Grand Canonical Monte Carlo Calculations Research Articles

Machine learning insights into predicting biogas separation in metal-organic frameworks

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

Tailoring ammonia capture in MOFs and COFs: A multi-scale and machine learning comprehensive investigation of functional group modification.

Our study aims to examine the impact of ligand functionalization on the ammonia adsorption properties of MOFs and COFs, by combining multi-scale calculations with machine learning techniques. Density Functional Theory calculations were performed to investigate the interactions between ammonia (NH3) and a comprehensive set of 48 strategically chosen functional groups. In all of the cases, it is observed that functionalized rings exhibit a stronger interaction with ammonia molecule compared to unfunctionalized benzene, while -O2Mg demonstrates the highest interaction energy with ammonia (15 times stronger than the bare benzene). The trend obtained from the thorough DFT screening is verified via Grand Canonical Monte-Carlo calculations by employing interatomic potentials derived from quantum chemical calculations. Isosteric heat of adsorption plots provide a comprehensive elucidation of the adsorption process, and important insights can be taken for studies in fine-tuning materials for ammonia adsorption. Furthermore, a proof of concept machine learning (ML) analysis is conducted, which demonstrates that ML can accurately predict NH3 binding energies despite the limited amount of data. The findings derived from our multi-scale methodology indicate that the functionalization strategy can be utilized to guide synthesis towards MOFs, COFs, or other porous materials for enhanced NH3 adsorption capacity.

Effect of Chlorine on Copper Electrodissolution

A computational study on the role of adsorbed Cl ions on copper dissolution is presented. Extensive grand canonical Monte Carlo (GCMC) calculations using reactive force-field (ReaxFF) are used to check the formation chlorine (Cl) adlayer structures on Cu (100) surface as a function of the chemical potential (µ). From experimental studies conducted by other researchers it has been observed that Cl adlattice on Cu in c(2X2) at maximum coverage. We observe that at low µ, Cl adlayer is disordered and moves to ordered c(2X2) structure at high µ. The binding energy of Cl, oxygen (O), hydroxide (OH-) and H2O on Cu at different sites is calculated and compared with Density-functional theory (DFT) values. Metal surface is often highly uneven and rough during electrodissolution process. Therefore, we also discuss the adsorption isotherm, disorder-order transformation, and preferential adsorption sites for rough surfaces. Specific anion adsorption (SAA) of Cl involves partial charge transfer to Cu substrate and these effect the type of bond and bond strength which is also briefly discussed. The electrodissolution of Cu in the presence of Cl is modelled using kinetic Monte Carlo simulation to obtain the polarization curve.Keywords: Adsorbed chlorine, electrodissolution, grand canonical monte carlo, molecular dynamics, ReaxFF, kinetic monte carlo.

Flattening of a Bent Sulfonated MOF Linker: Impact on Structures, Flexibility, Gas Adsorption, CO2/N2 Selectivity, and Proton Conduction.

Rational design of organic building blocks provides opportunities to control and tune various physicochemical properties of metal-organic frameworks (MOFs), including gas handling, proton conduction, and structural flexibility, the latter of which is responsible for new adsorption phenomena and often superior properties compared to rigid porous materials. In this work, we report synthesis, crystal structures, gas adsorption, and proton conduction for a flexible two-dimensional cadmium-based MOF (JUK-13-SO3H-SO2) containing a new sulfonated 4,4'-oxybis(benzoate) linker with a blocking SO2 bridge. This two-dimensional (2D) MOF is compared in detail with a previously reported three-dimensional Cd-MOF (JUK-13-SO3H), based on analogous, but nonflat, SO2-free sulfonated dicarboxylate. The comprehensive structure-property relationships and the detailed comparisons with insights into the networks flexibility are supported by five guest-dependent structures determined by single-crystal X-ray diffraction (XRD), and corroborated by spectroscopy (IR, 1H NMR), powder XRD, and elemental/thermogravimetric analyses, as well as by volumetric adsorption measurements (for N2, CO2, H2O), ideal adsorbed solution theory (IAST), density-functional theory (DFT+D) quantum chemical and grand-canonical Monte Carlo (GCMC) calculations, and electrochemical impedance spectroscopy (EIS) studies. Whereas both dynamic MOFs show moderate proton conductivity values, they exhibit excellent CO2/N2 selectivity related to the capture of CO2 from flue gases (IAST coefficients for 15:85 mixtures are equal to ca. 250 at 1 bar and 298 K). The presence of terminal sulfonate groups in both MOFs, introduced using a unique prechlorosulfonation strategy, is responsible for their hydrophilicity and water-assisted proton transport ability. The dynamic nature of the MOFs results in the appearance of breathing-type adsorption isotherms that exhibit large hysteresis loops (for CO2 and H2O) attributed to strong host-guest interactions. Theoretical modeling provides information about the adsorption mechanism and supports interpretation of experimental CO2 adsorption isotherms.

