fairchem

Use AI to model and discover new catalysts for use in renewable energy storage to help in addressing climate change.
https://github.com/facebookresearch/fairchem

Category: Renewable Energy
Sub Category: Hydro Energy

Keywords from Contributors

archiving measur autograd transforms optimize animals compose projection observation conversion

Last synced: about 8 hours ago
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Repository metadata

FAIR Chemistry's library of machine learning methods for chemistry

• Host: GitHub
• URL: https://github.com/facebookresearch/fairchem
• Owner: facebookresearch
• License: other
• Created: 2019-09-26T04:47:27.000Z (about 6 years ago)
• Default Branch: main
• Last Pushed: 2025-10-28T00:41:48.000Z (6 days ago)
• Last Synced: 2025-10-28T16:38:43.218Z (5 days ago)
• Language: Python
• Homepage: https://fair-chem.github.io/
• Size: 59.4 MB
• Stars: 1,799
• Watchers: 37
• Forks: 400
• Open Issues: 66
• Releases: 53
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

README.md

pip install fairchem-core

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

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

huggingface-cli login

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)

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

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)

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()

pip install fairchem-core[extras]

from ase import units
from ase.md.langevin import Langevin
from fairchem.core import pretrained_mlip, FAIRChemCalculator
import time

from fairchem.core.datasets.common_structures import get_fcc_carbon_xtal

predictor = pretrained_mlip.get_predict_unit(
    "uma-s-1p1", inference_settings="turbo", device="cuda", workers=8
)
calc = FAIRChemCalculator(predictor, task_name="omat")

atoms = get_fcc_carbon_xtal(8000)
atoms.calc = calc

dyn = Langevin(
    atoms,
    timestep=0.1 * units.fs,
    temperature_K=400,
    friction=0.001 / units.fs,
)
# warmup 10 steps
dyn.run(steps=10)
start_time = time.time()
dyn.attach(
    lambda: print(
        f"Step: {dyn.get_number_of_steps()}, E: {atoms.get_potential_energy():.3f} eV, "
        f"QPS: {dyn.get_number_of_steps()/(time.time()-start_time):.2f}"
    ),
    interval=1,
)
dyn.run(steps=1000)

Citation (CITATION.cff)

cff-version: 1.2.0
title: "FAIRChem"
version: 2.2.0
message: "If you use this software, please cite it as below."
doi: 10.5281/zenodo.15587498
url: "https://github.com/facebookresearch/fairchem"
license:
- MIT
authors:
- affiliation: FAIR, Meta
  family-names: Muhammed Shuaibi
- affiliation: FAIR, Meta AI
  family-names: Abhishek Das
- affiliation: FAIR, Meta
  family-names: Anuroop Sriram
- family-names: Misko
- affiliation: FAIR, Meta AI
  family-names: Luis Barroso-Luque
- family-names: Ray Gao
- family-names: Siddharth Goyal
- affiliation: FAIR, Meta AI
  family-names: Zachary Ulissi
- affiliation: FAIR, Meta
  family-names: Brandon Wood
- affiliation: Microsoft
  family-names: Tian Xie
- affiliation: Carnegie Mellon University
  family-names: Junwoong Yoon
- family-names: Brook Wander
- affiliation: Radical AI
  family-names: Adeesh Kolluru
- family-names: Richard Barnes
- affiliation: Carnegie Mellon University
  family-names: Ethan Sunshine
- affiliation: Toyota Research Institute
  family-names: Kevin Tran
- family-names: Xiang
- affiliation: FAIR, Meta AI
  family-names: Daniel Levine
- family-names: Nima Shoghi
- family-names: Ilias Chair
- affiliation: FAIR, Meta AI
- family-names: Janice Lan
- affiliation: Georgia Institute of Technology
  family-names: Kaylee Tian
- affiliation: Carnegie Mellon University
  family-names: Joseph Musielewicz
- family-names: clz55
- family-names: Weihua Hu
- affiliation: FAIR, Meta AI
- family-names: Kyle Michel
- family-names: willis
- family-names: vbttchr
message: If you use this software, please cite it using the metadata from this file.
repository-code: https://github.com/facebookresearch/fairchem
type: software

Owner metadata

• Name: Meta Research
• Login: facebookresearch
• Email:
• Kind: organization
• Description:
• Website: https://opensource.fb.com
• Location: Menlo Park, California
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/16943930?v=4
• Repositories: 1060
• Last ynced at: 2024-12-17T01:36:52.238Z
• Profile URL: https://github.com/facebookresearch

GitHub Events

Total

• Fork event: 86
• Discussion event: 1
• Create event: 242
• Release event: 20
• Issues event: 210
• Watch event: 558
• Delete event: 209
• Member event: 1
• Issue comment event: 379
• Push event: 835
• Pull request review event: 329
• Pull request event: 352
• Pull request review comment event: 189

Last Year

• Fork event: 86
• Discussion event: 1
• Create event: 242
• Release event: 20
• Issues event: 210
• Watch event: 558
• Delete event: 209
• Member event: 1
• Issue comment event: 379
• Push event: 835
• Pull request review event: 329
• Pull request event: 352
• Pull request review comment event: 189

Committers metadata

Last synced: 2 days ago

Committer domains:

• fb.com: 4
• meta.com: 4
• mailbox.org: 2
• berkeley.edu: 2
• hitachi.com: 1
• umich.edu: 1
• gatech.edu: 1
• qq.com: 1
• devfair0331.h2.fair: 1
• mail.mcgill.ca: 1
• andrew.cmu.edu: 1
• umn.edu: 1

Issue and Pull Request metadata

Last synced: 1 day ago

Package metadata

• Total packages: 4
• Total downloads:
  • pypi: 24,178 last-month
• Total dependent packages: 0 (may contain duplicates)
• Total dependent repositories: 0 (may contain duplicates)
• Total versions: 41
• Total maintainers: 1

Dependencies

Score: 21.718600661120625