PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks.

We report a fast and easy method (PACMAN) to assign partial atomic charges on metal-organic framework (MOF) and covalent-organic framework (COF) crystal structures based on graph convolution networks (GCNs) trained on >1.8 million high-fidelity partial atomic charge data obtained from the Quantum Metal-Organic Framework (QMOF) database. The developed model shows outstanding performance, achieving a mean absolute error (MAE) of 0.0055 e (test set performance) while maintaining consistency with DDEC6, Bader, and CM5 charges across diverse chemistry and topologies of MOFs and COFs. We find that the new method accurately assigns partial atomic charges for ion-containing nanoporous materials, which has not been possible in previous machine learning (ML) models. Grand canonical Monte Carlo (GCMC) simulation results for CO2 and N2 uptakes and the Widom particle insertion calculation for Henry's law constant of water results based on PACMAN and the original DDEC6 charges show excellent agreements compared to other ML models reported in the literature. The runtime analysis of the new method demonstrates that the partial atomic charges of MOF and COF structures with up to 500 atoms can be obtained in less than 10 s. An easy-to-use web interface has been developed to facilitate the adoption of the developed model.

High Hydrogen Storage in Trigonal Prismatic Monomer-Based Highly Porous Aromatic Frameworks.

Hydrogen storage is crucial in the shift toward a carbon-neutral society, where hydrogen serves as a pivotal renewable energy source. Utilizing porous materials can provide an efficient hydrogen storage solution, reducing tank pressures to manageable levels and circumventing the energy-intensive and costly current technological infrastructure. Herein, two highly porous aromatic frameworks (PAFs), C-PAF and Si-PAF, prepared through a Yamamoto C─C coupling reaction between trigonal prismatic monomers, are reported. These PAFs exhibit large pore volumes and Brunauer-Emmett-Teller areas, 3.93 cm3 g-1 and 4857 m2 g-1 for C-PAF, and 3.80 cm3 g-1 and 6099 m2 g-1 for Si-PAF, respectively. Si-PAF exhibits a record-high gravimetric hydrogen delivery capacity of 17.01 wt% and a superior volumetric capacity of 46.5 g L-1 under pressure-temperature swing adsorption conditions (77 K, 100 bar → 160 K, 5 bar), outperforming benchmark hydrogen storage materials. By virtue of the robust C─C covalent bond, both PAFs show impressive structural stabilities in harsh environments and unprecedented long-term durability. Computational modeling methods are employed to simulate and investigate the structural and adsorption properties of the PAFs. These results demonstrate that C-PAF and Si-PAF are promising materials for efficient hydrogen storage.

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF6 Replacement Gases.

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth's infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule's atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson's method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates.

Brunauer-Emmett-Teller Areas from Nitrogen and Argon Isotherms Are Similar.

Despite recommendations from the 2015 International Union of Pure and Applied Chemistry (IUPAC) technical report, surface areas of porous materials continue to be characterized by an N2 adsorption isotherm using the Brunauer-Emmett-Teller (BET) method. In this study, we provide the basis for such a practice by carrying out systematic large-scale molecular simulations on homogeneous and heterogeneous model surfaces. Specifically, we investigated the purported "orientational effect" of the N2 molecule on these surfaces. Grand canonical Monte Carlo (GCMC) simulation results from 257 diverse metal-organic frameworks show that the BET areas from Ar and N2 are similar in the range of 250-7500 m2/g with a mean deviation of 4%. Detailed analyses based on the consistency criteria for BET equations reveal that the large deviation (>10%) between the BET areas from Ar and N2 are materials specific and more prone to materials that are not able to satisfy the 3 and 4 consistency criteria. For materials that satisfy all four consistency criteria, the BET areas predicted from Ar and N2 isotherms are comparable.

Discovery of High-Performing Metal-Organic Frameworks for On-Board Methane Storage and Delivery via LNG-ANG Coupling: High-Throughput Screening, Machine Learning, and Experimental Validation.

Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG-ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG-ANG to attain the advanced research projects agency-energy (ARPA-E) target for onboard methane storage has not been fully investigated. In this work, large-scale computational screening is performed for 5446 metal-organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm3 (STP) cm-3 ) are identified. Furthermore, structure-performance relationships are realized under the LNG-ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT-23(Cu) and DUT-23(Co) show higher working capacities (≈373 cm3 (STP) cm-3 ) than that of any other porous material under ANG or LNG-ANG conditions, and excellent stability during cyclic LNG-ANG operation.

