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Energy","sub_category":"Hydro Energy","monthly_downloads":38717,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"[//]: # (\u003ch1 align=\"center\"\u003e)\n\n[//]: # ()\n[//]: # (\u003cp align=\"center\"\u003e)\n\n[//]: # (  \u003cimg width=\"559\" height=\"200\" src=\"https://github.com/user-attachments/assets/25cd752c-3c56-469d-8524-4e493646f6b2\"?)\n\n[//]: # (\u003c/p\u003e)\n\n[//]: # ()\n[//]: # (\u003c/h1\u003e)\n\n\u003ch4 align=\"center\"\u003e\n\n![tests](https://github.com/facebookresearch/fairchem/actions/workflows/test.yml/badge.svg?branch=main\u0026event=push)\n![PyPI - Version](https://img.shields.io/pypi/v/fairchem-core)\n![Static Badge](https://img.shields.io/badge/python-3.10%2B-blue)\n[![codecov](https://codecov.io/gh/facebookresearch/fairchem/graph/badge.svg)](https://codecov.io/gh/facebookresearch/fairchem)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15587498.svg)](https://doi.org/10.5281/zenodo.15587498)\n\n[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/facebookresearch/fairchem?quickstart=1)\n\n\u003c/h4\u003e\n\n# `fairchem` by the FAIR Chemistry team\n\n`fairchem` is the [FAIR](https://ai.meta.com/research/) Chemistry's centralized repository of all its data, models,\ndemos, and application efforts for materials science and quantum chemistry.\n\n\u003e :warning: **FAIRChem version 2 is a breaking change from version 1 and is not compatible with our previous pretrained models and code.**\n\u003e If you want to use an older model or code from version 1 you will need to install [version 1](https://pypi.org/project/fairchem-core/1.10.0/),\n\u003e as detailed [here](#looking-for-fairchem-v1-models-and-code).\n\n\u003e [!CAUTION]\n\u003e UMA models and legacy inorganic bulk models trained using OMat24 are trained with DFT and DFT+U total energy labels.\n\u003e These are not compatible with Materials Project calculations. If you are using UMA or models trained on OMat24 only\n\u003e for such calculations, you can find a OMat24 specific calculations of reference unary compounds and MP2020-style\n\u003e anion and GGA/GGA+U mixing corrections in the [OMat24 Hugging Face repo](https://huggingface.co/datasets/facebook/OMAT24).\n\u003e Do not use MP2020 corrections or use the MP references compounds when using OMat24 trained models. Additional care\n\u003e must be taken when computing energy differences, such as formation and energy above hull and comparing with calculations\n\u003e in the Materials Project since DFT pseudopotentials are different and magnetic ground states may differ as well.\n\n\n## Latest news\nMarch 2026 - UMA-1.2 released! ~50% faster, ~40% more accurate on Open Molecules test set, and expanded data coverage for catalysts (oxides and interfaces), molecules, and polymers!\n\nOct 2025 - [check out our seamless Multi-node, Multi-GPU and LAMMPs interfaces to run large scale dynamics!](#multi-gpu-inference-and-lammps)\n\n## Read our latest release post!\nRead about the [UMA model and OMol25 dataset](https://ai.meta.com/blog/meta-fair-science-new-open-source-releases/) release.\n\n[![Meta FAIR Science Release](https://github.com/user-attachments/assets/acddd09b-ed6f-4d05-9a4b-9ba5e2301150)](https://ai.meta.com/blog/meta-fair-science-new-open-source-releases/?ref=shareable)\n\n## Try the demo!\nIf you want to explore model capabilities check out our\n[educational demo](https://facebook-fairchem-uma-demo.hf.space/)\n\n[![Educational Demo](https://github.com/user-attachments/assets/7005d1bb-4459-403d-b299-d41fdd8c48ec)](https://facebook-fairchem-uma-demo.hf.space/)\n\n## Installation\nAlthough not required, we highly recommend installing using a package manager and virtualenv such as [uv](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer), it is much faster and better at resolving dependencies than standalone pip.\n\nInstall fairchem-core using pip\n```bash\npip install fairchem-core\n```\n\nIf you want to contribute or make modifications to the code, clone the repo and install in edit mode\n```bash\ngit clone git@github.com:facebookresearch/fairchem.git\n\npip install -e fairchem/packages/fairchem-core[dev]\n```\n\n## Quick Start\nThe easiest way to use pretrained models is via the [ASE](https://wiki.fysik.dtu.dk/ase/) `FAIRChemCalculator`.\nA single uma model can be used for a wide range of applications in chemistry and materials science by picking the\nappropriate task name for domain specific prediction.\n\n### Instantiate a calculator from a pretrained model\nMake sure you have a Hugging Face account, have already applied for model access to the\n[UMA model repository](https://huggingface.co/facebook/UMA), and have logged in to Hugging Face using an access token.