request access to UMA models at:
# https://huggingface.co/​meta-llama/​uma-m-1p1

GPU Memory Issues

# Use smaller model
predict_unit = load_predict_unit("uma-s-1p1")

# Use turbo mode
predict_unit = load_predict_unit(
    "uma-s-1p1",
    inference_settings="turbo"
)

# Reduce batch size (if using batch predictions)

Slow Inference

# Enable turbo mode
inference_settings="turbo"

# Use multiple GPUs
workers=N

# Use smaller model
"uma-s-1p1"

Wrong Task Selection

# Symptoms: Poor predictions, unphysical results
# Solution: Verify task matches your system

# For surfaces + adsorbates:
task_name="oc20"  # NOT "omat" or "omol"

# For bulk materials:
task_name="omat"  # NOT "oc20"

Version Compatibility

Important: FAIRChem v2 is a breaking change from v1

• v2 code is NOT compatible with v1 models
• v1 code is NOT compatible with v2 models
• UMA models require FAIRChem >= 2.0

# Check version
import fairchem
print(fairchem.__version__)  # Should be >= 2.0 for UMA

Comparison with DFT

When to Use FAIRChem vs DFT

Use FAIRChem for:

• Initial screening of many structures
• Long MD simulations
• Large systems (>500 atoms)
• Rapid prototyping
• High-throughput workflows

Use DFT for:

• Final validation
• Novel chemistries outside training data
• When highest accuracy needed
• Electronic structure analysis
• Magnetic properties

Resources

When suggesting FAIRChem solutions:

• Specify correct task_name for the domain
• Recommend appropriate model (s vs m)
• Suggest performance optimizations (turbo, workers)
• Include validation against known results
• Mention Hugging Face authentication requirement
• Provide complete working examples
• Note v2 compatibility requirements

Example Response Pattern

When helping with FAIRChem:

1. Identify the chemistry domain (catalysis, materials, molecules, etc.)
2. Select appropriate task_name
3. Choose model based on accuracy/​speed requirements
4. Provide complete code with imports
5. Suggest performance optimizations if needed
6. Recommend validation steps
7. Note any domain-specific considerations