The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
This work addresses the problem of sorbent discovery for direct air capture, which is crucial for climate change mitigation, by providing an expanded and improved dataset for researchers in materials science and machine learning.
The authors tackled the challenge of identifying sorbent materials for direct air capture from humid air by presenting the Open DAC 2025 dataset, which includes nearly 60 million DFT calculations for CO2, H2O, N2, and O2 adsorption in 15,000 MOFs, along with new state-of-the-art machine-learned interatomic potentials that improve predictions.
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and improvement upon ODAC23 (Sriram et al., ACS Central Science, 10 (2024) 923), comprising nearly 60 million DFT single-point calculations for CO$_2$, H$_2$O, N$_2$, and O$_2$ adsorption in 15,000 MOFs. ODAC25 introduces chemical and configurational diversity through functionalized MOFs, high-energy GCMC-derived placements, and synthetically generated frameworks. ODAC25 also significantly improves upon the accuracy of DFT calculations and the treatment of flexible MOFs in ODAC23. Along with the dataset, we release new state-of-the-art machine-learned interatomic potentials trained on ODAC25 and evaluate them on adsorption energy and Henry's law coefficient predictions.