Chemically Meaningful Atomic Charges That Reproduce the Electrostatic Potential in Periodic and Nonperiodic Materials

Variational Hirshfeld Partitioning: General Framework and the Additive Variational Hirshfeld Partitioning Method

We introduce the general mathematical framework of variational Hirshfeld partitioning, wherein the best possible approximation to a molecule's electron density is obtained by minimizing the f-divergence between the molecular density and a non-negative linear combination of (normalized) basis functions. This framework subsumes several existing methods that variationally optimize their pro-atoms, like (Gaussian) iterative stockholder analysis (ISA and GISA) and minimal basis iterative stockholder partitioning (MBIS), and provides a solid foundation for developing mathematically rigorous partitioning schemes. In this paper, we delve into the mathematical underpinnings of Hirshfeld-inspired partitioning schemes and show that among all the valid f-divergence measures only the extended Kullback-Leibler is a suitable choice. This led us to develop a novel partitioning scheme, called additive variational Hirshfeld (AVH), which constructs the pro-molecular density as a convex linear combination of the densities from selected states of isolated atoms and atomic ions. The AVH method is size-consistent with a unique solution and provides a straightforward approach for adding constraints for fragment properties. It also results in an intuitively appealing valence-bond-like decomposition of the molecular density as a weighted average of the densities of the atomic states in the molecule; that is, the AVH atomic density is a minimal deformation of the corresponding isolated atomic reference state's density. Compared to other variational Hirshfeld variants, our numerical results show that AVH yields chemically interpretable and sensible atomic charges that are not excessively large and demonstrate computational robustness.

Ionic liquids intercalation in titanium carbide MXenes: A first-principles investigation

Herein, we present a density functional theory with dispersion correction (DFT-D) calculations that focus on the intercalation of ionic liquids (ILs) electrolytes into the two-dimensional (2D) Ti3C2Tx MXenes. These ILs include the cation 1-ethyl-3-methylimidazolium (Emim+), accompanied by three distinct anions: bis(trifluoromethylsulfonyl)imide (TFSA-), (fluorosulfonyl)imide (FSA-) and fluorosulfonyl(trifluoromethanesulfonyl)imide (FTFSA-). By altering the surface termination elements, we explore the intricate geometries of IL intercalation in neutral, negative, and positive pore systems. Accurate estimation of charge transfer is achieved through five population analysis models, such as Hirshfeld, Hirshfeld-I, DDEC6 (density derived electrostatic and chemical), Bader, and VDD (voronoi deformation density) charges. In this work, we recommend the DDEC6 and Hirshfeld-I charge models, as they offer moderate values and exhibit reasonable trends. The investigation, aimed at visualizing non-covalent interactions, elucidates the role of cation-MXene and anion-MXene interactions in governing the intercalation phenomenon of ionic liquids within MXenes. The magnitude of this role depends on two factors: the specific arrangement of the cation, and the nature of the anionic species involved in the process.

Electrostatic Repulsion Facilitated Ion Transport in Covalent-Organic Framework Membranes

Covalent-organic framework (COF) membranes are increasingly used for many potential applications including ion separation, fuel cells, and ion batteries. It is of central importance to fundamentally and quantitatively understand ion transport in COF membranes. In this study, a series of COF membranes is designed with different densities and arrangements of functional groups and subsequently utilize molecular simulation to provide microscopic insights into ion transport in these membranes. The membrane with a single-sided layer exhibits the highest chloride ion (Cl-) conductivity of 77.2 mS cm-1 at 30 °C. Replacing the single-sided layer with a double-sided layer or changing layer arrangement leads to a decrease in Cl- conductivity up to 33% or 53%, respectively. It is revealed that the electrostatic repulsion between ions serves as a driving force to facilitate ion transport and the positions of functional groups determine the direction of electrostatic repulsion. Furthermore, the ordered pores generate concentrated ions and allow rapid ion transport. This study offers bottom-up inspiration on the design of new COF membranes with moderate density and proper arrangement of functional groups to achieve high ion conductivity.

Precision-engineered metal-organic frameworks: fine-tuning reverse topological structure prediction and design

Digital discoveries of metal-organic frameworks (MOFs) have been significantly advanced by the reverse topological approach (RTA). The node-and-linker assembly strategy allows predictable reticulations predefined by in silico coordination templates; however, reticular equivalents lead to substantial combinatorial explosion due to the infinite design space of building units (BUs). Here, we develop a fine-tuned RTA for the structure prediction of MOFs by integrating precise topological constraints and leveraging reticular chemistry, thus transcending traditional exhaustive trial-and-error assembly. From an extensive array of chemically realistic BUs, we subsequently design a database of 94 823 precision-engineered MOFs (PE-MOFs) and further optimize their structures. The PE-MOFs are assessed for post-combustion CO2 capture in the presence of H2O and top-performing candidates are identified by integrating three stability criteria (activation, water and thermal stabilities). This study highlights the potential of synergizing PE with the RTA to enhance efficiency and precision for computational design of MOFs and beyond.

MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks

Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call MEPO-ML, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs.

Ion Transport at Polymer-Argyrodite Interfaces

Solid-state electrolytes, particularly polymer/ceramic composite electrolytes, are emerging as promising candidates for lithium-ion batteries due to their high ionic conductivity and mechanical flexibility. The interfaces that arise between the inorganic and organic materials in these composites play a crucial role in ion transport mechanisms. While lithium ions are proposed to diffuse across or parallel to the interface, few studies have directly examined the quantitative impact of these pathways on ion transport and little is known about how they affect the overall conductivity. Here, we present an atomistic study of lithium-ion (Li+) transport across well-defined polymer-argyrodite interfaces. We present a force field for polymer-argyrodite interfacial systems, and we carry out molecular dynamics and enhanced sampling simulations of several composite systems, including poly(ethylene oxide) (PEO)/Li6PS5Cl, hydrogenated nitrile butadiene rubber (HNBR)/Li6PS5Cl, and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP)/Li6PS5Cl. For the materials considered here, Li-ion exhibits a preference for the ceramic material, as revealed by free energy differences for Li-ion between the inorganic and the organic polymer phase in excess of 13 kBT. The relative free energy profiles of Li-ion for different polymeric materials exhibit similar shapes, but their magnitude depends on the strength of interaction between the polymers and Li-ion: the greater the interaction between the polymer and Li-ions, the smaller the free energy difference between the inorganic and organic materials. The influence of the interface is felt over a range of approximately 1.5 nm, after which the behavior of Li-ion in the polymer is comparable to that in the bulk. Near the interface, Li-ion transport primarily occurs parallel to the interfacial plane, and ion mobility is considerably slower near the interface itself, consistent with the reduced segmental mobility of the polymer in the vicinity of the ceramic material. These findings provide insights into ionic complexation and transport mechanisms in composite systems, and will help improve design of improved solid electrolyte systems.

