facebookresearch/fairchem

Health v2 · maintenance only

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2231 stars · 499 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

FAIR Chemistry's centralized Python library of machine learning models, datasets, and applications for materials science and quantum chemistry. Its flagship UMA model is a universal machine-learning interatomic potential trained on 500M+ DFT calculations for molecules, materials, and catalysts.

Use cases

• predict adsorption energies for heterogeneous catalysis
• run molecular dynamics with a universal interatomic potential
• compute phonons and elastic properties of bulk materials
• generate molecular conformers and predict electronic properties
• simulate molecular crystals and MOFs for CO2 capture
• fine-tune pretrained atomistic models on custom datasets
• run large-scale GPU-accelerated simulations via LAMMPS

When to choose

• you need state-of-the-art ML potentials for atomistic simulations across molecules, materials, and catalysts
• you want to fine-tune or train graph neural network potentials on your own DFT data
• you need fast, energy-conserving inference as an alternative to expensive DFT calculations

When to avoid

• you need exact DFT-level accuracy or Materials Project-compatible energetics without careful corrections
• you depend on FAIRChem v1 pretrained models, since v2 is a breaking change
• your work is unrelated to atomistic/chemical simulation

Facets

library · maturity active

machine-learning deep-learning simulation sdk chemistry machine-learning python interatomic-potentials uma molecular-dynamics catalysis materials-science dft atomistic-simulation lammps quantum-chemistry computational-chemistry linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/fairchem")