RESEARCH
ML APPLICATIONS
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
August 04, 2025
Abstract
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 70 million DFT single-point calculations for CO2, H2O, N2, and O2 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.
AUTHORS
Written by
Logan M. Brabson
Xiaohan Yu
Sihoon Choi
Kareem Abdelmaqsoud
Elias Moubarak
Pim de Haan
Sindy Löwe
Johann Brehmer
John R. Kitchin
Max Welling
Andrew J. Medford
David S. Sholl
Anuroop Sriram
C. Lawrence Zitnick
Zachary Ulissi
Publisher
arXiv
Research Topics
Core Machine Learning
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