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

An automated protocol to construct flexibility parameters for classical forcefields: applications to metal-organic frameworks

In this work, forcefield flexibility parameters were constructed and validated for more than 100 metal-organic frameworks (MOFs). We used atom typing to identify bond types, angle types, and dihedral types associated with bond stretches, angle bends, dihedral torsions, and other flexibility interactions. Our work used Manz's angle-bending and dihedral-torsion model potentials. For a crystal structure containing N atoms in its unit cell, the number of independent flexibility interactions is 3(N atoms - 1). Because the number of bonds, angles, and dihedrals is normally much larger than 3(N atoms - 1), these internal coordinates are redundant. To reduce (but not eliminate) this redundancy, our protocol prunes dihedral types in a way that preserves symmetry equivalency. Next, each dihedral type is classified as non-rotatable, hindered, rotatable, or linear. We introduce a smart selection method that identifies which particular torsion modes are important for each rotatable dihedral type. Then, we computed the force constants for all flexibility interactions together via LASSO regression (i.e., regularized linear least-squares fitting) of the training dataset. LASSO automatically identifies and removes unimportant forcefield interactions. For each MOF, the reference dataset was quantum-mechanically-computed in VASP via DFT with dispersion and included: (i) finite-displacement calculations along every independent atom translation mode, (ii) geometries randomly sampled via ab initio molecular dynamics (AIMD), (iii) the optimized ground-state geometry using experimental lattice parameters, and (iv) rigid torsion scans for each rotatable dihedral type. After training, the flexibility model was validated across geometries that were not part of the training dataset. For each MOF, we computed the goodness of fit (R-squared value) and the root-mean-squared error (RMSE) separately for the training and validation datasets. We compared flexibility models with and without bond-bond cross terms. Even without cross terms, the model yielded R-squared values of 0.910 (avg across all MOFs) ± 0.018 (st. dev.) for atom-in-material forces in the validation datasets. Our SAVESTEPS protocol should find widespread applications to parameterize flexible forcefields for material datasets. We performed molecular dynamics simulations using these flexibility parameters to compute heat capacities and thermal expansion coefficients for two MOFs.

Elucidating the transport of water and ions in the nanochannel of covalent organic frameworks by molecular dynamics

Inspired by biological channels, achieving precise separation of ion/water and ion/ion requires finely tuned pore sizes at molecular dimensions and deliberate exposure of charged groups. Covalent organic frameworks (COFs), a class of porous crystalline materials, offer well-defined nanoscale pores and diverse structures, making them excellent candidates for nanofluidic channels that facilitate ion and water transport. In this study, we perform molecular simulations to investigate the structure and kinetics of water and ions confined within the typical COFs with varied exposure of charged groups. The COFs exhibit vertically arrayed nanochannels, enabling diffusion coefficients of water molecules within COFs to remain within the same order of magnitude as in the bulk. The motion of water molecules manifests in two distinct modes, creating a mobile hydration layer around acid groups. The ion diffusion within COFs displays a notable disparity between monovalent (M+) and divalent (M2+) cations. As a result, the selectivity of M+/M2+ can exceed 100, while differentiation among M+ is less pronounced. In addition, our simulations indicate a high rejection (R > 98%) in COFs, indicating their potential as ideal materials for desalination. The chemical flexibility of COFs indicates that would hold significant promise as candidates for advanced artificial ion channels and separation membranes.

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.

Design of Mixed-Matrix MOF Membranes with Asymmetric Filler Density and Intrinsic MOF/Polymer Compatibility for Enhanced Molecular Sieving

The separation of high-value-added chemicals from organic solvents is important for many industries. Membrane-based nanofiltration offers a more energy-efficient separation than the conventional thermal processes. Conceivably, mixed-matrix membranes (MMMs), encompassing metal-organic frameworks (MOFs) as fillers, are poised to promote selective separation via molecular sieving, synergistically combining polymers flexibility and fine-tuned porosity of MOFs. Nevertheless, conventional direct mixing of MOFs with polymer solutions results in underutilization of the MOF fillers owing to their uniform cross-sectional distribution. Therefore, in this work, a multizoning technique is proposed to produce MMMs with an asymmetric-filler density, in which the MOF fillers are distributed only on the surface of the membrane, and a seamless interface at the nanoscale. The design strategy demonstrates five times higher MOF surface coverage, which results in a solvent permeance five times higher than that of conventional MMMs while maintaining high selectivity. Practically, MOFs are paired with polymers of similar chemical nature to enhance their adhesion without the need for surface modification. The approach offers permanently accessible MOF porosity, which translates to effective molecular sieving, as exemplified by the polybenzimidazole and Zr-BI-fcu-MOF system. The findings pave the way for the development of composite materials with a seamless interface.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Ion-Concentration-Hopping Heterolayer Gel for Ultrahigh Gradient Energy Conversion

Conventional solid ion channel systems relying on single one- or two-dimensional confined nanochannels enabled selective and ultrafast convective ion transport. However, due to intrinsic solid channel stacking, these systems often face pore-pore polarization and ion concentration blockage, thereby restricting their efficiency in macroscale ion transport. Here, we constructed a soft heterolayer-gel system that integrated an ion-selective hydrogel layer with a water-barrier organogel layer, achieving ultrahigh cation selectivity and flux and effectively providing high-efficiency gradient energy conversion on a macroscale order of magnitude. Specifically, the hydrogel layer featured an unconfined 3D network, where the fluctuations of highly hydrated polyelectrolyte chains driven by thermal dynamics enhanced cation selectivity and mitigated transfer energy barriers. Such chain fluctuation mechanisms facilitated ion-cluster internal transmission, thereby enhancing ion concentration hopping for more efficient ion-selective transport. Compared to the existing rigid nanochannel-based gradient energy conversion systems, such a heterogel-based power generator exhibited a record power density of 192.90 and 1.07 W/m2 at the square micrometer scale and square centimeter scale, respectively (under a 500-fold artificial solution). We anticipate that such heterolayer gels would be a promising candidate for energy separation and storage applications.

