Open DAC 2023
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Paper summary
Meta releases a large DFT dataset for training ML models that predict sorbent-adsorbate interactions in Direct Air Capture (DAC).
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01
38M+ DFT calculations: Consists of more than 38M density functional theory calculations on metal-organic frameworks (MOFs), enabling large-scale ML-driven DAC material discovery.
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DAC research: Targets direct air capture, where efficient CO₂-capturing MOFs are needed - a high-impact climate application for ML.
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ML baselines: Provides strong ML baselines showing that ML surrogates can replace expensive DFT calculations for MOF screening.
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Open-science contribution: Positions the dataset as an open foundation for materials ML research on climate applications.