fairchem by the FAIR Chemistry team

fairchem is the FAIR Chemistry’s centralized repository of all its data, models, demos, and application efforts for materials science and quantum chemistry.

FAIRChem version 2 is a breaking change from version 1 and is not compatible with our previous pretrained models and code. If you want to use an older model or code from version 1 you will need to install version 1, as detailed here.

Some of the docs and new features in FAIRChem version 2 are still being updated so you may see some changes over the next few weeks. Check back here for the latest instructions. Thank you for your patience!

Read our latest release post!

Read about the UMA model and OMol25 dataset release.

Try the demo!

If you want to explore model capabilities check out our educational demo

Installation

Although not required, we highly recommend installing using a package manager and virtualenv such as uv, it is much faster and better at resolving dependencies than standalone pip.

Install fairchem-core using pip

pip install fairchem-core

If you want to contribute or make modifications to the code, clone the repo and install in edit mode

git clone git@github.com:facebookresearch/fairchem.git

pip install -e fairchem/packages/fairchem-core[dev]

Quick Start

The easiest way to use pretrained models is via the ASE FAIRChemCalculator. A single uma model can be used for a wide range of applications in chemistry and materials science by picking the appropriate task name for domain specific prediction.

Instantiate a calculator from a pretrained model

Make sure you have a Hugging Face account, have already applied for model access to the UMA model repository, and have logged in to Hugging Face using an access token. You can use the following to save an auth token,

huggingface-cli login

Models are referenced by their name, below are the currently supported models:

Model Name	Description
uma-s-1p1	Latest version of the UMA small model, fastest of the UMA models while still SOTA on most benchmarks (6.6M/150M active/total params)
uma-m-1p1	Best in class UMA model across all metrics, but slower and more memory intensive than uma-s (50M/1.4B active/total params)

Set the task for your application and calculate

• oc20: use this for catalysis
• omat: use this for inorganic materials
• omol: use this for molecules
• odac: use this for MOFs
• omc: use this for molecular crystals

Relax an adsorbate on a catalytic surface,

from ase.build import fcc100, add_adsorbate, molecule
from ase.optimize import LBFGS
from fairchem.core import pretrained_mlip, FAIRChemCalculator

predictor = pretrained_mlip.get_predict_unit("uma-s-1p1", device="cuda")
calc = FAIRChemCalculator(predictor, task_name="oc20")

# Set up your system as an ASE atoms object
slab = fcc100("Cu", (3, 3, 3), vacuum=8, periodic=True)
adsorbate = molecule("CO")
add_adsorbate(slab, adsorbate, 2.0, "bridge")

slab.calc = calc

# Set up LBFGS dynamics object
opt = LBFGS(slab)
opt.run(0.05, 100)

Relax an inorganic crystal,

from ase.build import bulk
from ase.optimize import FIRE
from ase.filters import FrechetCellFilter
from fairchem.core import pretrained_mlip, FAIRChemCalculator

predictor = pretrained_mlip.get_predict_unit("uma-s-1p1", device="cuda")
calc = FAIRChemCalculator(predictor, task_name="omat")

atoms = bulk("Fe")
atoms.calc = calc

opt = LBFGS(FrechetCellFilter(atoms))
opt.run(0.05, 100)

Run molecular MD,

from ase import units
from ase.io import Trajectory
from ase.md.langevin import Langevin
from ase.build import molecule
from fairchem.core import pretrained_mlip, FAIRChemCalculator

predictor = pretrained_mlip.get_predict_unit("uma-s-1p1", device="cuda")
calc = FAIRChemCalculator(predictor, task_name="omol")

atoms = molecule("H2O")
atoms.calc = calc

dyn = Langevin(
    atoms,
    timestep=0.1 * units.fs,
    temperature_K=400,
    friction=0.001 / units.fs,
)
trajectory = Trajectory("my_md.traj", "w", atoms)
dyn.attach(trajectory.write, interval=1)
dyn.run(steps=1000)

Calculate a spin gap,

from ase.build import molecule
from fairchem.core import pretrained_mlip, FAIRChemCalculator

predictor = pretrained_mlip.get_predict_unit("uma-s-1p1", device="cuda")

#  singlet CH2
singlet = molecule("CH2_s1A1d")
singlet.info.update({"spin": 1, "charge": 0})
singlet.calc = FAIRChemCalculator(predictor, task_name="omol")

#  triplet CH2
triplet = molecule("CH2_s3B1d")
triplet.info.update({"spin": 3, "charge": 0})
triplet.calc = FAIRChemCalculator(predictor, task_name="omol")

triplet.get_potential_energy() - singlet.get_potential_energy()

LICENSE

fairchem is available under a MIT License. Models/checkpoint licenses vary by application area.