fairchem-data-odac 0.1.0

Code for ODAC MOF configurations and VASP input sets for direct air capture

ODAC23 Dataset

To download the ODAC23 dataset, please see the links here.

Pre-trained ML models and configs are available here.

Large ODAC files can be downloaded by running the command python src/fairchem/core/scripts/download_large_files.py odac from the root of the fairchem repo.

This repository contains the list of promising MOFs discovered in the ODAC23 paper, as well as details of the classifical force field calculations.

Information about supercells can be found in supercell_info.csv for each example (this file is downloaded to the local repo only when the above script is run).

Citing

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},
}