Modeling Zinc Complexes Using Neural Networks

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Predicted Separation of Acid Gases from Gas Mixtures by Functionalized Porous Aromatic Frameworks

The selective adsorption of target acid gas molecules from binary gas mixtures by porous aromatic frameworks (PAFs) with two identical functional groups per aromatic ring (PAF-R2) was computationally investigated using grand canonical Monte Carlo simulations. PAF-R2 adsorption was considered for three binary mixtures of small molecular concentrations of acid gas and abundant nitrogen gas (CO2/N2, SO2/N2, and H2S/N2). The results indicate that additional functional groups enhance acid gas loadings and selectivity, compared with pristine PAF and single-functionalized PAFs. Low pressures yield linearly increasing gas loadings and constant selectivity, while high pressures yield much higher adsorption and selectivity. In particular, SO2 loading and selectivity under high pressures are heavily influenced by the PAF's maximum adsorption limit, which can be linked back to the functional groups and their configuration. In summary, PAF-(3,5)-(COOH)2 (nomenclature of PAFs is provided in the Appendix in the Supporting Information) and many other PAF with the same two electron-withdrawing groups are predicted to have great acid gas adsorption and selectivity from gas mixtures, while PAF-(3,5)-(OH)2 (one of PAFs with two identical electron-donating groups) is predicted to have good adsorption and selectivity, especially under elevated pressures. The results of this work can provide insights into various types of PAFs with great selective adsorption ability and their corresponding conditions. The simulation procedures and results may inspire the exploration and screening of other types of PAFs or porous materials, for acid gas absorption.

Computational screening and functional tuning of chemically stable metal organic frameworks for I2/CH3I capture in humid environments

High chemical stability is of vital significance in rendering metal organic frameworks (MOFs) as promising adsorbents for capturing leaked radioactive nuclides, under real nuclear industrial conditions with high humidity. In this work, grand canonical Monte Carlo (GCMC) and density functional theory (DFT) methods have been employed to systematically evaluate I2/CH3I capture performances of 21 experimentally confirmed chemically stable MOFs in humid environments. Favorable structural factors and the influence of hydrophilicity for iodine capture were unveiled. Subsequently, the top-performing MIL-53-Al with flexible tunability was functionalized with different functional groups to achieve the better adsorption performance. It has been revealed that the adsorption affinity and pore volume were two major factors altered by the functionalization of polar functional groups, which collectively influenced the iodine adsorption properties. In general, this work has screened the chemically stable high-performance MOF iodine adsorbents and provided comprehensive insights into the key factors affecting I2/CH3I uptake and separation in humid environments.

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.

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g-1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Level-Shifted Embedded Cluster Method for Modeling the Chemistry of Metal Oxides

The embedded cluster method has been used extensively in the study of the chemical and physical properties of metal oxides. This method has been a popular tool due to its relatively high accuracy and low computational cost. An even more promising option may entail integrating the embedded cluster method with the combined quantum mechanical and molecular mechanical (QM/MM) approach, thereby enabling further consideration of interactions within the entire system for superior results. We aim to accurately model the chemistry of metal oxides using this combined scheme. Here, using the prototypical MgO(100) surface as a test system, with Mg9O14 as the cluster in the quantum mechanical region, we show that the embedded cluster with untailored boundary effective core potentials (ECPs) can have frontier orbital energy levels that substantially deviate from the quantum mechanical reference results. This occurs even when Mg9O9, which retains the stoichiometry of MgO, is used as the cluster in the quantum mechanical region. As a result, the chemical properties of the embedded cluster models differ from those of the quantum mechanical reference model. To address this issue, we propose a new variant of the embedded cluster method called the level-shifted embedded cluster (LSEC) method, which allows the energy levels to be shifted to match the reference levels by tuning the boundary ECPs. Our validation calculations on the adsorption of various adsorbates with different properties on the MgO(100) surface show that the overall performance of QM/MM with the LSEC method is excellent for the adsorption energies, geometries, and charge properties. The excellent performance holds for both the nonstoichiometric and stoichiometric clusters (i.e., Mg9O14 and Mg9O9, respectively), demonstrating the robustness of the LSEC method. We expect that the LSEC method can be combined with QM/MM or used separately for future chemical studies of metal oxides and other ionically bonded systems.

Atomic Charge Dependency of Spiropyran/Merocyanine Adsorption as a Precursor to Surface Isomerization Reactions

This computational study investigates the adsorption of various spiropyran and merocyanine isomers on a NaCl substrate using a combination of density functional theory (DFT) and molecular mechanics (MM) calculations. Four different charge methods were used to determine the partial atomic charges for the adsorbate molecules, including Mulliken population analysis and three electrostatic potential (ESP) methods (Merz-Kollman, ChelpG, and Hu-Lu-Yang), while three different force fields (AMBER 3, CHARMM 27, and MM+) were employed for the MM calculations. The results show that the various DFT charge methods produced similar outcomes for the molecules' partial atomic charges, with some exceptions for individual atoms and methods. Additionally, it was found that the ESP charge methods were more sensitive to the conformer orientation than the Mulliken approach. The adsorption behavior of merocyanine conformers with the central bond in trans orientation (T-conformers) was similar for various configurations, with the molecule adsorbing mostly flat with its aromatic rings almost parallel to the substrate. However, C-conformers (with their central bond in cis orientation) and spiropyran isomers exhibited inconsistent adsorption behavior, mostly because only some of the aromatic rings contributed to the adsorption behavior. Due to additional van der Waals interactions of more aromatic rings, the adsorption energies for T-conformers are consistently 0.2-0.3 eV higher than for C-conformers and for spiropyran. The study found that the adsorption geometries and energies of stable T-conformers were independent of the partial atomic charge scheme and force field used, and C-conformers show parameter-dependent behavior upon adsorption, leading to metastable configurations. These findings indicate viable pathways during the spiropyran-merocyanine isomerization reactions. Therefore, the results provide initial insights into the possibility of switching spiropyran isomers into merocyanine isomers and vice versa after adsorption onto substrates.

