Open DAC 2023
Free while signed in. Answers cite the passages they came from.
Meta releases a large DFT dataset for training ML models that predict sorbent-adsorbate interactions in Direct Air Capture (DAC).
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.
DAC research: Targets direct air capture, where efficient CO₂-capturing MOFs are needed - a high-impact climate application for ML.
ML baselines: Provides strong ML baselines showing that ML surrogates can replace expensive DFT calculations for MOF screening.
Open-science contribution: Positions the dataset as an open foundation for materials ML research on climate applications.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack