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

Batch scripting vs. interactive steering (fairchem-mcp)

This skill covers the batch workflow: write a Python script, run it, read the output. That is the right default for reproducible, unattended calculations.

For interactive, agent-steered simulation, there is a companion MCP server, fairchem-mcp (https://github.com/jkitchin/fairchem-mcp). It keeps the model resident, runs relaxations/MD in the background, and lets you watch a job and steer it mid-flight. Prefer it when:

• You want to monitor a long relaxation/MD and react to a trend verdict (decreasing / plateaued / stuck / diverging).
• You want to change strategy mid-run — switch optimizer when FIRE stalls, tighten/loosen fmax, change temperature, pause or abort — without restarting.
• You want the model loaded once and reused across many quick calls.
• You want to introspect the installed API or live objects (introspect) or drop to raw Python on the same live Atoms (execute / inspect_expr).

Key tools: attach_emt / load_model / attach_lammps (LAMMPS classical potentials as the force engine), build_structure, start_relaxation / start_md, start_neb (reaction barriers, steer set_climb near convergence) and start_phonons (frequencies + imaginary-mode/stability check), start_minima_search (multiple distinct relaxed geometries via deflation/flooding — adsorption sites, cluster isomers, conformers), start_eos_scan (strain scan → equation-of-state fit: V0, E0, bulk modulus), start_elastic_scan (stress-vs-strain → elastic tensor C_ij, VRH moduli, Born stability), start_convex_hull (formation energies → phase stability / energy above hull, e.g. alloy design), transition-state searches start_saddle_search (dimer, Hessian-free), start_sella_search (Sella RFO), and start_pounce_saddles (enumerate saddles by Morse index), get_status, get_results, steer, introspect. Register it with {"mcpServers": {"fairchem": {"command": "fairchem-mcp"}}}. Use plain batch scripts (below) when you don't need live observation or steering.

What is FAIRChem?

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

• Pretrained UMA models (uma-s-1p2, uma-m-1p1) for universal predictions
• Domain-specific tasks: catalysis (oc20, oc22, oc25), materials (omat), molecules (omol), MOFs (odac), crystals (omc)
• ASE integration via FAIRChemCalculator
• Multi-GPU support for distributed inference via Ray
• 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-1p2: Small model (6.6M/290M parameters) - fastest inference, best small model
• uma-s-1p1: Small model (6.6M/150M parameters) - previous generation
• uma-m-1p1: Medium model (50M/1.4B parameters) - highest accuracy

2. Task Names (Domains)

Specify the chemistry domain for domain-specific predictions:

• oc20: Catalysis (surfaces with adsorbates)
• oc22: Oxide catalysis (uma-s-1p2 only)
• oc25: Electrocatalysis (uma-s-1p2 only)
• omat: Inorganic materials (crystals, bulk)
• omol: Molecules (organic chemistry, polymers)
• 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:

• default: Balanced speed and accuracy
• turbo: Faster inference, slightly reduced accuracy
• turbo_umas: Maximum speed for UMA-S models (uses triton kernels)
• Execution modes: general, umas_fast_pytorch, umas_fast_gpu

Installation

# Install fairchem
pip install fairchem-core

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

# For multi-GPU support (Ray)
pip install fairchem-core[extras]

# 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 at facebook/UMA.

Basic Usage Pattern

Standard Workflow

from fairchem.core import pretrained_mlip, FAIRChemCalculator
from ase.build import bulk
from ase.optimize import LBFGS

# 1. Load pretrained model
predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")

# 2. Create calculator for specific domain
calc = FAIRChemCalculator(
    predictor,
    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)

Convenience Method

from fairchem.core import FAIRChemCalculator

# One-step calculator creation
calc = FAIRChemCalculator.from_model_checkpoint(
    "uma-m-1p1",
    task_name="omat",
    inference_settings="turbo"
)

Common Workflows

Workflow 1: Catalysis - Surface Adsorption

from fairchem.core import pretrained_mlip, FAIRChemCalculator
from ase.build import fcc111, add_adsorbate
from ase.optimize import LBFGS
from ase.constraints import FixAtoms

# Load model
predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")

# Create calculator for catalysis
calc = FAIRChemCalculator(
    predictor,
    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.core import pretrained_mlip, FAIRChemCalculator
from ase.build import bulk
from ase.optimize import FIRE
from ase.filters import FrechetCellFilter

# Load model
predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")

# Calculator for materials
calc = FAIRChemCalculator(
    predictor,
    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.core import pretrained_mlip, FAIRChemCalculator
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
predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p2",  # Use latest small model for MD
    inference_settings="turbo_umas"  # Maximum speed for UMA-S
)

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

# Large system
atoms = bulk("C", "diamond", a=3.57) * (10, 10, 10)
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.core import pretrained_mlip, FAIRChemCalculator
from ase.build import molecule
from ase.optimize import LBFGS

# Load model
predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")

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

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

# Set spin and charge for omol (defaults: charge=0, spin=1)
mol.info.update({"spin": 1, "charge": 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.core import pretrained_mlip, FAIRChemCalculator
from ase.neb import NEB
from ase.optimize import BFGS
from ase.io import read

# Load model
predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")

# Calculator
calc = FAIRChemCalculator(
    predictor,
    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")

Workflow 6: Formation Energy Calculation

from fairchem.core import pretrained_mlip, FAIRChemCalculator

# FormationEnergyCalculator computes formation energies with optional corrections
from fairchem.core import FormationEnergyCalculator

predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")
calc = FormationEnergyCalculator(
    predictor,
    task_name="omat"
)

atoms.calc = calc
formation_energy = atoms.get_potential_energy()

Warning: UMA models use DFT/DFT+U total energy labels that are NOT compatible with Materials Project MP2020 corrections. Do not use MP2020 corrections with OMat24-trained models.