Understanding Carbon Monoxide Binding and Interactions in M-MOF-74 (M = Mg, Mn, Ni, Zn)

Small molecules may adsorb strongly in metal-organic frameworks (MOFs) through interactions with under-coordinated open metal sites (OMS) that often exist within these structures. Among adsorbates, CO is attractive to study both for its relevance in energy-related applications and for its ability to engage in both σ-donation and π-backbonding interactions with the OMS in MOFs. Concomitant with strong adsorption, structural changes arise due to modifications of the electronic structure of both the adsorbate and adsorbent. These structural changes affect the separation performance of materials, and accurately capturing these changes and the resulting energetics is critical for accurate predictive modeling of adsorption. Traditional approaches to modeling using classical force fields typically do not capture or account for changes at the electronic level. To characterize the structural and energetic effects of the local structural changes, we employed density functional theory (DFT) to study CO adsorption in M-MOF-74s. M-MOF-74s feature OMS at which CO is known to adsorb strongly and can be synthesized with a variety of divalent metal cations with distinct performance in adsorption. We considered M-MOF-74s with a range of metals of varied d-band occupations (Mg (3d0), Mn (3d5), Ni (3d8), and Zn (3d10)) with various structural constraints ranging from geometrically constrained adsorbent and adsorbate ions to fully optimized geometries to deconvolute the relative contributions of various structural effects to the adsorption energetics and binding distances observed. Our data indicate that the most significant structural changes during adsorption correlate with the greatest π-backbonding behaviors and commensurately result in a sizable binding energy change observed for CO adsorption. The insights built from this work are relevant to two longstanding research challenges within the MOF community: rational design of materials for separations and the design of force fields capable of accurately modeling adsorption.

Enhanced CO2 capture in partially interpenetrated MOFs: Synergistic effects from functional group, pore size, and steric-hindrance

Excessive CO2 emissions have contributed to global environmental issues, driving the development of CO2 capture adsorbents. Among various candidates, metal-organic frameworks (MOFs) are considered the most promising due to their unique microporous structure. Herein, a series of partially interpenetrated MOFs named UPC-XX were built to investigate the continuous enhancement in CO2 capture performance via synergistic effects from functional group, pore size, and steric-hindrance using theoretical calculations. It's showed that the introduction of functional groups improved the structure polarity and created more adsorption sites, thus, enhanced CO2 capture capacity. The pore size modification augments the exposure of adsorption sites to mitigate the negative impact of pore space and surface area reduction caused by the introduction of functional groups, thereby further increasing the CO2 capture capacity. The steric-hindrance effect optimized the adsorption sites distribution, which hasn't been considered in the previous two regulation strategies, thus, further increased the CO2 capture capacity. The results underscore UPC-MOFs as outstanding adsorbent materials, among the UPC-MOFs, UPC-OSO3-steric exhibited the highest CO2 capture capacity of 12.69 mmol/g with selectivities of 1142.41 (CO2 over N2) and 507.42 (CO2 over CH4) at 1.0 bar, 298 K. And the synergistic effect mechanisms of functional group, structure size, and steric hindrance were elucidated through theoretical calculations analyzing pore characteristics, gas distribution, isosteric heat, and van der Waals/Coulomb interactions.

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

Microscopic insight into the shaping of MOFs and its impact on CO2 capture performance

The traditional synthesis method produces microcrystalline powdered MOFs, which prevents direct implementation in real-world applications which demand strict control of shape, morphology and physical properties. Therefore, shaping of MOFs via the use of binders is of paramount interest for their practical use in gas adsorption/separation, catalysis, sensors, etc. However, so far, the binders have been mostly selected by trial-and-error without anticipating the adhesion between the MOF and binder components to ensure the processability of homogeneous and mechanically stable shaped MOFs and the impact of the shaping on the intrinsic properties of the MOFs has been overlooked. Herein, we deliver a first systematic multiscale computational exploration of MOF/binder composites by selecting CALF-20, a prototypical MOF for real application in the field of CO2 capture, and a series of binders that cover a rather broad spectrum of properties in terms of rigidity/flexibility, porosity, and chemical functionality. The adhesion between the two components and hence the effectiveness of the shaping as well as the impact of the overall porosity of the CALF-20/binder on the CO2/N2 selectivity, CO2 sorption capacity and kinetics was analyzed. Shaping of CALF-20 by carboxymethyl cellulose was predicted to enable a fair compromise between excellent adhesion between the two components, whilst maintaining high CO2/N2 selectivity, large CO2 uptake and CO2 transport as fast as in the CALF-20. This multiscale computational tool paves the way towards the selection of an appropriate binder to achieve an optimum shaping of a given MOF in terms of processability whilst maintaining its high level of performance.

In silico characterization of nanoparticles

Nanoparticles (NPs) make for intriguing heterogeneous catalysts due to their large active surface area and excellent and often size-dependent catalytic properties that emerge from a multitude of chemically different surface reaction sites. NP catalysts are, in principle, also highly tunable: even small changes to the NP size or surface facet composition, doping with heteroatoms, or changes of the supporting material can significantly alter their physicochemical properties. Because synthesis of size- and shape-controlled NP catalysts is challenging, the ability to computationally predict the most favorable NP structures for a catalytic reaction of interest is an in-demand skill that can help accelerate and streamline the material optimization process. Fundamentally, simulations of NP model systems present unique challenges to computational scientists. Not only must considerable methodological hurdles be overcome in performing calculations with hundreds to thousands of atoms while retaining appropriate accuracy to be able to probe the desired properties. Also, the data generated by simulations of NPs are typically more complex than data from simulations of, for example, single crystal surface models, and therefore often require different data analysis strategies. To this end, the present work aims to review analytical methods and data analysis strategies that have proven useful in extracting thermodynamic trends from NP simulations.

