fairchem

What problem does it solve?

FAIRChem provides expert guidance for using Meta's FAIRChem library to run machine-learning-driven chemistry calculations across catalysis, materials, molecules, MOFs, and molecular crystals, delivering 100-1000× speedups over traditional DFT.

Core Features & Use Cases

• UMA models: Use uma-s-1p1 (small) or uma-m-1p1 (medium) for universal predictions.
• Domain versatility: Apply the same model across catalysis, materials, molecules, MOFs, and crystals.
• ASE integration: Drop-in ASE calculator for energy, forces, and MD workflows.
• Multi-GPU scaling: Distribute inference across GPUs for large systems.

Quick Start

Load UMA model (e.g., uma-m-1p1) and create FAIRChemCalculator for domain omat, then run an ASE LBFGS optimization on a bulk lattice.