Enhanced ethane/ethylene separation based on metal regulation in zeolitic imidazolate frameworks

The acquisition of polymer-grade (≥ 99.95%) C2H4 poses a challenge due to the presence of ethane (C2H6) having similar physical and chemical properties. Consequently, the one-step purification of C2H4 becomes a crucial and demanding process. In this study, we synthesized ZIF-78 with a GME configuration using different metal sources (Zn, Co). Both substances have been identified as ethane-selective adsorbents with excellent thermal stability. The Brunauer Emmett Teller (BET) surface area of ZIF-78-Co (748 m2/g) surpasses that of ZIF-78-Zn (585 m2/g), and the former exhibits a higher Qst value for C2H6, resulting in enhanced adsorption capacity for C2H6 (50.61 cm3/g) and selectivity for C2H6/C2H4 (1.71) compared to ZIF-78-Zn (48.97 cm3/g, 1.46) at 298 K and 1 bar. Grand Canonical Monte Carlo (GCMC) calculations indicate that C2H6 has a stronger interaction with the ZIF-78-Co framework. Breakthrough experiments for the C2H6/C2H4 (50:50, V/V) mixture at 298 K and 1 bar demonstrate that ZIF-78-Co achieves separation in approximately 5 min/g, outperforming ZIF-78-Zn. And the separation time for ZIF-78-Co in the C2H6/C2H4 (10:90, V/V) mixture is 9 min/g. Furthermore, ZIF-78-Co exhibits excellent structural stability, thermal stability, water stability, and acid-base stability. Therefore, it holds promising prospects for practical industrial separation. Additionally, we hope that our findings inspire further experimentation on alternative metal ethane adsorbents.

Behavior of VOCs competitive adsorption with water vapor in a slit-shaped phosphoric acid mesoporous activated carbon model

Pistachio shells were activated with H3PO4, and mesoporous activated carbon (AC) was pyrolyzed and prepared at high temperature. The impacts of phosphoric acid concentrations on the structure, physicochemical properties, and VOC adsorption performance of ACs were investigated. The results indicated that the specific surface area, average pore size, and surface P content of 20% phosphoric acid AC reached 1031.8 m2·g−1, 4.16 nm, and 3.87%, respectively. With increasing phosphoric acid concentration, the microporosity and graphitization of the ACs decreased, and the average pore size and P content of the ACs increased. The adsorption performance of PSAC-1P and PSAC-2P for VOCs in high-humidity exhaust gas was the most favorable. grand canonical Monte Carlo (GCMC) calculations revealed that at a relative humidity of 80%, water molecules and VOCs were adsorbed on the graphite-structured carbon surface in turn. The larger the pore size, the easier it was for water molecules to aggregate, and the adsorption sites were occupied by water molecules. P-functional groups effectively inhibited water molecular clusters from forming water clusters, benefitting from the mesopore adsorption of VOCs in competition with water vapor. The findings provide significant support for the effective adsorption treatment of VOCs in high-humidity exhaust gas.

