base on FAIR Chemistry's library of machine learning methods for chemistry <p align="center"> <img width="559" height="200" src="https://github.com/user-attachments/assets/25cd752c-3c56-469d-8524-4e493646f6b2"? </p> <h4 align="center">    [](https://doi.org/10.5281/zenodo.15587498) [](https://github.com/codespaces/new/facebookresearch/fairchem?quickstart=1) </h4> # `fairchem` by the FAIR Chemistry team </h1> `fairchem` is the [FAIR](https://ai.meta.com/research/) 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](https://pypi.org/project/fairchem-core/1.10.0/), > as detailed [here](#looking-for-fairchem-v1-models-and-code). > :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 OMol25 dataset](https://ai.meta.com/blog/meta-fair-science-new-open-source-releases/) release. [](https://ai.meta.com/blog/meta-fair-science-new-open-source-releases/?ref=shareable) ## Try the demo! If you want to explore model capabilities check out our [educational demo](https://facebook-fairchem-uma-demo.hf.space/) [](https://facebook-fairchem-uma-demo.hf.space/) ## Installation Although 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. Install fairchem-core using pip ```bash pip install fairchem-core ``` ## Quick Start The easiest way to use pretrained models is via the [ASE](https://wiki.fysik.dtu.dk/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](https://huggingface.co/facebook/UMA), and have logged in to Hugging Face using an access token. You can use the following to save an auth token, ```bash 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, ```python 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, ```python 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, ```python 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, ```python 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() ``` ### LICENSE `fairchem` is available under a [MIT License](LICENSE.md). Models/checkpoint licenses vary by application area. ", Assign "at most 3 tags" to the expected json: {"id":"13779","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"