\nYou can use the following to save an auth token,\n```bash\nhuggingface-cli login\n```\n\nModels are referenced by their name, below are the currently supported models:\n\n| Model Name | Description |\n|---|---|\n| uma-s-1p2 | Latest version of the UMA small model, fastest of the UMA models while still SOTA on most benchmarks (6.6M/290M active/total params) |\n| uma-s-1p1 | Early version of the UMA small model while still SOTA on most benchmarks (6.6M/150M active/total params) |\n| 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) |\n\n### Set the task for your application and calculate\n\n- **oc20:** use this for catalysis\n- **oc22:** use this for oxide catalysis (1p2 only)\n- **oc25:** use this for (electro)catalysis (1p2 only)\n- **omat:** use this for inorganic materials\n- **omol:** use this for molecules+polymers\n- **odac:** use this for MOFs\n- **omc:** use this for molecular crystals\n\n#### Relax an adsorbate on a catalytic surface,\n```python\nfrom ase.build import fcc100, add_adsorbate, molecule\nfrom ase.optimize import LBFGS\nfrom fairchem.core import pretrained_mlip, FAIRChemCalculator\n\npredictor = pretrained_mlip.get_predict_unit(\"uma-s-1p2\", device=\"cuda\")\ncalc = FAIRChemCalculator(predictor, task_name=\"oc20\")\n\n# Set up your system as an ASE atoms object\nslab = fcc100(\"Cu\", (3, 3, 3), vacuum=8, periodic=True)\nadsorbate = molecule(\"CO\")\nadd_adsorbate(slab, adsorbate, 2.0, \"bridge\")\n\nslab.calc = calc\n\n# Set up LBFGS dynamics object\nopt = LBFGS(slab)\nopt.run(0.05, 100)\n```\n\n#### Relax an inorganic crystal,\n```python\nfrom ase.build import bulk\nfrom ase.optimize import FIRE\nfrom ase.filters import FrechetCellFilter\nfrom fairchem.core import pretrained_mlip, FAIRChemCalculator\n\npredictor = pretrained_mlip.get_predict_unit(\"uma-s-1p2\", device=\"cuda\")\ncalc = FAIRChemCalculator(predictor, task_name=\"omat\")\n\natoms = bulk(\"Fe\")\natoms.calc = calc\n\nopt = FIRE(FrechetCellFilter(atoms))\nopt.run(0.05, 100)\n```\n\n#### Run Molecular Dynamics (MD)\nNote: `pretrained_mlip.get_predict_unit()` currently uses a seed to set the global state of the numpy RNG. In order to obtain different trajectories for different runs of the following code, we have to set a random seed as shown below:\n```python\nfrom ase import units\nfrom ase.io import Trajectory\nfrom ase.md.langevin import Langevin\nfrom ase.build import molecule\nfrom fairchem.core import pretrained_mlip, FAIRChemCalculator\n\nseed = np.random.randint(0, np.iinfo(np.int32).max, dtype=int)\npredictor = pretrained_mlip.get_predict_unit(\"uma-s-1p2\", device=\"cuda\", seed=seed)\ncalc = FAIRChemCalculator(predictor, task_name=\"omol\")\n\natoms = molecule(\"H2O\")\natoms.calc = calc\n\ndyn = Langevin(\n    atoms,\n    timestep=0.1 * units.fs,\n    temperature_K=400,\n    friction=0.001 / units.fs,\n)\ntrajectory = Trajectory(\"my_md.traj\", \"w\", atoms)\ndyn.attach(trajectory.write, interval=1)\ndyn.run(steps=1000)\n```\n\n#### Calculate a spin gap,\n```python\nfrom ase.build import molecule\nfrom fairchem.core import pretrained_mlip, FAIRChemCalculator\n\npredictor = pretrained_mlip.get_predict_unit(\"uma-s-1p2\", device=\"cuda\")\n\n#  singlet CH2\nsinglet = molecule(\"CH2_s1A1d\")\nsinglet.info.update({\"spin\": 1, \"charge\": 0})\nsinglet.calc = FAIRChemCalculator(predictor, task_name=\"omol\")\n\n#  triplet CH2\ntriplet = molecule(\"CH2_s3B1d\")\ntriplet.info.update({\"spin\": 3, \"charge\": 0})\ntriplet.calc = FAIRChemCalculator(predictor, task_name=\"omol\")\n\ntriplet.get_potential_energy() - singlet.get_potential_energy()\n```\n\n#### Multi-GPU Inference and LAMMPs\nIf you have multiple gpus (or multiple nodes), we handle all the parallelism for you under the hood by a single flag (workers=N). For example, you can run the following 8000 atom md simulation with ~10 qps (8x H100 GPU), ~10x faster than single-gpu inference! Current benchmarks show we can run uma-s @ ~1 ns/per day with 100k+ atoms systems in real MD scenarios (more on this to come!). This is also compatible with LAMMPs to perform large scale MD. See our [docs](https://fair-chem.github.io/core/common_tasks/summary.html) for more details. This requires the Ray package to be installed and comes with the extras bundle.\n```\npip install fairchem-core[extras]\n```\n\n```python\nfrom ase import units\nfrom ase.md.langevin import Langevin\nfrom fairchem.core import pretrained_mlip, FAIRChemCalculator\nimport time\n\nfrom fairchem.core.datasets.common_structures import get_fcc_crystal_by_num_atoms\n\nseed = np.random.randint(0, np.iinfo(np.int32).max, dtype=int)\npredictor = pretrained_mlip.get_predict_unit(\n    \"uma-s-1p2\", inference_settings=\"turbo\", device=\"cuda\", workers=8, seed=seed,\n)\ncalc = FAIRChemCalculator(predictor, task_name=\"omat\")\n\natoms = get_fcc_crystal_by_num_atoms(8000)\natoms.calc = calc\n\ndyn = Langevin(\n    atoms,\n    timestep=0.1 * units.fs,\n    temperature_K=400,\n    friction=0.001 / units.fs,\n)\n# warmup 10 steps\ndyn.run(steps=10)\nstart_time = time.time()\ndyn.attach(\n    lambda: print(\n        f\"Step: {dyn.get_number_of_steps()}, E: {atoms.get_potential_energy():.3f} eV, \"\n        f\"QPS: {dyn.get_number_of_steps()/(time.time()-start_time):.2f}\"\n    ),\n    interval=1,\n)\ndyn.run(steps=1000)\n```\n\n### LICENSE\n`fairchem` is available under a [MIT License](LICENSE.md). 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