Accelerating Discovery of Mechanically Stable Metal-Organic Frameworks for Vinylidene Fluoride Storage by Active Learning.

Metal-organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, a handful of MOFs have been explored for the storage of toxic fluorinated gases (Keasler et al. Science, 2023, 381, 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated. In this work, we apply active learning (AL) to accelerate the discovery of hypothetical MOFs (hMOFs) that can efficiently store a specific fluorinated gas, namely, vinylidene fluoride (VDF). First, a force field was developed for VDF and utilized to predict the working capacities (ΔN) of VDF in an initial data set of 4502 MOFs from the computation-ready experimental MOF (CoRE-MOF) database that successfully underwent featurization and grand-canonical Monte Carlo simulations. Next, the initial data set was diversified by Greedy sampling in an unexplored sample space of 119,387 hMOFs from the ab initio REPEAT charge MOF (ARC-MOF) database. A budget of 10,000 samples (i.e., <10% of total ARC-MOFs) was selected to train a random forest model. Then, ΔN in the unlabeled ARC-MOFs were predicted and top-performing ones were validated by simulations. Integrating with the stability requirement, mechanically stable ARC-MOFs were finally identified, along with high ΔN. Furthermore, by Pareto-Frontier analysis, we revealed that long linear linkers can enhance ΔN, while bulkier multiphenyl linkers or interpenetrated frameworks improve mechanical strength. From this work, we efficiently discover top-performing MOFs for VDF storage by AL and also demonstrate the importance of integrating stability to identify stable promising MOFs for a practical application.

Nanoscopic Insights into the Collapse of Metal-Organic Frameworks during Solvent Evacuation: Molecular Simulation Investigation.

Many metal-organic frameworks (MOFs) undergo structural collapse upon solvent evacuation during activation, which is attributed to the capillary force generated by the solvent. However, little effort has been devoted to unveiling the nature of such a force. Herein, we employ molecular dynamics (MD) simulations to investigate the evacuation of different solvents in two MOFs (MOF-5 and UMCM-9). The contractive stress induced by solvent evacuation is quantified and unraveled to positively correlate with the surface tension of the solvent. Moreover, the mechanical strength (or amorphization) of the MOF is calculated using reactive MD simulations. By comparing the contractive stress with the amorphization stress, for the first time, we predict the likelihood of collapse of MOFs during activation by different solvents, which agrees well with the experiments. The methodology developed provides nanoscopic insights into the activation process; it can assist in avoiding structural collapse by judiciously selecting a proper solvent for activation or by modifying a framework.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

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.

Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning.

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test. First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among ≈280 000 candidates, ≈130 000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications.

Classifying and Predicting the Thermal Expansion Properties of Metal-Organic Frameworks: A Data-Driven Approach.

Metal-organic frameworks (MOFs) are versatile materials for a wide variety of potential applications. Tunable thermal expansion properties promote the application of MOFs in thermally sensitive composite materials; however, they are currently available only in a handful of structures. Herein, we report the first data set for thermal expansion properties of 33,131 diverse MOFs generated from molecular simulations and subsequently develop machine learning (ML) models to (1) classify different thermal expansion behaviors and (2) predict volumetric thermal expansion coefficients (αV). The random forest model trained on hybrid descriptors combining geometric, chemical, and topological features exhibits the best performance among different ML models. Based on feature importance analysis, linker chemistry and topological arrangement are revealed to have a dominant impact on thermal expansion. Furthermore, we identify common building blocks in MOFs with exceptional thermal expansion properties. This data-driven study is the first of its kind, not only constructing a useful data set to facilitate future studies on this important topic but also providing design guidelines for advancing new MOFs with desired thermal expansion properties.

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.

Computational design of metal hydrides on a defective metal-organic framework HKUST-1 for ethylene dimerization.

