FAIR Chemistry Machine Learning ToolkitNew

What it is

FAIR Chemistry is the successor of the Open Catalyst Project: a complete stack - datasets (OC20, OC22, ODCD), models (GemNet, GemNet-OC, eSCN, EquiformerV2, SpinConv), and training/evaluation code for ML on atomic systems.

Why start here

• Largest public catalysis relaxations/dynamics datasets in the field
• Reference model implementations with published baselines
• Clear task definitions: energy/force prediction, relaxation, dynamics, adsorption

Learning path

1. Install fairchem-core; load an OC20 split; inspect a structure record
2. Train a small GemNet-OC on a subset; reproduce an IS2RE baseline
3. Run relaxation and compare final energies against DFT references
4. Read the OCP/FAIR Chem papers to understand benchmark design

Ecosystem fit

Pairs with pymatgen for analysis and with ASE for structure manipulation; outputs feed into catalyst screening workflows.