openmaterials.ai / codes / fairchem

fairchem: machine-learned potentials for catalysis and materials

The FAIR Chemistry library of graph neural network potentials and the Open Catalyst datasets they learn from, OC20 for adsorbates on stable surfaces and OC22 for oxides.

What it is and how it works

Adsorbates on surfaces at scale

OC20 holds 1,281,040 DFT relaxations of 82 adsorbates on surfaces from 55 elements, about 264,890,000 single points. Models trained on it predict energies and forces for unseen adsorbates and catalysts (Chanussot 2021).

Three tasks

S2EF maps a structure to energy and forces, IS2RS predicts the relaxed structure from an initial one, IS2RE predicts the relaxed adsorption energy (Chanussot 2021).

Graph neural network models

CGCNN, SchNet and DimeNet++ with a 6 A cutoff and up to 50 neighbours (Chanussot 2021). Forces come from the energy gradient or directly, as in ForceNet and GemNet.

OC22 oxide electrocatalysts

62,331 relaxations of oxide slabs and adsorbate-covered slabs with PBE+U and spin polarization, all atoms free, and the DFT total energy as target (Tran 2023).

Transfer between datasets

Pretraining GemNet-OC on OC20 and fine-tuning on OC22 lowers the in-domain total energy MAE from 0.374 eV to 0.239 eV (Tran 2023).

Code and checkpoints

The ocp repository publishes baseline models, data loaders and training scripts, and its MODELS.md lists pretrained OC20 checkpoints (Chanussot 2021).

What people do with it

Lowest-energy adsorbate placements

OC20-trained potentials plus one DFT single point per pick matched dense DFT within 0.1 eV, or went lower, for 87.36% of OC20-Dense systems at a 2290x speedup. Lan 2023

OER scaling on oxide surfaces

In the OC22 paper, GemNet-OC gives the OOH against OH Gibbs scaling with slope 0.73 and intercept 3.44 eV, against the literature 0.73 and 3.49 eV. Tran 2023

Adsorption energies on rutile oxides

In the OC22 paper, predicted O, H and OH adsorption energies on rutile surfaces correlate with BEEF-vdW literature values at 75% or better, most within 0.6 eV. Tran 2023

How it is used

  1. Pick a pretrained model

    Choose a checkpoint trained on OC20, OC22 or both, matched to metals or oxides.

  2. Build slab and adsorbate

    Cut a surface from a bulk cell, add vacuum and place the adsorbate on a site.

  3. Predict energy and forces

    The calculator returns the energy and per-atom forces of the structure in one pass.

  4. Relax or screen

    Iterate forces to a relaxed geometry, or rank many adsorbate placements by energy.

  5. Confirm with DFT

    A single DFT point on the relaxed structure checks the model prediction.