Closer Look at Adsorption of Sarin and Simulants on Metal-Organic Frameworks

The development of effective protection against exposure to chemical warfare agents (CWAs), such as sarin, relies on studies of its adsorption on the capturing materials and seeking candidates capable of adsorbing large amounts of sarin gas. Many metal-organic frameworks (MOFs) are promising materials for the effective capture and degradation of sarin and simulant substances. Among the simulants capable of mimicking thermodynamic properties of the agent, not all of them have been investigated on the ability to act similarly in the adsorption process, in particular, whether the agent and a simulant have similar mechanisms of binding to the MOF surface. Molecular simulation studies not only provide a safe way to investigate the aforementioned processes but can also help reveal the mechanisms of interactions between the adsorbents and the adsorbing compounds at the molecular level. We performed Monte Carlo simulations of the adsorption of sarin and three simulants, dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), and diisopropyl fluorophosphate (DIFP), on selected MOFs that have previously shown strong capabilities to adsorb sarin. On the basis of the calculated adsorption isotherms, enthalpy of adsorption, and radial distribution functions, we revealed common mechanisms among the particularly efficient adsorbents as well as the ability of simulants to mimic them. The findings can help in selecting a suitable simulant compound to study CWA adsorption on MOFs and guide further synthesis of efficient MOFs for the capture of organophosphorus compounds.

Spacer-Engineered Ionic Channels in Covalent Organic Framework Membranes toward Ultrafast Proton Transport

Side-chain engineering of covalent organic frameworks as advanced ion conductors is a critical issue to be explored. Herein, ionic covalent organic framework membranes (iCOFMs) with spacer-engineered ionic channel are de novo designed and prepared. The ionic channels are decorated with side chains comprising spacers having different carbon chain lengths and the -SO₃ H groups at the end. Attributed to the synergistic contribution from the spacers and the -SO₃ H groups, the iCOFM with moderate-length spacer exhibit the highest through-plane proton conductivity of 889 mS cm^-1 at 90 °C.

What is the Optimal Dipole Moment for Nonpolarizable Models of Liquids?

In classical nonpolarizable models, electrostatic interactions are usually described by assigning fixed partial charges to interaction sites. Despite the multitude of methods and theories proposed over the years for partial charge assignment, a fundamental question remains─what is the correct degree of polarization that a fixed-charge model should possess to provide the best balance of interactions (including induction effects) and yield the best description of the potential energy surface of a liquid phase? We address this question by approaching it from two separate and independent viewpoints: the QUantum mechanical BEspoke (QUBE) approach, which assigns bespoke force field parameters for individual molecules from ab initio calculations with minimal empirical fitting, and the Polarization-Consistent Approach (PolCA) force field, based on empirical fitting of force field parameters with an emphasis on transferability by rigorously accounting for polarization effects in the parameterization process. We show that the two approaches yield consistent answers to the above question, namely, that the dipole moment of the model should be approximately halfway between those of the gas and the liquid phase. Crucially, however, the reference liquid-phase dipole needs to be estimated using methods that explicitly consider both mean-field and local contributions to polarization. In particular, continuum dielectric models are inadequate for this purpose because they cannot account for local effects and therefore significantly underestimate the degree of polarization of the molecule. These observations have profound consequences for the development, validation, and testing of nonpolarizable models.

Atomic charges in molecules defined by molecular real space partition into atomic subspaces

Atomic charge (AC), which is the charge distribution of a molecule, is an important property that is closely associated with structures, reactivities, and intra- and inter-molecular interactions among molecules. Several theoretical models or methods can be used to obtain the magnitudes of AC with different characteristics. These models can be classified into fuzzy-atoms models and models partitioning a molecule into individual atoms with sharp boundaries. The first category includes Mulliken, natural population analysis (NPA), Hirshfeld, Merz-Kollman-Singh (MK), CHELPG, the electronegativity equalization method (EEM), the atom-bond electronegativity equalization method (ABEEM), and atomic polar tensor (APT). The second category is derived from quantum chemical topology (QCT) and includes the quantum theory of atoms in molecules (QTAIM) and QCT analysis based on the potential acting on one electron in a molecule (PAEMQCT). Herein, after giving a bird's-eye view of the population methods of the first category, we specifically describe some features of the second category. We only present the basic framework of QCT for obtaining ACs from QTAIM and PAEMQCT and show their important characteristics. QCT establishes the basis of the following chemical concept: a molecule is spatially partitioned into individual atoms with sharp boundaries. The ACs from QTAIM are close to the atomic valence in chemistry, and ACs from PAEMQCT may be practically suitable for modeling intra- and inter-molecular interactions.

Designing Nanofluidic Diode from a Hybrid-Bilayer Covalent Organic Framework: Molecular Simulation Investigation

Nanofluidic diodes are potentially useful in many important applications such as sensing, electronics, and energy conversion. However, the manufacturing of controllable nanopores for nanofluidic diodes is technically challenging. Herein, a nanofluidic diode is designed from a highly programmatic covalent organic framework (COF). Through molecular simulation, remarkable diode behavior is observed in a hybrid-bilayer COF but not in its constituent single-layer COFs. The rectification effect of ion current in the hybrid-bilayer COF is attributed to an asymmetric electrostatic potential across the COF nanopore. Furthermore, a synergistic effect of counterion is unraveled in the hybrid-bilayer COF, and the presence of counterion is found to reduce the entry barrier and facilitate ion transport. The performance of the hybrid-bilayer COF as a nanofluidic diode is comprehensively investigated by varying salt concentration, layer number, interlayer spacing, and slipping. This proof-of-concept simulation study demonstrates the feasibility of the hybrid-bilayer COF as a nanofluidic diode and the finding may stimulate the development of new nanofluidic platforms.

