Applicability of Tail Corrections in the Molecular Simulations of Porous Materials

A Refined Set of Universal Force Field Parameters for Some Metal Nodes in Metal-Organic Frameworks

Metal-organic frameworks (MOFs) exhibit promise as porous materials for carbon capture due to their design versatility and large pore sizes. The generic force fields (e.g., UFF and Dreiding) use one universal set of Lennard-Jones parameters for each element, while MOFs have a much richer local chemical environment than those chemical environments used to fit the UFF. When MOFs contain hard-Lewis acid metals, UFF systematically overestimates CO2 uptakes. To address this, we developed a workflow to affordably and efficiently generate reliable force fields to predict CO2 adsorption isotherms of MOFs containing metals from groups IIA (Mg, Ca, Sr, and Ba) and IIIA (Al, Ga, and In), connected to various carboxylate ligands. This method uses experimental isotherms as input. The optimal parameters are obtained by minimizing the loss function of the experimental and simulated isotherms, in which we use the Multistate Bennett Acceptance Ratio (MBAR) theory to derive the functionality relationship of loss functions in terms of force field parameters.

Study on the Influence Mechanism of Hot Flue Gas Injection Pressure on the Wettability of Anthracite Coal: From the Perspective of Molecular Dynamics

A multicomponent wetting model of coal-water-methane-hot flue gas was hereby constructed to investigate the influence of complex components of hot flue gas on coal wettability. Besides, whether it is feasible to use the NIST method to capture the system pressure was verified from a microscopic perspective. Moreover, how the interaction energy and hydrogen bonds between water and coal, the spreading length of water nanodroplets in the X-direction, and the three-phase contact angle vary with the hot flue gas injection pressure were discussed. Here are the findings: (1) The absolute value of interaction energy between water and coal is negatively correlated with the pressure. In addition, the gradient of decrease shrinks continuously when the pressure rises. (2) As the pressure rises, a decline is monitored in both the number of hydrogen bonds and the spreading length of water nanodroplets in the X-direction, and a critical pressure value exists around 32.64 MPa, which divides the variation into two stages, i.e., rapid decrease and slow decrease. (3) The three-phase contact angle grows with the rise of pressure, and its critical pressure value is similar to that of number of hydrogen bonds and spreading length. In addition, it is found that the density of the gas adsorption layer augments as the pressure rises, which can be seen that a higher injection pressure is favorable for gas wetting. These research observations brought to light that appropriately raising the hot flue gas injection pressure can promote the transition of wetting mode from water wetting to gas wetting, which is of great benefit for relieving the water lock effect and effectively improving the transportation environment of gas.

Deep learning-based recommendation system for metal-organic frameworks (MOFs)

This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a high-dimensional chemical space and suggests a pool of promising materials for specific applications based on user-endorsed MOFs with similarity analysis. This proposed approach significantly reduces the need for exhaustive labeling of every material in the database, focusing instead on a select fraction for in-depth investigation. Ranging from methane storage and carbon capture to quantum properties, this study illustrates the system's adaptability to various applications.

Avoiding pitfalls in molecular simulation of vapor sorption: Example of propane and isobutane in metal-organic frameworks for adsorption cooling applications

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal-organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance.

Pore Structure Compartmentalization for Advanced Characterization of Metal-Organic Framework Materials

Metal-organic frameworks (MOFs) are nanoporous crystals which are widely used as selective adsorbents, separation membranes, catalysts, gas and energy storage media, and drug delivery vehicles. The unique adsorption and transport properties of MOFs are determined by their complex three-dimensional (3D) networks of pores, cages, and channels that differ in size, shape, and chemical composition. While the morphological structure of MOF crystals is known, practical MOF materials are rarely ideal crystals. They contain secondary phases, binders, residual chemicals, and various types of defects. It is of paramount importance to evaluate the degree of crystallinity and accessibility of different pore compartments to adsorb guest molecules. To this end, we recently suggested the method of fingerprint isotherms based on the comparison of the experimentally measured adsorption isotherms and theoretical isotherms on ideal MOF crystals produced by Monte Carlo (MC) simulations and decomposed with respect to different pore compartments [Parashar, S. ACS Appl. Nano Mater. 2021, 4, 5531-5540 and Dantas, S.; Neimark, A. V. ACS Appl. Mater. Interfaces 2020, 12, 15595-15605]. In this work, we develop an automated algorithm for pore network compartmentalization that is a prerequisite for calculations of the fingerprint isotherms. The proposed algorithm partitions the unit cell into realistically shaped compartments based on the geometric pore size distribution. The proposed method is demonstrated on several characteristic systems, including Cu-BTC, IRMOF-1, UiO-66, PCN-224, ZIF-412, and 56 structures from the CoRE MOF database.