Microplasma Band Structure Engineering in Graphene Quantum Dots for Sensitive and Wide-Range pH Sensing.

pH sensing using active nanomaterials is promising in many fields ranging from chemical reactions to biochemistry, biomedicine, and environmental safety especially in the nanoscale. However, it is still challenging to achieve nanotechnology-enhanced rapid, sensitive, and quantitative pH detection with stable, biocompatible, and cost-effective materials. Here, we report a rational design of nitrogen-doped graphene quantum dot (NGQD)-based pH sensors by boosting the NGQD pH sensing properties via microplasma-enabled band-structure engineering. Effectively and economically, the emission-tunable NGQDs can be synthesized from earth-abundant chitosan biomass precursor by controlling the microplasma chemistry under ambient conditions. Advanced spectroscopy measurements and density functional theory (DFT) calculations reveal that functionality-tuned NGQDs with enriched -OH functional groups and stable and large Stokes shift along the variations of pH value can achieve rapid, label-free, and ionic-stable pH sensing with a wide sensing range from pH 1.8 to 13.6. The underlying mechanism of pH sensing is related to the protonation/deprotonation of -OH group of NGQDs, leading to the maximum pH-dependent luminescence peak shift along with the bandgap broadening or narrowing. In just 1 h, a single microplasma jet can produce a stable colloidal NGQD dispersion with 10 mg/mL concentration lasting for at least 100 pH detections, and the process is scalable. This approach is generic and opens new avenues for nanographene-based materials synthesis for applications in sensing, nanocatalysis, energy generation and conversion, quantum optoelectronics, bioimaging, and drug delivery.

Shape-Selective Ultramicroporous Carbon Membranes for Sub-0.1 nm Organic Liquid Separation.

Liquid-phase chemical separations from complex mixtures of hydrocarbon molecules into singular components are large-scale and energy-intensive processes. Membranes with molecular specificity that efficiently separate molecules of similar size and shape can avoid phase changes, thereby reducing the energy intensity of the process. Here, forward osmosis molecular differentiation of hexane isomers through a combination of size- and shape-based separation of molecules is demonstrated. An ultramicroporous carbon membrane produced with 6FDA-polyimides realized the separation of isomers for different shapes of di-branched, mono-branched, and linear molecules. The draw solvents provide the driving force for fractionation of hexane isomers with a sub-0.1 nm size difference at room temperature without liquid-phase pressurization. Such membranes could perform bulk chemical separations of organic liquids to achieve major reductions in the energy intensity of the separation processes.

High-Throughput Discovery of Ni(IN)2 for Ethane/Ethylene Separation.

Although ethylene (C2 H4 ) is one of the most critical chemicals used as a feedstock in artificial plastic chemistry fields, it is challenging to obtain high-purity C2 H4 gas without any trace ethane (C2 H6 ) by the oil cracking process. Adsorptive separation using C2 H6 -selective adsorbents is beneficial because it directly produces high-purity C2 H4 in a single step. Herein, Ni(IN)2 (HIN = isonicotinic acid) is computationally discovered as a promising adsorbent with the assistance of the multiscale high-throughput computational screening workflow and Computation-Ready, Experimental (CoRE) metal-organic framework (MOF) 2019 database. Ni(IN)2 is subsequently synthesized and tested to show the ideal adsorbed solution theory (IAST) selectivity of 2.45 at 1 bar for a C2 H6 /C2 H4 mixture (1:15), which is one of the top-performing selectivity values reported for C2 H6 -selective MOFs as well as excellent recyclability, suggesting that this material is a promising C2 H6 -selective adsorbent. Process-level simulation results based on experimental isotherms demonstrate that the material is one of the top materials reported to date for ethane/ethylene separation under the conditions considered in this work.

Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method.

Surface areas of porous materials such as metal-organic frameworks (MOFs) are commonly characterized using the Brunauer-Emmett-Teller (BET) method. However, it has been shown that the BET method does not always provide an accurate surface area estimation, especially for large-surface area MOFs. In this work, we propose, for the first time, a data-driven approach to accurately predict the surface area of MOFs. Machine learning is employed to train models based on adsorption isotherm features of more than 300 diverse structures to predict a benchmark measure of the surface area known as the true monolayer area. We demonstrate that the ML-based methods can predict true monolayer areas significantly better than the BET method, showing great promise for their potential as a more accurate alternative to the BET method in the structural characterization of porous materials.

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation.

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.

The Origin of p-Xylene Selectivity in a DABCO Pillar-Layered Metal-Organic Framework: A Combined Experimental and Computational Investigation.