Model Loading Options

Load Pretrained UMA Model

from fairchem.core import pretrained_mlip

# Standard loading
predictor = pretrained_mlip.get_predict_unit("uma-m-1p1")

# With turbo mode (faster, slight accuracy trade-off)
predictor = pretrained_mlip.get_predict_unit(
    "uma-m-1p1",
    inference_settings="turbo"
)

# UMA-S specific turbo (maximum speed with triton kernels)
predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p2",
    inference_settings="turbo_umas"
)

# Specify device
predictor = pretrained_mlip.get_predict_unit(
    "uma-m-1p1",
    device="cuda:0"
)

# Load local checkpoint
predictor = pretrained_mlip.get_predict_unit(
    "/path/to/checkpoint.pt",
    device="cuda"
)

Available Models

• uma-s-1p2: Small, fastest, improved accuracy (6.6M/290M params) — recommended small model
• uma-s-1p1: Small, fast (6.6M/150M params)
• uma-m-1p1: Medium, highest accuracy (50M/1.4B params)

Task Selection Guide

Performance Optimization

Multi-GPU Inference

# Use multiple GPUs automatically (requires fairchem-core[extras])
calc = FAIRChemCalculator(
    predictor,
    task_name="omat",
    workers=8  # Use 8 GPUs
)

# Achieves ~10× speedup on 8× H100 GPUs
# ~1 ns/day with 100k+ atom systems

Turbo Mode

# Standard turbo
predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p2",
    inference_settings="turbo"
)

# UMA-S specific maximum speed (triton kernels)
predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p2",
    inference_settings="turbo_umas"
)

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

Higher Precision

import torch

# FP64 for higher precision calculations
predictor = pretrained_mlip.get_predict_unit(
    "uma-m-1p1",
    base_precision_dtype=torch.float64
)

Hessian Calculation

# Enable gradient-based Hessian computation
predictor = pretrained_mlip.get_predict_unit(
    "uma-m-1p1",
    predict_untrained_hessian=True
)

Batch Predictions

# For multiple similar calculations, iterate over structures
results = []
for atoms in [atoms1, atoms2, atoms3]:
    atoms.calc = calc
    results.append({
        'energy': atoms.get_potential_energy(),
        'forces': atoms.get_forces()
    })

Best Practices

1. Task Selection

Always choose the appropriate task for your system:

• Surfaces with adsorbates → oc20
• Oxide catalysis → oc22 (uma-s-1p2 only)
• Electrocatalysis → oc25 (uma-s-1p2 only)
• Bulk materials → omat
• Isolated molecules → omol
• MOFs → odac
• Molecular crystals → omc

2. Model Selection

• Initial screening: Use uma-s-1p2 + turbo_umas
• Production calculations: Use uma-m-1p1
• Very large systems: Use uma-s-1p2 + workers
• New tasks (oc22, oc25): Must use uma-s-1p2

3. Spin and Charge for Molecules

When using omol task, set spin and charge via atoms.info:

atoms.info.update({"spin": 1, "charge": 0})  # defaults

4. 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)

5. Energy Label Compatibility

UMA models trained on OMat24 use DFT/DFT+U total energy labels. These are NOT compatible with Materials Project MP2020 corrections. Do not mix.

6. Memory Management

For large systems:

# Use latest small model with turbo
predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p2",
    inference_settings="turbo_umas"
)

# Distribute across GPUs
calc = FAIRChemCalculator(
    predictor,
    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)

LAMMPS Integration

For very large-scale MD simulations, FAIRChem supports LAMMPS integration with multi-GPU support. This is recommended for systems with 100k+ atoms.

Troubleshooting

Hugging Face Authentication

# Login to Hugging Face
huggingface-cli login

# Request access to UMA models at:
# https://huggingface.co/facebook/UMA

GPU Memory Issues

# Use smaller model
predictor = pretrained_mlip.get_predict_unit("uma-s-1p2")

# Use turbo mode
predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p2",
    inference_settings="turbo_umas"
)

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

Slow Inference

# Enable turbo mode
inference_settings="turbo_umas"  # For UMA-S models

# Use multiple GPUs
workers=N

# Use latest small model
"uma-s-1p2"

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
• For v1 compatibility: pip install fairchem-core==1.10.0

# 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-1p2 vs m-1p1)
• Suggest performance optimizations (turbo_umas, workers)
• Include validation against known results
• Mention Hugging Face authentication requirement
• Provide complete working examples
• Note v2 compatibility requirements
• Set spin/charge for omol calculations

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 (spin/charge for omol, energy labels for omat)