Enhance Hydrogen Isotopes Separation by Alkali Earth Metal Dopant in Metal-Organic Framework

Kinetic quantum sieving (KQS) based on pore size and chemical affinity quantum sieving (CAQS) based on adsorption site are two routes of porous materials to separate hydrogen isotope mixtures. Alkali earth metals (Be, Mg, and Ca) were doped into UiO-67 to explore whether these metal sites can promote H₂/D₂ separation. Based on the zero-point energy and adsorption enthalpy calculated by density functional theory calculations, the Be dopant shows better H₂/D₂ separation performance than other alkali earth metal dopants and unsaturated metal sites in metal-organic frameworks based on CAQS. Orbital interaction strongly relates to the chemical affinity and further influences the D₂/H₂ selectivity. Moreover, the predicted D₂/H₂ selectivity of Be-doped sites (49.4) at 77 K is even larger than the best experimental result (26). Finally, the different dynamic behaviors of H₂ and D₂ on Be-doped UiO-67 indicate its strong H₂/D₂ separation performance via KQS.

Development of Nanoscale Graphene Oxide Models for the Adsorption of Biological Molecules

Graphene oxide (GO), a nanomaterial with promising applications that range from water purification to enzyme immobilization, is actively present in scientific research since its discovery. GO studies with computational methodologies such as molecular dynamics are frequently reported in the literature; however, the models used often rely on approximations, such as randomly placing functional groups and the use of generalized force fields. Therefore, it is important to develop new MD models that provide a more accurate description of GO structures and their interaction with an aqueous solvent and other adsorbate molecules. In this paper, we derived new force field non-bonded parameters from linear-scaling density functional theory calculations of nanoscale GO sheets with more than 10,000 atoms through an atoms-in-molecules (AIM) partitioning scheme. The resulting GAFF2-AIM force field, derived from the bonded terms of GAFF2 parameterization, reproduces the solvent structure reported in ab initio MD simulations better than the force field nowadays widely used in the literature. Additionally, we analyzed the effect of the ionic strength of the medium and of the C/O ratio on the distribution of charges surrounding the GO sheets. Finally, we simulated the adsorption of natural amino acid molecules to a GO sheet and estimated their free energy of binding, which compared very favorably to their respective experimental values, validating the force field presented in this work.

gmak: A Parameter-Space Mapping Strategy for Force-Field Calibration

In the context of classical molecular simulations, the accuracy of a force field is highly influenced by the values of the relevant simulation parameters. In this work, a parameter-space mapping (PSM) workflow is proposed to aid in the calibration of force-field parameters, based mainly on the following features: (i) regular-grid discretization of the search space; (ii) partial sampling of the search-space grid; (iii) training of surrogate models to predict the estimates of the target properties for nonsampled parameter sets; (iv) post hoc interpretation of the results in terms of multiobjective optimization concepts; (v) attenuation of statistical errors achieved via empiric extension of the duration of the simulations; (vi) iterative search-space translation according to a user-defined scalar objective function that measures the accuracy of the force field (e.g., the weighted root-mean-square deviation of the target properties relative to the reference data). This combination of features results in a hybrid of a single- and a multiobjective optimization strategy, allowing for the approximate determination of both a local minimum of the chosen objective function and its neighboring Pareto efficient points. The PSM workflow is implemented in the extensible Python program gmak, which is made available in the Git repository at http://github.com/mssm-labmmol/gmak. Using this implementation, the PSM workflow was tested in a proof-of-concept fashion in the recalibration of the Lennard-Jones parameters of the 3-point Optimal Point Charge (OPC3) water model for compatibility with the GROMOS treatment of nonbonded interactions. The recalibrated model reproduces typical pure-liquid properties with an accuracy similar to the original OPC3 model and represents a significant improvement relative to the Simple Point Charge (SPC) model, which is the official recommendation for simulations using GROMOS force fields.

Computational Characterization of Zr-Oxide MOFs for Adsorption Applications

Zr-oxide secondary building units construct metal-organic framework (MOF) materials with excellent gas adsorption properties and high mechanical, thermal, and chemical stability. These attributes have led Zr-oxide MOFs to be well-recognized for a wide range of applications, including gas storage and separation, catalysis, as well as healthcare domain. Here, we report structure search methods within the Cambridge Structural Database (CSD) to create a curated subset of 102 Zr-oxide MOFs synthesized to date, bringing a unique record for all researchers working in this area. For the identified structures, we manually corrected the proton topology of hydroxyl and water molecules on the Zr-oxide nodes and characterized their textural properties, Brunauer-Emmett-Teller (BET) area, and topology. Importantly, we performed systematic periodic density functional theory (DFT) calculations comparing 25 different combinations of basis sets and functionals to calculate framework partial atomic charges for use in gas adsorption simulations. Through experimental verification of CO₂ adsorption in selected Zr-oxide MOFs, we demonstrate the sensitivity of CO₂ adsorption predictions at the Henry's regime to the choice of the DFT method for partial charge calculations. We characterized Zr-MOFs for their CO₂ adsorption performance via high-throughput grand canonical Monte Carlo (GCMC) simulations and revealed how the chemistry of the Zr-oxide node could have a significant impact on CO₂ uptake predictions. We found that the maximum CO₂ uptake is obtained for structures with the heat of adsorption values >25 kJ/mol and the largest cavity diameters of ca. 6-7 Å. Finally, we introduced augmented reality (AR) visualizations as a means to bring adsorption phenomena alive in porous adsorbents and to dynamically explore gas adsorption sites in MOFs.

