fairchem-core

What it is and what it does

Fairchem-core is FAIR Chemistry's centralized repository of machine learning models for materials science and quantum chemistry. It provides pretrained models (UMA series) that can predict molecular and materials properties across multiple domains: catalysis, oxides, molecules, polymers, inorganic crystals, MOFs, and molecular crystals. The package integrates with ASE (Atomic Simulation Environment) via the FAIRChemCalculator, allowing users to perform structure relaxations, molecular dynamics simulations, and property calculations by selecting a task name appropriate to their domain.

The package depends on a large scientific stack including torch, ray, scipy, and specialized libraries like e3nn and hydra-core. It supports multi-GPU and multi-node inference for large-scale simulations. Version 2 is a breaking change from version 1 and incompatible with older pretrained models. The latest UMA-1.2 release (March 2026) claims approximately 50% faster inference and expanded data coverage for catalysts, molecules, and polymers.

Use it for:

• Relax adsorbate-surface systems for heterogeneous catalysis research without running expensive DFT calculations.
• Perform molecular dynamics simulations of molecules and polymers at scale using GPU acceleration.
• Predict formation energies and structural stability of inorganic crystals for materials discovery workflows.
• Calculate spin gaps and other quantum properties of molecules for computational chemistry studies.
• Run large-scale MD simulations (100k+ atoms) on multi-GPU clusters for industrial materials modeling.
• Estimate properties of metal-organic frameworks and molecular crystals for screening applications.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Provides pretrained machine learning models for predicting molecular and materials properties, integrated with ASE for structure relaxation, molecular dynamics, and quantum chemistry calculations.

Yes, if you work in computational chemistry or materials science and want fast, pretrained ML-based property predictions integrated with ASE. Requires GPU access, a Hugging Face account, and familiarity with the ASE ecosystem. Active maintenance, permissive license, and no known vulnerabilities support adoption. Version 2 is a breaking change—verify compatibility with your existing workflows before upgrading from version 1.

Install

fairchem-core on PyPI

pip install fairchem-core

uv add fairchem-core

poetry add fairchem-core

Installing fairchem-core

Before you install

Low friction installation with a pure Python wheel. Active maintenance with recent releases; last commit 2026-08-14. Requires 22 runtime dependencies including torch, ray, and scientific computing libraries—a substantial but standard stack for ML-driven materials science.

License in practice

MIT License permits commercial use, modification, and redistribution with minimal restrictions. Suitable for both research and production applications.

Quickstart

pip install fairchem-core

from fairchem.core import pretrained_mlip, FAIRChemCalculator
from ase.build import bulk
from ase.optimize import FIRE

predictor = pretrained_mlip.get_predict_unit("uma-s-1p2", device="cuda")
calc = FAIRChemCalculator(predictor, task_name="omat")
atoms = bulk("Fe")
atoms.calc = calc
opt = FIRE(atoms)
opt.run(0.05, 100)

Requires CUDA-capable GPU and Hugging Face account with access to the UMA model repository; login via huggingface-cli login before use.

Verify before relying

• Whether multi-GPU inference (workers=N flag) requires additional configuration beyond Ray installation.
• Compatibility of UMA-1.2 models with existing downstream workflows and whether version 2 breaking changes affect common use patterns.
• Performance characteristics and memory requirements for different model sizes (uma-s vs uma-m) on typical hardware.

Package facts

Evidence: fairchem_core-2.21.0-py3-none-any.whl

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