Detection of nitrofurazone with metal–organic frameworks and reduced graphene oxide composites: insights from molecular dynamics simulations

Two-dimensional metal-organic framework (MOF) composites were produced by incorporating Fe-MOFs into reduced graphene oxide (rGO) nanosheets to form Fe-MOF/rGO composites by hydrothermal synthesis. SEM, TEM, XRD, XPS, and measurements of contact angles were used to characterize the composites. TEM studies revealed that the rod-like-shaped Fe-MOFs were extensively dispersed on the rGO sheets. Incorporating Fe-MOF into rGO significantly improves performance due to the large surface area, chemical stability, and high electrical conductivity. The response signals for the electrochemical sensing performance of Fe-MOF/rGO-modified electrodes to nitrofurazone (NFZ) were significantly enhanced. Differential pulse voltammetry was used to detect the NFZ, and the MOF/rGO sensor possesses a lower detection limit (0.77μM) with two dynamic ranges from 0.6-60 to 128-499.3 μM and high sensitivity (1.909 μA·mM-1·cm-2). Moreover, the anti-interference properties of the sensor were quite reproducible and stable. To understand the mechanism responsible for the enhanced sensing performance of the composite, grand canonical Monte Carlo calculations were performed for Fe-MOF/rGO composites with five unit cells of Fe-MOF and four layers of rGO. We attributed the improvement to the fact that the interface between the Fe-MOF and rGO absorbed increased NFZ molecules. The findings reported herein confirm that such Fe-MOF/rGO composites have significantly improved electrochemical performance and practical applicability of sensing nitrofurazone.

Superior Selective CO2 Adsorption and Separation over N2 and CH4 of Porous Carbon Nitride Nanosheets: Insights from GCMC and DFT Simulations.

Development of high-performance materials for the capture and separation of CO2 from the gas mixture is significant to alleviate carbon emission and mitigate the greenhouse effect. In this work, a novel structure of C9N7 slit was developed to explore its CO2 adsorption capacity and selectivity using Grand Canonical Monte Carlo (GCMC) and Density Functional Theory (DFT) calculations. Among varying slit widths, C9N7 with the slit width of 0.7 nm exhibited remarkable CO2 uptake with superior CO2/N2 and CO2/CH4 selectivity. At 1 bar and 298 K, a maximum CO2 adsorption capacity can be obtained as high as 7.06 mmol/g, and the selectivity of CO2/N2 and CO2/CH4 was 41.43 and 18.67, respectively. In the presence of H2O, the CO2 uptake of C9N7 slit decreased slightly as the water content increased, showing better water tolerance. Furthermore, the underlying mechanism of highly selective CO2 adsorption and separation on the C9N7 surface was revealed. The closer the adsorption distance, the stronger the interaction energy between the gas molecule and the C9N7 surface. The strong interaction between the C9N7 nanosheet and the CO2 molecule contributes to its impressive CO2 uptake and selectivity performance, suggesting that the C9N7 slit could be a promising candidate for CO2 capture and separation.

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts

Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO2(111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.

Heterofullerene C48B12-impregnated MOF-5 and IRMOF-10 for hydrogen storage: A combined DFT and GCMC simulations study

The H2 storage properties of isoreticular metal-organic framework materials (IRMOFs), MOF-5 and IRMOF-10, impregnated with different numbers and types of heterogeneous C48B12 molecules were investigated using density functional theory and grand canonical Monte Carlo (GCMC) calculations. The excess hydrogen adsorption isotherms of IRMOFs at 77 K within 20 bar indicate that suitable number and type of C48B12 molecules play a crucial role in improving the H2 storage properties of IRMOFs. Among the studied pure and nC48B12 (n = 1, 2, 4, 8) in Ci symmetry impregnating into MOF-5, at 77 K under 6 bar, MOF-5-4C48B12 with a 3.5 wt% and 29.9 g/L hydrogen storage density, and at 77 K under 12 bar, the pure MOF-5 with a 4.9 wt% and 31.0 g/L hydrogen storage density has the best hydrogen storage properties. Whereas, among the studied pure and nC48B12 (n = 1, 2, 4, 8) in S6 symmetry impregnating into IRMOF-10, IRMOF-10-8C48B12 always shows the best hydrogen storage properties among the pure and C48B12-impregnated IRMOF-10 at 77 K within 20 bar. IRMOF-10-8C48B12 has a 6.0 wt% and 34.6 g/L hydrogen storage density at 77 K under 6 bar, and has a 7.1 wt% and 41.4 g/L hydrogen storage density at 77 K under 12 bar. The confinement effect of IRMOFs on C48B12 molecules, and steric hindrance effect of C48B12 molecules on IRMOFs mainly affects the H2 uptake capacity by comparing the absolute H2 molecules in individual IRMOFs units, C48B12 molecules, and IRMOFs-nC48B12 compounds. The absolute hydrogen adsorption profiles show that eight C48B12 molecules impregnating into MOF-5 can exert obvious steric effects for H2 adsorption. The saturated gravimetric and volumetric H2 densities of IRMOF-10-8C48B12 higher than those of MOF-5-8C48B12 due to with larger free volume.

