Pretrained models

2025 recommendation: We now suggest using the UMA model, trained on all of the FAIR chemistry datasets before using one of the checkpoints below. The UMA model has a number of nice features over the previous checkpoints

1. It is state-of-the-art in out-of-domain prediction accuracy

2. It predicts total energies of a system, which are helpful for predicting properties beyond adsorption energies, and removes some ambiguity when your MOF may reconstruct.

3. The UMA small model is an energy conserving and smooth checkpoint, so should work much better for vibrational calculations, molecular dynamics, etc.

4. The UMA model is most likely to be updated in the future.

ODAC23

• All config files for the ODAC23 models are available in the configs/odac directory.

S2EF models

Model Name	Model	Checkpoint	Config
SchNet-S2EF-ODAC	SchNet	checkpoint	config
DimeNet++-S2EF-ODAC	DimeNet++	checkpoint	config
PaiNN-S2EF-ODAC	PaiNN	checkpoint	config
GemNet-OC-S2EF-ODAC	GemNet-OC	checkpoint	config
eSCN-S2EF-ODAC	eSCN	checkpoint	config
EquiformerV2-S2EF-ODAC	EquiformerV2	checkpoint	config
EquiformerV2-Large-S2EF-ODAC	EquiformerV2 (Large)	checkpoint	config

IS2RE Direct models

Model Name	Model	Checkpoint	Config
Gemnet-OC-IS2RE-ODAC	Gemnet-OC	checkpoint	config
eSCN-IS2RE-ODAC	eSCN	checkpoint	config
EquiformerV2-IS2RE-ODAC	EquiformerV2	checkpoint	config

The models in the table above were trained to predict relaxed energy directly. Relaxed energies can also be predicted by running structural relaxations using the S2EF models from the previous section.

IS2RS

The IS2RS is solved by running structural relaxations using the S2EF models from the prior section.

The Open DAC 2023 (ODAC23) dataset is licensed under a Creative Commons Attribution 4.0 License.

Please consider citing the following paper in any research manuscript using the ODAC23 dataset:

@article{odac23_dataset,
    author = {Anuroop Sriram and Sihoon Choi and Xiaohan Yu and Logan M. Brabson and Abhishek Das and Zachary Ulissi and Matt Uyttendaele and Andrew J. Medford and David S. Sholl},
    title = {The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture},
    year = {2023},
    journal={arXiv preprint arXiv:2311.00341},
}