facebookresearch/fairchem

`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.

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

Install fairchem-core using pip,

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pip install git+https://github.com/facebookresearch/fairchem.git@fairchem_core-2.0.0#subdirectory=packages/fairchem-core

The PyPI install (pip install fairchem-core) is not available right now as we are waiting for a few dependencies to release their PyPI packages, will update this soon when it’s available!

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.

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 adsorbate on a catalytic surface,

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

calc = FAIRChemCalculator(hf_hub_filename="uma_sm.pt", device="cuda", 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)

Or relax an inorganic crystal,

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

calc = FAIRChemCalculator(hf_hub_filename="uma_sm.pt", device="cuda", task_name="omat")

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

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

Run molecular MD,

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

calc = FAIRChemCalculator(hf_hub_filename="uma_sm.pt", device="cuda", 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)

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
EquiformersV2
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,

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pip install fairchem-core==1.10

And using the OCPCalculator

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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.