A hydrophobic CO 2 physisorbent Most materials for carbon dioxide (CO 2 ) capture of fossil fuel combustion, such as amines, rely on strong chemisorption interactions that are highly selective but can incur a large energy penalty to release CO 2 . Lin et al . show that a zinc-based metal organic framework material can physisorb CO 2 and incurs a lower regeneration penalty. Its binding site at the center of the pores precludes the formation of hydrogen-bonding networks between water molecules. This durable material can preferentially adsorb CO2 at 40% relative humidity and maintains its performance under flue gas conditions of 150°C. —PDS

Metal−organic frameworks (MOFs) have garnered interest as potential solid sorbent materials for postcombustion CO 2 capture. With a seemingly infinite design space, high-throughput computational screening of MOFs has developed into an effective tool for the development of new materials. In this work, machine learning (ML) has been used to develop accurate quantitative structure−property relationship (QSPR) models to rapidly predict the CO 2 working capacity and CO 2 /N 2 selectivity at the low-pressure conditions relevant to postcombustion carbon capture (0.15 bar CO 2 , 0.85 bar N 2 ). A database of over 340 000 MOFs constructed from hundreds of types of building units arranged in over 1000 net topologies was used to train and test the models. Neural network ML models were optimized using six geometric descriptors along with three so-called chemical descriptors, namely, the atomic property-weighted radial distribution function (AP-RDF) and some variants thereof, the bag-of-atoms, and the chemical motif density descriptors. The ML models built using geometric descriptors alone resulted in test set correlation R 2 values of only 0.71 and 0.75 for CO 2 working capacity and CO 2 /N 2 selectivity, respectively. ML models built with a single type of chemical descriptor all outperformed the geometry-only models giving R 2 values ranging from 0.83 to 0.94 with the AP-RDF model being the most accurate. Overall, the best model was built using a combination of AP-RDF, chemical motif, and geometric descriptors (R 2 = 0.96 when predicting the CO 2 working capacity and R 2 = 0.95 for the selectivity). To date, these are the most accurate ML models for predicting low-pressure gas uptake of MOFs. The combined model was able to capture 994 of the true top 1000 MOFs (from a test set of ∼70 000) within the top 5000 MOFs as predicted by the model with CO 2 working capacity as the target. Thus, if the ML model were used to prescreen materials for more compute intensive GCMC simulations, then it would result in a greater than 10 times speed up while still capturing >99% of high-performing materials. These results highlight the importance of chemical descriptors in predicting low-pressure gas adsorption properties in nanoporous materials.

Metal–organic frameworks (MOFs) are a class of crystalline materials composed of metal nodes or clusters connected via semi-rigid organic linkers. Owing to their high-surface area, porosity, and tunability, MOFs have received significant attention for numerous applications such as gas separation and storage. Atomistic simulations and data-driven methods [e.g., machine learning (ML)] have been successfully employed to screen large databases and successfully develop new experimentally synthesized and validated MOFs for CO2 capture. To enable data-driven materials discovery for any application, the first (and arguably most crucial) step is database curation. This work introduces the ab initio REPEAT charge MOF (ARC–MOF) database. This is a database of ∼280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. A key feature of ARC–MOF is that it contains density functional theory-derived electrostatic potential fitted partial atomic charges for each MOF. Additionally, ARC–MOF contains pre-computed descriptors for out-of-the-box ML applications. An in-depth analysis of the diversity of ARC–MOF with respect to the currently mapped design space of MOFs was performeda critical, yet commonly overlooked aspect of previously reported MOF databases. Using this analysis, balanced subsets from ARC–MOF for various ML purposes have been identified, with a case study of the effect of training set on the ML performance. Other chemical and geometric diversity analyses are presented, with an analysis on the effect of the charge-assignment method on atomistic simulation of the gas uptake in MOFs.

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The threshold photoelectron spectra (TPES) and ion dissociation breakdown curves for trifluoroacetic acid (TFA) and trifluoroacetic anhydride (TFAN) were measured by imaging photoelectron photoion coincidence spectroscopy employing both effusive room-temperature samples and samples introduced in a seeded molecular beam. The fine structure in the breakdown diagram of TFA mirroring the vibrational progression in the TPES suggests that direct ionization to the X̃ + state leads to parent ions with a lower “effective temperature” than nonresonant ionization in between the vibrational progression. Composite W1U, CBS-QB3, CBS-APNO, G3, and G4 calculations yielded an average ionization energy (IE) of 11.69 ± 0.06 eV, consistent with the experimental value of 11.64 ± 0.01 eV, based on Franck–Condon modeling of the TPES. The measured 0 K appearance energies (AE0K) for the reaction forming CO2H+ + CF3 from TFA were 11.92 for effusive data and 11.94 ± 0.01 eV for molecular beam data, consistent with the calculated composite method 0 K reaction energy of 11.95 ± 0.08 eV. Together with the 0 K heats of formation (Δf H 0K) of CO2H+ and CF3, this yields a Δf H 0K of neutral TFA of −1016.6 ± 1.5 kJ mol–1 (−1028.3 ± 1.5 kJ mol–1 at 298 K). TFAN did not exhibit a molecular ion at room-temperature conditions, but a small signal was observed when rovibrationally cold species were probed in a molecular beam. The two observed dissociation channels were CF3C­(O)­OC­(O)+ + CF3 and the dominant, sequential reaction CF3CO+ + CF3 + CO2. Calculations revealed a low-energy isomer of ionized TFAN, incorporating the three moieties CF3CO+, CF3, and CO2 joined in a noncovalent complex, mediating its unimolecular dissociation.