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FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations, molecular dynamics, spin-state energetics, and surface catalysis workflows with the same pretrained network by switching a task flag. Tasks span heterogeneous domains—catalysis (OC20-style), inorganic materials (OMat), molecules (OMol), MOFs (ODAC), and molecular crystals (OMC)—allowing one model family to serve many simulations. The README provides quick paths for pulling models (e.g., via Hugging Face access), then running energy/force predictions on GPU or CPU.
Features
- Single library spanning materials science and quantum chemistry tasks
- Pretrained UMA models with an ASE-compatible FAIRChemCalculator
- Simple task switch (oc20, omat, omol, odac, omc) for domain-specific predictions
- V2 core with streamlined installation and reduced third-party dependencies
- Examples for relaxations, MD, and spin-gap calculations on CPU or GPU
- Actively released package with docs, demos, and model registry integration
Project Samples