Transferable Classical Force Field for Pure and Mixed Metal Halide Perovskites Parameterized from First-Principles

Many key features in photovoltaic perovskites occur in relatively long time scales and involve mixed compositions. This requires realistic but also numerically simple models. In this work we present a transferable classical force field to describe the mixed hybrid perovskite MAxFA1-xPb(BryI1-y)3 for variable composition (∀x, y ∈ [0, 1]). The model includes Lennard-Jones and Buckingham potentials to describe the interactions between the atoms of the inorganic lattice and the organic molecule, and the AMBER model to describe intramolecular atomic interactions. Most of the parameters of the force field have been obtained by means of a genetic algorithm previously developed to parametrize the CsPb(BrxI1-x)3 perovskite (Balestra et al. J. Mater. Chem. A. 2020, DOI: 10.1039/d0ta03200j). The algorithm finds the best parameter set that simultaneously fits the DFT energies obtained for several crystalline structures with moderate degrees of distortion with respect to the equilibrium configuration. The resulting model reproduces correctly the XRD patterns, the expansion of the lattice upon I/Br substitution, and the thermal expansion coefficients. We use the model to run classical molecular dynamics simulations with up to 8600 atoms and simulation times of up to 40 ns. From the simulations we have extracted the ion diffusion coefficient of the pure and mixed perovskites, presenting for the first time these values obtained by a fully dynamical method using a transferable model fitted to first-principles calculations. The values here reported can be considered as the theoretical upper limit, that is, without grain boundaries or other defects, for ion migration dynamics induced by halide vacancies in photovoltaic perovskite devices under operational conditions.

Iron(II) Phthalocyanine Adsorbed on Defective Graphenes: A Density Functional Study

The adsorptions of iron(II) phthalocyanine (FePc) on graphene and defective graphene were investigated systematically using density functional theory. Three types of graphene defects covering stone-wales (SW), single vacancy (SV), and double vacancy (DV) were taken into account, in which DV defects included DV(5-8-5), DV(555-777), and DV(5555-6-7777). The calculations of formation energies of defects showed that the SW defect has the lowest formation energy, and it was easier for DV defects to form compared with the SV defect. It is more difficult to rotate or move FePc on the surface of defective graphenes than on the surface of graphene due to bigger energy differences at different sites. Although the charge analysis indicated the charge transfers from graphene or defective graphene to FePc for all studied systems, the electron distributions of FePc on various defective graphenes were different. Especially for FePc@SV, the d xy orbital of Fe in the conduction band moved toward the Fermi level about 1 eV, and the d xz of Fe in the valence band for FePc@SV also moved toward the Fermi level compared with FePc@graphene and other FePc@defective graphenes. Between the planes of FePc and defective graphene, the electron accumulation occurs majorly in the position of the FePc molecular plane for FePc@SW, FePc@DV(5-8-5), and FePc@DV(5555-6-7777) as well as FePc@graphene. However, electrons were accumulated on the upper and lower surfaces of the FePc molecular plane for FePc@SV and FePc@DV(555-777). Thus, the electron distribution of FePc can be modulated by introducing the interfaces of different defective graphenes.

Computational Screening of Metal-Organic Frameworks for Ammonia Capture from H2/N2/NH3 Mixtures

The separation of ammonia from H₂/N₂/NH₃ mixtures is an important step in ammonia decomposition for hydrogen production and ammonia synthesis from H₂ and N₂ based nonaqueous technologies. Metal-organic frameworks (MOFs) are considered as potential materials for capturing ammonia. In the present work, high-throughput screening of 2932 Computation-Ready Experimental MOFs (CoRE MOFs) was carried out for ammonia capture from H₂/N₂/NH₃ mixtures by Grand Canonical Monte Carlo (GCMC) simulations. It was found that the high-performing MOFs are characterized by tube-like channels, moderate LCD (largest cavity diameter) (4-7.5 Å), and high Q _st ⁰(NH₃) (the isosteric heat of NH₃ adsorption) (>45 kJ/mol). MOFs with high NH₃ adsorption capacity often feature moderate surface area, while the surface area of MOFs with high NH₃ selectivity is relatively lower, which limits the NH₃ adsorption capacity. Q _st ⁰ and the Henry's constant (K _H ) are two energy descriptors describing the interactions between adsorbents and adsorbates. The former has a stronger correlation with the adsorption selectivity, while the latter has a stronger correlation with the adsorption capacity. By analyzing the molecular density distribution of adsorbates in high-performing MOFs, it was found that unsaturated coordinated metal sites provide the main functional binding sites for NH₃. Most MOFs with high NH₃ selectivity have multiple different metal nodes or other atoms except C, O, and H, such as N and P. Multiple metal nodes and nonmetallic atoms provide more functional binding sites. Finally, the adsorption behavior with various concentrations of gas mixtures was examined to verify the universality of the screening calculations, and the effect of framework flexibility on adsorption performance was explored.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH₃I); as a primary representative of organic iodides, CH₃¹²⁹I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH₃I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

Interactions of Water with Pristine and Defective MoS2

Molybdenum disulfide (MoS₂) is a lamellar solid lubricant often used in aerospace applications because of its extremely low friction coefficient (∼0.01) in inert environments. The lubrication performance of MoS₂ is significantly impaired by exposure to even small amounts of water and oxygen, and the mechanisms behind this remain poorly understood. We use density functional theory calculations to study the binding of water on MoS₂ sheets with and without defects. In general, we find that pristine MoS₂ is slightly hydrophilic but that defects greatly increase the binding affinity for water. Intercalated water disrupts the crystal structure of bulk MoS₂ due to the limited space between lamellae (∼3.4 Å), and this leads to generally unfavorable adsorption, except in the cases where water molecules are located on the sites of sulfur vacancies. We also find that water adsorption is more favorable directly below a surface layer of MoS₂ compared to in the bulk.