Computational Investigation of the Interaction of Multifunctionalized Porous Aromatic Frameworks with SO2

The adsorption of pure SO2, a sulfur-containing acid gas, by porous aromatic frameworks (PAFs) was investigated with a range of computational methods including first-principles density functional theory and grand canonical Monte Carlo calculations. We found that the presence of combinations of functional groups, including the electron-donating groups −CH3, −OH, and −NH2, and the electron-withdrawing groups −CN, −COOH, and −NO2, within the PAF structures was predicted to enhance SO2 uptake at low pressure. In particular, the simulations predicted that the functionalized PAFs, especially double-functionalized PAFs, PAF-(OH)2 and PAF-(COOH)2, as well as mixed-functionalized PAFs, PAF-2-CN-3-NO2 and PAF-3-OH-5-NH2, were able to capture high loadings of SO2 pure gas under a very low pressure at 298 K. The additional functional groups were able to strengthen the interactions between the PAF frameworks and the acid gas molecules. At the same time, introducing two functional groups to PAFs generally decreases the maximum adsorption limit, due to the smaller pore volume available to the gases. In this work, we created a library of various functionalized PAFs, as well as simulated their adsorption isotherms. The results of this work can be used as a guideline for other combinations of functionalized PAFs and their experimental synthesis for maximal acid gas adsorption.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules.

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

Computer simulation investigation of the adsorption of acetamide on low density amorphous ice. An astrochemical perspective.

The adsorption of acetamide on low density amorphous (LDA) ice is investigated by grand canonical Monte Carlo computer simulations at the temperatures 50, 100, and 200K, characteristic of certain domains of the interstellar medium (ISM). We found that the relative importance of the acetamide-acetamide H-bonds with respect to the acetamide-water ones increases with decreasing temperature. Thus, with decreasing temperature, the existence of the stable monolayer, characterizing the adsorption at 200K, is gradually replaced by the occurrence of marked multilayer adsorption, preceding even the saturation of the first layer at 50K. While isolated acetamide molecules prefer to lay parallel to the ice surface to maximize their H-bonding with the surface water molecules, this orientational preference undergoes a marked change upon saturation of the first layer due to increasing competition of the adsorbed molecules for H-bonds with water and to the possibility of their H-bond formation with each other. As a result, molecules stay preferentially perpendicular to the ice surface in the saturated monolayer. The chemical potential value corresponding to the point of condensation is found to decrease linearly with increasing temperature. We provide, in analogy with the Clausius-Clapeyron equation, a thermodynamic explanation of this behavior and estimate the molar entropy of condensed phase acetamide to be 34.0 J/mol K. For the surface concentration of the saturated monolayer, we obtain the value 9.1 ± 0.8 µmol/m2, while the heat of adsorption at infinitely low surface coverage is estimated to be -67.8 ± 3.0 kJ/mol. Our results indicate that the interstellar formation of peptide chains through acetamide molecules, occurring at the surface of LDA ice, might well be a plausible process in the cold (i.e., below 50K) domains of the ISM; however, it is a rather unlikely scenario in its higher temperature (i.e., 100-200K) domains.

