fairchem-core

fairchem by FAIR Chemistry

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

:warning: 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.

:warning: 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 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

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

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-1", 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-1", 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-1", 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-1", 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()

Looking for Fairchem V1, models and code?

Fairchem V2 is a major upgrade and we completely rewrote the trainer, fine-tuning, models and calculators.

We plan to bring back the following models compatible with Fairchem V2 soon:

• Gemnet-OC
• EquiformerV2
• ESEN

We will also be releasing more detailed documentation on how to use Fairchem V2, stay tuned!

The old OCPCalculator, trainer code will NOT be revived. We apologize for the inconvenience and please raise Issues if you need help! In the meantime, you can still use models from fairchem version 1, by installing version 1,

pip install fairchem-core==1.10

And using the OCPCalculator

from fairchem.core import OCPCalculator

calc = OCPCalculator(
    model_name="EquiformerV2-31M-S2EF-OC20-All+MD",
    local_cache="pretrained_models",
    cpu=False,
)

LICENSE

fairchem is available under a MIT License. MIT License

Copyright (c) Meta Platforms, Inc. and affiliates.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.