Exploring the Potential of Metal-Organic Frameworks for Cryogenic Helium-Based Gas Gap Heat Switches via High-Throughput Computational Screening

With the advantages of a long lifetime and high reliability, gas gap heat switches (GGHSs) are attractive in many thermal management applications, especially in space-borne cryogenic systems. The performance of a GGHS is significantly affected by the adsorption characteristics of the adsorbent in the sorption pump. Compared with the commonly used adsorbent in the GGHSs (activated carbon), metal-organic frameworks (MOFs) have larger surface areas, higher pore volumes, and exceptional tunability, which motivates this study to explore their potential for application in cryogenic GGHSs. To this end, two performance metrics, the required volume of adsorbent (vsor) and total input heat (qtot), were computed for about 6000 MOFs via molecular simulations and compared with those of activated carbon. It is found that over 2300 MOFs possess a smaller vsor than activated carbon, and the smallest vsor of MOFs is about 12.7% of that of activated carbon. vsor and qtot generally change in the same direction, which implies it is possible to reduce both parameters simultaneously by choosing a suitable MOF. Structure-performance analysis reveals that 1/vsor consistently increases first and then decreases with pore limiting diameter, largest cavity diameter, available pore volume, accessible surface area, helium void fraction, and bulk density. Descriptor ranges corresponding to high-performing MOFs were identified based on Precision-Recall analysis. Notably, Zr-containing MOFs are particularly likely to have smaller vsor values than activated carbon. It is anticipated that the promising MOFs identified by this study will motivate further experimental investigations, and the insights into structure-performance relationships can serve to guide the rational design of novel MOF candidates for GGHSs.

Wetting Behavior of Kerogen Surfaces: Insights from Molecular Dynamics

In this study, the wettability of a kerogen surface, a key component of shale reservoirs, is investigated by using molecular dynamics simulations. Specifically, we examined the impact of droplet size and morphology as well as surface roughness on the water contact angles. The findings highlighted that the contact angle dependency on the droplet size intensifies with increased rigidity of the surface. Conversely, as the surface becomes more flexible and rougher, it gains hydrophilicity. The higher hydrophilicity stems from the ability of water molecules to penetrate the kerogen corrugations and form more hydrogen bonds with heteroatoms, particularly oxygen. Notably, the contact angle of kerogen hovers between 65 and 75°, thereby crossing the transition from an underoil hydrophilic to an underoil hydrophobic state. Consequently, minor alterations in the kerogen nanostructure can dramatically alter the wetting preference between water and oil. This insight is of paramount significance for refining strategies in managing fluid interactions in shale reservoirs such as geological carbon storage or oil extraction.

Ultrahigh Lithium Selective Transport in Two-Dimensional Confined Ice

Inspired by selective ion transport in biological membrane proteins, researchers developed artificial ion channels that sieve monovalent cations, catering to the increasing lithium demand. In this work, we engineered an ion transport channel based on the confined ice within two-dimensional (2D) capillaries and found that the permselectivity of monovalent cations depends on the anisotropy of the confined ice. Particularly, the 2D confined ice showed an anomalous lithium selective transport along the (002) direction in the vermiculite capillary, with the Li+/Na+ and Li+/K+ permselectivity reaching up to 556 ± 86 and 901 ± 172, respectively, superior to most ion-selective channels. However, the 2D confined ice along the (100) direction showed less Li+ permselectivity. Additionally, the anisotropy of 2D confined ice can be tuned by adjusting the interlayer spacing. By providing insights into the ion transport in the 2D confined ice, our work may inspire more design of monovalent ion-selective channels for efficient lithium separation.