Graph neural network predictions of metal organic framework CO[formula omitted] adsorption properties

The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow and there is a need to expedite such processes. We use Atomistic Line Graph Neural Network (ALIGNN) method to predict CO2 adsorption in metal organic frameworks (MOF), which are known for their high functional tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with 137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption isotherms. We develop high accuracy and fast models for pre-screening applications. We apply the trained model on CoREMOF database and computationally rank them for experimental synthesis. In addition to the CO2 adsorption isotherm, we also train models for electronic bandgaps, surface area, void fraction, lowest cavity diameter, and pore limiting diameter, and illustrate the strength and limitation of such graph neural network models. For a few candidate MOFs we carry out GCMC calculations to evaluate the deep-learning (DL) predictions.

Anion Regulates scu Topological Porous Coordination Polymers into the Acetylene Trap

The development of efficient porous absorbents with high uptake and selectivity remains a great challenge, especially for the recovery of acetylene (C2H2) from its carbon dioxide (CO2)-containing mixtures. Here, we propose and report an anion-planting strategy for regulating the scu topological porous coordination polymers (PCPs) into the C2H2 trap. The three electronegative anions SiF62-, TiF62-, and ZrF62-, in addition to the ligand of 3,5-di(1H-imidazol-1-yl)benzoic acid (HL) and Cu2+ ion, were employed to construct highly porous PCPs (NTU-60, NTU-61, and NTU-62) with varied window aperture. Especially, due to a matching distance (dF-F) of 5.7 Å along the c-axis, the limited space that can be assigned as a single C2H2 trap enables NTU-61 to show optimal ability for C2H2 (van der Waals (vdW) parameters of the two H atoms: ∼5.72 Å) recognition, validated by Grand Canonical Monte Carlo (GCMC) calculations and Raman spectra. These characteristics allow the NTU-series to show higher C2H2 uptake, as well as excellent C2H2/CO2 separation performance under dynamic conditions. The molecular insight and strategy here not only permit balanced adsorption and separation in a single domain but also exhibit an opportunity to develop advanced adsorbents in nearly all frameworks with lattice or coordinated ions, which may act as the platforms for various selective guest trappings with on-demand time and/or spatial resolution.

Efficient Xe/Kr separation on two Metal-Organic frameworks with distinct pore shapes

Adsorptive separation of xenon (Xe) from krypton (Kr) is challenging owing to their low concentrations and similar physical properties. Herein, we report two metal–organic frameworks (MOFs) assembled from the same aliphatic ligand (trans-1, 4-cyclohexanedicarboxylic acid, H2CDC), but showing distinct pore shapes. The rhombus pores on Al-CDC show a narrower pore aperture of 4.5 Å × 6.0 Å, contracting to the pseudo-square pores (5.8 Å × 6.8 Å) on Cu-CDC. As a result, Al-CDC exhibits a high Xe adsorption capacity (2.45 mmol g−1) and Xe/Kr selectivity (10.7) at 298 K and 1.0 bar. The confined pore space and abundant saturated C-H groups provide stronger van der Waals interactions toward Xe, which is disclosed by the Grand Canonical Monte Carlo (GCMC) and Density Functional Theory (DFT) calculations. Dynamic breakthrough experiments further confirm their industrial application potential and stable reusability.

Fullerene-impregnated IRMOFs for balanced gravimetric and volumetric H2 densities: A combined DFT and GCMC simulations study

