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

Abstract

Direct air capture (DAC) of CO₂ with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H₂O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Figure 1

Materials, adsorbates, tasks, and potential applications of the ODAC23 dataset. Images are randomly sampled from the dataset.

Figure 2

Distribution of the number of MOF + adsorbate DFT calculations for the (a) S2EF and (b) IS2RS/IS2RE tasks on a logarithmic scale. The horizontal lines emphasize the size of the dataset.

Figure 3

Parity plots showing DFT-calculated CO₂ and H₂O adsorption energies in (a) pristine and (b) defective MOFs. (c–f) MOF examples with common features of the promising MOFs.

Figure 4

Examples showing different impacts of the defects in MOFs. The defects generated are shown in red squares. Negative impact of defects on DAC (a–d): Defective QOVSOL with a defect concentration of 0.12 shows less favorable CO₂ adsorption (a and c) and stronger H₂O adsorption (b and d). Positive impact of defects on DAC (e–g): The H₂O adsorption is slightly more favorable in defective POLDUQ with a defect concentration of 0.06 (f and h), but the CO₂ adsorption is much stronger at the defect site (e and g).

Figure 5

Comparison of adsorbate interaction energies calculated with FFs and DFT. (a) Histogram of energy differences between FF and DFT for 29,644 CO₂ calculations (red) and 20,892 H₂O calculations (blue). (b) Binned errors and DFT interaction energy distributions split by adsorbate. (c, d) Absolute difference between FF and DFT energies plotted versus DFT interaction energy for CO₂ and H₂O, respectively.

Figure 6

Radar plots for S2EF (a) energy and (b) force MAEs, (c) IS2RE energy MAEs, and (d) IS2RS AFbT for the top three best models—GemNet-OC (red), eSCN (blue), and EquiformerV2 (large, except in (c) where the lighter model is shown) (cyan). Dashed lines correspond to the relaxation approach for IS2RE; all other models are direct predictions. Axes correspond to different in- and out-of-domain test sets and are aligned so that the best result is closest to the origin of the plot in all cases.

Figure 7

Binned errors and relative density of the number of points (solid lines) as a function of DFT adsorption energy for (a) ML predicted adsorption energies on open metal site (OMS) (red) and non-OMS (blue) and (b) interaction energies predicted by FFs (magenta) and corresponding adsorption energies predicted by ML (green) models. Compared to FFs, ML models are significantly more accurate in the chemisorption regime and are comparable in the physisorption regime. Positive adsorption energies are omitted from the plot because they are rare and likely unphysical; plots with the full range of adsorption energies are provided in Figure S4.

Figure 8

Force MAE on the test-id set for the top 3 S2EF models when trained on different amounts of training data. The lines show scaling laws obtained by fitting a line between log of the force MAE and log of the number of training MOFs for each model.