High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO2/H2 separation

It is challenging to identify the optimal metal-organic framework (MOF) adsorbents for gas adsorption and membrane-based separation from the large-scale material databases. The high-throughput computational screening (HTCS) method was adopted to discover the optimal materials for CO₂/H₂ separation from thousands of MOFs. First, a hierarchical strategy was used to select 1092 MOFs from 13 512 MOFs, and their adsorption capacity towards the equimolar CO₂/H₂ mixture at 298 K and 10 bar was further calculated using the grand canonical Monte Carlo (GCMC) simulations. The results show that those MOFs with lvtb topology and organic linker 1,2,4,5-tetrazine are conducive to exhibiting high performance CO₂/H₂ adsorption separation among top-100 MOFs with high performance. The MOFs with pore limited diameter (PLD), largest cavity diameter (LCD), gravimetrical surface area (GSA), and void fraction in the range of 4-12 Å, 5-12 Å, 5500-6500 m² g^-1 and 0.80-0.85, respectively, have high adsorption capacity towards CO₂. Second, the dynamic adsorption properties of the top-4 MOFs were simulated by the breakthrough curves of the binary (CO₂/H₂) and quinary (CO₂/H₂/CH₄/CO/N₂) mixtures in the fixed adsorption bed. MOF-4641 exhibits a high breakthrough time of 130 for the quinary mixture. Finally, the adsorption mechanism of CO₂ in the top-4 MOFs was investigated by the radial distribution function (RDF), the mass center probability density distribution, etc. The atomic insights from HTCS and breakthrough curve predictions in this work will be helpful in developing novel porous materials and obtaining superior CO₂ separation performance.

From computational high-throughput screenings to the lab: taking metal-organic frameworks out of the computer

Metal-organic frameworks (MOFs) are one of the most researched designer materials today, as their high tunability offers scientists a wide space to imagine all kinds of possible structures. Their uniquely flexible customisability spurred the creation of hypothetical datasets and the syntheses of more than 100 000 MOFs officially reported in the Cambridge Structural Database. To scan such large numbers of MOFs, computational high-throughput screenings (HTS) have become the customary method to identify the most promising structure for a given application, and/or to spot useful structure-property relationships. However, despite all these data-mining efforts, only a fraction of HTS studies have identified synthesisable top-performing MOFs that were then further investigated in the lab. In this perspective, we review these specific cases and suggest possible steps to push future HTS more systematically towards synthesisable structures.

Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO2 Capture

SO₂ presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal-organic frameworks (MOFs), cost-effective separation processes for SO₂ emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO₂/CH₄, SO₂/CO₂, and SO₂/N₂ separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO₂/CH₄ selectivities of 62.4-16899.7, SO₂ working capacities of 0.3-20.1 mol/kg, and SO₂ regenerabilities of 5.8-98.5%. Regarding SO₂/CO₂ separation, they possess SO₂/CO₂ selectivities of 13.3-367.2, SO₂ working capacities of 0.1-17.7 mol/kg, and SO₂ regenerabilities of 1.9-98.2%. For the SO₂/N₂ separation, their SO₂/N₂ selectivities, SO₂ working capacities, and SO₂ regenerabilities span the ranges of 137.9-67,338.9, 0.4-20.6 mol/kg, and 7.0-98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.

DFT Investigations of KTiOPO4Mx (M = K, Na, Li) Anodes for Alkali-Ion Battery

Properties of KTiOPO4M x (M = K, Na, and Li, x = 0.000 to 1.000) as anodes for potassium-ion battery (PIB), sodium-ion battery (SIB), and lithium-ion battery (LIB) are investigated by density functional theory (DFT) calculations. Our work reveals that electrochemical performance of KTiOPO4 as an anode for PIB is superior to that for SIB and LIB, in terms of average voltage and ion diffusion kinetics. The ab initio molecular dynamics (AIMD) simulations indicate that KTiOPO4M x anode exhibits high structural stability, and alkali ion intercalation contributes to accelerate ion diffusion during charging process. Particularly, the low activation energy 0.406 eV of K migration on surface KTP(210) obtained by the climbing-image nudged elastic band (Cl-NEB) method, which suggests a high-rate capability. The systematical comparison of the performance of KTiOPO4 as an anode for PIB, SIB, and LIB on the theoretical perspective clarifies that a large channel is not always promising for small radius ion intercalation and diffusion.

Designing stable binary endohedral fullerene lattices

Nanoparticle lattices and endohedral fullerenes have both been identified as potential building blocks for future electronic, magnetic and optical devices; here it is proposed that it could be possible to combine those concepts and design stable nanoparticle lattices composed from binary collections of endohedral fullerenes. The inclusion of an atom, for example Ca or F, within a fullerene cage is known to be accompanied by a redistribution of surface charge, whereby the cage can acquire either a negative (Ca) or positive (F) charge. From calculations involving a combination of van der Waals and many-body electrostatic interactions, it is predicted that certain binary combinations, for example a metal (A) and a halogen (B), could result in the formation of stable nanoparticle lattices with the familiar AB and AB₂ stoichiometries. Much of the stability is due to Coulomb interactions, however, charge-induced and van der Waals interactions, which always enhance stability, are found to extend the range of charge on a cage over which lattices are stable. Some lattice types are shown to be three or four times more stable than an equivalent neutral C₆₀ structure. An extension of the calculations to the fabrication of structures involving endohedral C₈₄ is also discussed.

Combined DFT and Molecular Mechanics Modeling of the Adsorption of Semiconducting Molecules on an Ionic Substrate: PTCDA and CuPc on NaCl

Experimental results suggest that molecular geometry and energies can be influenced by the presence of thin film substrates as well as surrounding molecules. It is imperative that computational models take this influence into account. The accurate computational modeling of these molecules is an efficient way of carrying out chemistry calculations and reinforcing experimental findings. In our study, density functional theory (DFT) and molecular mechanics (MM) are used to model the configurations of the organic semiconducting materials, 3,4,9,10-perylene tetracarboxylic dianhydride, C₂₄H₈O₆ (PTCDA), and copper(II) phthalocyanine, C₃₂H₁₆CuN₈ (CuPc), as adsorbed on single- and double-layer NaCl substrates of various dimensions and charge settings. After a geometry and charge optimization of the molecules using DFT, the molecular geometries are optimized under different environments using computational calculations with specific force-field settings in HyperChem Professional 8.0(TM) software using MM. Energies and geometries of the molecules are then recorded, and our data are compared to experimental results of similar systems. We find that, with the appropriate choice of substrate properties, the calculated molecular configurations directly reflect those found experimentally. Our results support the idea that this method of simulation can produce reliable models in the field of physical chemistry.