mlsanityData to 7 Oct 2026

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

arXiv 2311.00341 ↗First posted 1 Nov 2023cond-mat.mtrl-sci cs.LGcited by 18

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Abstract (arXiv)

New methods for carbon dioxide removal are urgently needed to combat global climate change. Direct air capture (DAC) is an emerging technology to capture carbon dioxide directly from ambient air. Metal-organic frameworks (MOFs) have been widely studied as potentially customizable adsorbents for DAC. However, discovering promising MOF sorbents for DAC is challenging because of the vast chemical space to explore and the need to understand materials as functions of humidity and temperature. We explore a computational approach benefiting from recent innovations in machine learning (ML) and present a dataset named Open DAC 2023 (ODAC23) consisting of more than 38M density functional theory (DFT) calculations on more than 8,400 MOF materials containing adsorbed CO2 and/or H2O. ODAC23 is by far the largest dataset of MOF adsorption calculations at the DFT level of accuracy currently available. In addition to probing properties of adsorbed molecules, the dataset is a rich source of information on structural relaxation of MOFs, which will be useful in many contexts beyond specific applications for DAC. A large number of MOFs with promising properties for DAC are identified directly in ODAC23. We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level. This open-source dataset and our initial ML models will provide an important baseline for future efforts to identify MOFs for a wide range of applications, including DAC.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 20 of its 153 references that are in the map. First posted on arXiv 1 Nov 2023.

Builds on

20 of 153 references.

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Citing papers per year: 2023: 2; 2024: 3; 2025: 10; 2026: 3.

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  1. 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributionscited by 52
  2. 2024Open Materials 2024 (OMat24) Inorganic Materials Dataset and Modelscited by 50
  3. 2023Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materialscited by 12
  4. 2025PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Designcited by 11
  5. 2025MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modelingcited by 7
  6. 2025The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capturecited by 7
  7. 2026EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformerscited by 7
  8. 2025Lessons from the trenches on evaluating machine-learning systems in materials sciencecited by 4
  9. 2025OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Predictioncited by 4
  10. 2025Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spacescited by 2
  11. 2024Adapting OC20-trained EquiformerV2 Models for High-Entropy Materialscited by 1
  12. 2025Towards Faster and More Compact Foundation Models for Molecular Property Predictioncited by 1
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  4. 2025Comparing the latent features of universal machine-learning interatomic potentialscited by 0
  5. 2026Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learningcited by 0
  6. 2026Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Datacited by 0