We report high experimental p-xylene (pX) selectivity in a pillar-layered metal-organic framework, DUT-8(Cu). Vapor- and liquid-phase adsorption experiments were carried out to confirm high pX selectivity and large pX uptakes in DUT-8(Cu). Grand canonical Monte Carlo simulation results show that the presence of DABCO ligands allows for the packing of pX molecules and is responsible for the pX selective nature of the material. The simulation also suggests that the presence of isooctane solvents in the liquid-phase experiments plays an essential role by lowering the adsorption of other xylene isomers, and leads to increased pX selectivity in the liquid-phase as compared to the vapor phase. Density functional theory simulations show that the preferential arrangement is due to the preferential adsorption of pX on the DABCO ligand and the preferential adsorption of isooctane over other xylene isomers.

High-Throughput Computational Screening of Multivariate Metal-Organic Frameworks (MTV-MOFs) for CO2 Capture.

Multivariate metal-organic frameworks (MTV-MOFs) contain multiple linker types within a single structure. Arrangements of linkers containing different functional groups confer structural diversity and surface heterogeneity and result in a combinatorial explosion in the number of possible structures. In this work, we carried out high-throughput computational screening of a large number of computer-generated MTV-MOFs to assess their CO2 capture properties using grand canonical Monte Carlo simulations. The results demonstrate that functionalization enhances CO2 capture performance of MTV-MOFs when compared to their parent (unfunctionalized) counterparts, and the pore size plays a dominant role in determining the CO2 adsorption capabilities of MTV-MOFs irrespective of the combinations of the three functional groups (-F, -NH2, and -OCH3) that we investigated. We also found that the functionalization of parent MOFs with small pores led to larger enhancements in CO2 uptake and CO2/N2 selectivity than functionalization in larger-pore MOFs. Free energy contour maps are presented to visually compare the influence of linker functionalization between frameworks with large and small pores.

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm.

Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here.

High-Throughput Screening of Metal-Organic Frameworks for CO2 Capture in the Presence of Water.

Competitive coadsorption of water is a major problem in the deployment of adsorption-based CO2 capture. Water molecules may compete for adsorption sites, reducing the capacity of the material, and dehumidification prior to separating CO2 from N2 increases process complexity and cost. The development of adsorbent materials that can selectively adsorb CO2 in the presence of water would be a major step forward in the deployment of CO2 capture materials in practice. In this study, large-scale computational screening was carried out to search for metal-organic frameworks (MOFs) with high selectivity toward CO2 over H2O. Calculating framework charges for thousands of MOFs is a significant challenge, so initial screening used a fast, but approximate, charge calculation method. On the basis of the initial screening, 15 MOFs were selected, and Monte Carlo simulations were carried out to compute the adsorption isotherms for these MOFs using more accurate framework charges calculated by density functional theory. A detailed investigation was performed on the effect of using different methods for calculating partial charges, and it was found that electrostatic interactions contribute the majority of the adsorption energy of H2O in the selected MOFs.

Water stabilization of Zr6-based metal-organic frameworks via solvent-assisted ligand incorporation.

Water stability in metal-organic frameworks (MOFs) is critical for several practical applications. While water instability is mainly thought to stem from linker hydrolysis, MOFs with strong, hydrolysis-resistant metal-linker bonds can be susceptible to damage by capillary forces, which cause cavities and channels to collapse during activation from water. This study utilizes metal node functionalization as a strategy to create vapor-stable and recyclable MOFs.

Atomic mobility in a polymer glass after shear and thermal cycles.

Molecular dynamics simulations and energy landscape analyses are carried out to study the atomic mobility of a polymer glass during the physical aging process that follows shear and thermal cycles. The mobility is characterized by the fraction of atoms moving more than their diameter in a given time interval. The mobility is enhanced after a shear or thermal cycle, and this enhancement decays with time. These mobility results are related to the position of the system on the energy landscape, as characterized by the average energy of the energy minima visited by the system; the mobility over longer time scales increases with the average energy of the energy minima visited, but the mobility over shorter time scales does not show a correlation with this average energy. From these results, we conclude that barriers separating metabasins composed of proximate energy minima, rather than barriers between individual energy minima, control the physical aging process. We also show that, after some finite time, the mobility following shear and thermal cycle appears to behave similarly to the mobility without perturbations; however, the system is at different regions of the energy landscape in these two cases, which implies that mobility alone does not characterize the state of the system.

Sheared polymer glass and the question of mechanical rejuvenation.

There has been much recent debate as to whether mechanical deformation reverses the aging of a material, and returns it to a structure characteristic of the system at a higher temperature. We use molecular dynamics simulation to address this problem by carrying out shear and temperature increase simulation on atactic glassy polystyrene. Our results show explicitly that the structure (as quantified by the torsion population) changes associated with shear and temperature increase are quantitatively--and in some cases qualitatively--different. This is due to the competition between rejuvenation and physical aging, and we show this by carrying out a relaxation simulation. The conclusion agrees with those from previous experiments and simulations, which were suggestive of mechanical deformation moving the system to structures distinct from those reached during thermal treatment.