The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908556328960000 |
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| author | Sriram, Anuroop Brabson, Logan M. Yu, Xiaohan Choi, Sihoon Abdelmaqsoud, Kareem Moubarak, Elias de Haan, Pim Löwe, Sindy Brehmer, Johann Kitchin, John R. Welling, Max Zitnick, C. Lawrence Ulissi, Zachary Medford, Andrew J. Sholl, David S. |
| author_facet | Sriram, Anuroop Brabson, Logan M. Yu, Xiaohan Choi, Sihoon Abdelmaqsoud, Kareem Moubarak, Elias de Haan, Pim Löwe, Sindy Brehmer, Johann Kitchin, John R. Welling, Max Zitnick, C. Lawrence Ulissi, Zachary Medford, Andrew J. Sholl, David S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03162 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture Sriram, Anuroop Brabson, Logan M. Yu, Xiaohan Choi, Sihoon Abdelmaqsoud, Kareem Moubarak, Elias de Haan, Pim Löwe, Sindy Brehmer, Johann Kitchin, John R. Welling, Max Zitnick, C. Lawrence Ulissi, Zachary Medford, Andrew J. Sholl, David S. Materials Science Machine Learning Chemical Physics 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. |
| title | The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture |
| topic | Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2508.03162 |