Metal−organic frameworks (MOFs) have drawn considerable attention for their potential in a variety of energy applications such as gas separations and storage. With thousands of MOFs reported and more being discovered, molecular simulations can play a critical role in facilitating the material discovery. In those calculations, accurate charge assignments to the framework atoms are essential. In this study, we expand on the connectivity-based atom contribution (CBAC) method to develop an efficient, robust, and accurate approach for charge assignments. Distinct from the original CBAC method, which uses 1st layer connectivity of a target atom, our approach, denoted as multilayer CBAC (m-CBAC), incorporates multilayer connectivity up to 2nd layers. An extensive set of ∼2700 MOFs with the density-derived electrostatic and chemical (DDEC) charges is used to train the databases. The approach assigns charges in a systematic manner, where the highest-level connectivity database (i.e., 2nd-layer connectivity) is first searched, followed by lower-level connectivity patterns until the connectivity pattern is recognized. This approach makes the charge predictions feasible to almost all MOFs. Our results show that the charges assigned using m-CBAC resemble the DDEC charges very well (Pearson coefficient of 0.988). At the same time, the m-CBAC approach is computationally efficient, which is orders of magnitude faster than quantum mechanical approaches. Also, this study demonstrates that the accurate charge assignments from m-CBAC lead to reliable predictions on the Henry coefficient of CO 2 in MOFs. Overall, the m-CBAC approach can enable fast charge assignments for MOFs with good accuracy, and a software for m-CBAC charge assignments together with charges assigned for ∼12 000 MOFs in a recently released MOF database is made available along with this work.

Nanoporous materials, such as metal–organic frameworks (MOFs), have shown great potential as adsorbents for separations in a wide variety of energy- or environment-related applications. One promising application is sour gas sweetening; a raw natural gas contains small amounts of H2S that can be detrimental to the efficient utilization of the energy source. However, the large database of nanoporous materials has made the discovery of optimum materials significantly demanding. While molecular simulations can play a complementary role in facilitating the materials search, their brute-force utilization still requires a vast amount of computational resources. In this study, we incorporate a machine learning algorithm with structural and chemistry descriptors as inputs for efficient screening. Specifically, the random forest regressor, which can also be useful for elucidating structure–property relationships, is employed. For reliable predictions with machine learning, the choices of features play considerably important roles. In addition to commonly adopted geometrical and chemical features, we propose and incorporate a set of newly designed features for training the model. These new features represent preferential binding sites of open-metal sites and dense framework atoms on the pore surface. Our analysis shows that the inclusion of the newly designed features greatly improves the machine learning performance. Our work can pave the way for the future design of nanoporous materials for sour gas sweetening. These newly designed features can also be used for the development of machine learning models for other applications, especially those involving molecules with strong dipole and/or quadruple moments, such as carbon capture.

Incorporating molecular amines as mobile carriers in facilitated transport membranes (FTMs) has been demonstrated to significantly enhance the CO2 permeance and CO2/N2 selectivity of the membrane for CO2 capture from flue gas. In this study, by employing computational techniques including density functional theory calculations and molecular simulations, the role of mobile carriers has been systematically studied at a molecular level from the perspectives of the amine–CO2 reaction chemistry, diffusivities of carriers and gases, and N2 solubility. The latter two properties were also investigated as a function of water uptake. The water uptake values of FTMs were experimentally quantified too. The introduction of mobile carriers was shown to substantially enhance the diffusivities of CO2 reaction products compared to FTMs without mobile carriers. The choice of mobile carriers was also demonstrated to influence the separation performance. Computationally, 2-(1-piperazinyl)­ethylamine sarcosinate (PZEA-Sar) exhibited a faster reaction kinetics and slightly higher CO2 absorption capacity as compared to piperazine glycinate (PZ-Gly). Experimentally, the FTM incorporating PZEA-Sar mobile carriers also showed a higher CO2 permeance. The good agreement validated the computational models employed and insights generated in this study. The outcomes of this work shed light on the future design and selection of carrier structures, and the adopted computational approaches can be employed to discover promising mobile carrier candidates.

No abstract

No abstract