Probing Structural Defects in MOFs Using Water Stability

Defects in the crystal structures of metal-organic frameworks (MOFs), whether present intrinsically or introduced via so-called defect engineering, can play strong roles in the properties of MOFs for various applications. Unfortunately, direct experimental detection and characterization of defects in MOFs are very challenging. We show that in many cases, the differences between experimentally observed and computationally predicted water stabilities of MOFs can be used to deduce information on the presence of point defects in real materials. Most computational studies of MOFs consider these materials to be defect-free, and in many cases, the resulting structures are predicted to be hydrophobic. Systematic experimental studies, however, have shown that many MOFs are hydrophilic. We show that the existence of chemically plausible point defects can often account for this discrepancy and use this observation in combination with detailed molecular simulations to assess the impact of local defects and flexibility in a variety of MOFs for which defects had not been considered previously.

Computational investigation of hysteresis and phase equilibria of n-alkanes in a metal-organic framework with both micropores and mesopores

Adsorption hysteresis is a phenomenon related to phase transitions that can impact applications such as gas storage and separations in porous materials. Computational approaches can greatly facilitate the understanding of phase transitions and phase equilibria in porous materials. In this work, adsorption isotherms for methane, ethane, propane, and n-hexane were calculated from atomistic grand canonical Monte Carlo (GCMC) simulations in a metal-organic framework having both micropores and mesopores to better understand hysteresis and phase equilibria between connected pores of different size and the external bulk fluid. At low temperatures, the calculated isotherms exhibit sharp steps accompanied by hysteresis. As a complementary simulation method, canonical (NVT) ensemble simulations with Widom test particle insertions are demonstrated to provide additional information about these systems. The NVT+Widom simulations provide the full van der Waals loop associated with the sharp steps and hysteresis, including the locations of the spinodal points and points within the metastable and unstable regions that are inaccessible to GCMC simulations. The simulations provide molecular-level insight into pore filling and equilibria between high- and low-density states within individual pores. The effect of framework flexibility on adsorption hysteresis is also investigated for methane in IRMOF-1.

An Ecosystem for Digital Reticular Chemistry

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.

Can molecular simulations reliably compare homogeneous and heterogeneous ice nucleation?

In principle, the answer to the posed titular question is undoubtedly "yes." But in practice, requisite reference data for homogeneous systems have been obtained with a treatment of intermolecular interactions that is different from that typically employed for heterogeneous systems. In this article, we assess the impact of the choice of truncation scheme when comparing water in homogeneous and inhomogeneous environments. Specifically, we use explicit free energy calculations and a simple mean field analysis to demonstrate that using the "cut-and-shift" version of the Lennard-Jones potential (common to most simple point charge models of water) results in a systematic increase in the melting temperature of ice I_h. In addition, by drawing an analogy between a change in cutoff and a change in pressure, we use existing literature data for homogeneous ice nucleation at negative pressures to suggest that enhancements due to heterogeneous nucleation may have been overestimated by several orders of magnitude.

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

Competitive Adsorption of Xylenes at Chemical Equilibrium in Zeolites

The separation of xylenes is one of the most important processes in the petrochemical industry. In this article, the competitive adsorption from a fluid-phase mixture of xylenes in zeolites is studied. Adsorption from both vapor and liquid phases is considered. Computations of adsorption of pure xylenes and a mixture of xylenes at chemical equilibrium in several zeolite types at 250 °C are performed by Monte Carlo simulations. It is observed that shape and size selectivity entropic effects are predominant for small one-dimensional systems. Entropic effects due to the efficient arrangement of xylenes become relevant for large one-dimensional systems. For zeolites with two intersecting channels, the selectivity is determined by a competition between enthalpic and entropic effects. Such effects are related to the orientation of the methyl groups of the xylenes. m-Xylene is preferentially adsorbed if xylenes fit tightly in the intersection of the channels. If the intersection is much larger than the adsorbed molecules, p-xylene is preferentially adsorbed. This study provides insight into how the zeolite topology can influence the competitive adsorption and selectivity of xylenes at reaction conditions. Different selectivities are observed when a vapor phase is adsorbed compared to the adsorption from a liquid phase. These insight have a direct impact on the design criteria for future applications of zeolites in the industry. MRE-type and AFI-type zeolites exclusively adsorb p-xylene and o-xylene from the mixture of xylenes in the liquid phase, respectively. These zeolite types show potential to be used as high-performing molecular sieves for xylene separation and catalysis.

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.