This paper aimed to study the effects of fullerene (C60) impregnation on the isoreticular metal-organic framework (IRMOF) materials MOF-650 (ZnO4 nodes were connected to azulenedicarboxylate linkers), MOF-5(ZnO4 nodes were connected to benzenedicarboxylate linkers), and IRMOF-10 (ZnO4 nodes were connected to biphenyldicarboxylate linkers) for H2 storage, these IRMOFs had similar structures but different pore volumes and organic linkers. Density functional theory (DFT) and grand canonical monte Carlo (GCMC) calculations indicated that C60 plays an important role in balancing the gravimetric and volumetric H2 densities of the IRMOFs. The C60@IRMOFs revealed improved volumetric density when H2 was undersaturated but reduced gravimetric density under H2 saturation. The saturated gravimetric H2 density of the IRMOFs was decided by the free volume. At 77 K, C60@MOF-650 had a gravimetric H2 density of 5.3 wt% and volumetric H2 density of 42 g/L under 10 bar, and C60@IRMOF-10 had a gravimetric H2 density of 7.4 wt% and volumetric H2 density of 43 g/L under 18 bar. These values nearly meet the United States Department of Energy (DOE) gravimetric and volumetric H2 density ultimate targets (gravimetric H2 density, 6.5 wt%; volumetric H2 density, 50 g/L) under ambient pressures. Among the studied IRMOFs, C60@MOF-650 and C60@IRMOF-10 demonstrated the best H2 storage properties at 233 and 298 K.

Nitrogen‐rich melamine cyanurate derived hollow porous carbon microspheres prepared through CuCl2 activation for VOCs adsorption

AbstractNitrogen‐enriched porous carbon material with large specific surface area is of great demand in various applications. Herein, CuCl2 is adapted as a mild activator to transform the mixture of soybean meal and melamine cyanurate into a novel type of nitrogen‐enriched hollow porous carbon microspheres with well‐defined hierarchically structure. Excitingly, this mild activation agent not only helps generating porous structure, but also helps increasing the nitrogen content (mainly pyrrolic‐N) of products. The product NPC‐800‐4 exhibits the highest specific surface area (1672.45 m2 g−1) while a pretty high nitrogen content (13.09 at%). The NPC‐800‐4 sample performs the largest toluene and acetone adsorption capacity (818.46 mg g−1 and 758.87 mg g−1 respectively). Furthermore, Grand canonical Monte Carlo (GCMC) and density functional theory (DFT) calculations prove that the introduction of nitrogen functional groups can heighten the polarity of carbon framework and the electrostatic interaction, thus enhance the acetone adsorption capacity.

A combination of multi-scale calculations with machine learning for investigating hydrogen storage in metal organic frameworks

Combining multi-scale calculations with machine learning, we investigate how the ligand functionalization affects the hydrogen storage profile of Metal Organic Frameworks. The binding energy of hydrogen with 58 strategically selected functionalized benzenes was calculated with accurate ab-initio methods. Our results show that many functional groups (e.g. -OPO3H2, –OCONH2) increase the interaction strength up to 15–25% compared to benzene while –OSO3H holds the most promise with an enhancement up to 80%. Grand Canonical Monte-Carlo calculations with interatomic potentials derived from the ab-initio calculations, verify the trend obtained from the meticulous screening. In addition, a proof of principle Machine Learning analysis is performed on the ab-initio results showing a good prediction of the H2 binding energies even with a limited amount of data. The results from our bottom-up approach lead us to conclude that this functionalization strategy can be applied to various porous materials in order to enhance their hydrogen storage performance.

Computation-guided descriptor for efficient zeolite catalysts screening in C4 alkylation process

Zeolites as the environmentally friendly catalysts for C4 alkylation suffer from rapid deactivation in commercial application. In this work, the intrinsic descriptor (i.e., the i-butane/1-butene ratio in micropores) for catalyst deactivation was proposed by configurational-bias Grand Canonical Monte Carlo (CB-GCMC) calculations. It is found that pore structural topology affects the competitive adsorption between i-butane and 1-butene due to distinct structure of the two adsorbates. The i-butane/1-butene ratios in the pore decreased in the order: BEA > FAU > MOR > MFI. In other words, 3D interconnecting BEA channels formed by 12-membered rings with ordered pore configuration were suggested to have higher i-butane/1-butene ratio, because i-butane has better spatial matching with 12-membered rings on basis of the analysis of the size. Notably, experimental deactivation rate is inversely proportional to calculated i-butane/1-butene ratio inside microproes with good linear correlation. This descriptor for deactivation offers a promising avenue for rational screening of more efficient zeolite catalysts for C4 alkylation.