We introduce CoRE MOF DB 2024, an openly accessible database of ∼14 k computation‑ready metal‑organic 𝖿ramework structures enriched with machine‑learned properties (stability metrics, heat capacities, DDEC06 charges, etc.). An upgraded MOFid 2.0 encodes metal nodes, organic linkers, and network topologies. Coupling the database with high‑fidelity temperature‑swing adsorption simulations enabled integrated material‑process screening for diverse CO₂‑capture scenarios, identifying a dozen MOFs predicted to outperform industrial benchmark CALF‑20. The automated curation pipeline allows continuous updates and can accelerate data‑driven discovery across adsorption‑based separations.

- Authors (Pusan National University): Guobin Zhao, Haewon Kim, Sunghyun Yoon, Yongchul G. Chung (School of Chemical Engineering, Graduate School of Data Science)

- Title of original paper: CoRE MOF DB: a curated experimental metal‑organic 𝖿ramework database with machine‑learned properties for integrated material‑process screening

- Journal: Matter

- Web link: https://doi.org/10.1016/j.matt.2025.102140 

- Contact e-mail: drygchung@gmail.com