Catalytic ethylene dimerization to 1-butene is a crucial reaction in the chemical industry, as 1-butene is used for the production of most common plastics (e.g., polyethylene). With well-defined tuneable structures and unsaturated active sites, defective metal-organic frameworks have recently emerged as potential catalysts for ethylene dimerization. Herein, we computationally design a series of metal hydrides on defective HKUST-1 namely H-M-DHKUST-1 (M: Co, Ni, Cu, Ru, Rh and Pd), and subsequently assess their catalytic activity for ethylene dimerization by density functional theory calculations. Due to the antiferromagnetic behavior of dimeric metal-based clusters, we comprehensively investigate all possible multiplicity states on H-M-DHKUST-1 and observe multiplicity crossing. The ground-state reaction barriers for four elementary steps (initiation, C-C coupling, ß-hydride elimination and 1-butene desorption) are rationalized and C-C coupling is revealed to be the rate-determining step on H-Co-, H-Ni-, H-Ru-, H-Rh- and H-Pd-DHKUST-1. The energy barrier for ß-hydride elimination is found to be the lowest on H-Ru- and H-Rh-DHKUST-1, attributed to the weak stability of agostic arrangement; however, the energy barrier for 1-butene desorption is the highest on H-Rh-DHKUST-1. Among the designed H-M-DHKUST-1, Co- and Ni-based ones are predicted to exhibit the best overall catalytic performance. The mechanistic insights from this study may facilitate the development of new MOFs toward efficient ethylene dimerization and other industrially important reactions.

Chiral Covalent Organic Cages: Structural Isomerism and Enantioselective Catalysis.

Covalent organic cages are a prominent class of discrete porous architectures; however, their structural isomerism remains relatively unexplored. Here, we demonstrate the structural isomerism of chiral covalent organic cages that renders distinct enantioselective catalytic properties. Imine condensations of tetra-topic 5,10-di(3,5-diformylphenyl)-5,10-dihydrophenazine and ditopic 1,2-cyclohexanediamine produce two chiral [4 + 8] organic cage isomers with totally different topologies and geometries that depend on the orientations of four tetraaldehyde units with respect to each other. One isomer (PN-1) has an unprecedented Johnson-type J26 structure, whereas another (PN-2) adopts a tetragonal prismatic structure. After the reduction of the imine linkages, the cages are transformed into two amine bond-linked isomers PN-1R and PN-2R. After binding to Ni(II) ions, both can serve as efficient catalysts for asymmetric Michael additions, whereas PN-2R affords obviously higher enantioselectivity and reactivity than PN-1R presumably because of its large cavity and open windows that can concentrate reactants for the reactions. Density-functional theory (DFT) calculations further confirm that the enantioselective catalytic performance varies depending on the isomer.

Manipulation of Cationic Group Density in Covalent Organic Framework Membranes for Efficient Anion Transport.

Anion exchange membranes with high anion conductivity are highly desired for electrochemical applications. Increasing ion exchange capacity is a straightforward approach to enhancing anion conductivity but faces a challenge in dimensional stability. Herein, we report the design and preparation of three kinds of isoreticular covalent organic framework (COF) membranes bearing tunable quaternary ammonium group densities as anion conductors. Therein, the cationic groups are integrated into the backbones by flexible ether-bonded alkyl side chains. The highly quaternary ammonium-group-functionalized building units endow COF membranes with abundant cationic groups homogeneously distributed in the ordered channels. The flexible side chains alleviate electrostatic repulsion and steric hindrance caused by large cationic groups, ensuring a tight interlayer stacking and multiple interactions. As a result, our COF membranes achieve a high ion exchange capacity and exceptional dimensional stability simultaneously. Furthermore, the effect of the ionic group density on the ion conductivity in rigid COF channels is systematically explored. Experiments and simulations reveal that the ionic group concentration and side chain mobility jointly determine the ion transport behavior, resulting in the abnormal phenomenon that the anion conductivity is not positively correlated to the ionic group density. The optimal COF membrane achieves the ever-reported highest hydroxide ion conductivity over 300 mS cm-1 at 80 °C and 100% RH. This study offers insightful guidelines on the rational design and preparation of high-performance anion conductors.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective.

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery.

Organic solvents are extensively utilized in industries as raw materials, reaction media, and cleaning agents. It is crucial to efficiently recover solvents for environmental protection and sustainable manufacturing. Recently, organic solvent nanofiltration (OSN) has emerged as an energy-efficient membrane technology for solvent recovery; however, current OSN membranes are largely fabricated by trial-and-error methods. In this study, for the first time, we develop a machine learning (ML) approach to design new thin-film composite membranes for solvent recovery. The monomers used in interfacial polymerization, along with membrane, solvent and solute properties, are featurized to train ML models via gradient boosting regression. The ML models demonstrate high accuracy in predicting OSN performance including solvent permeance and solute rejection. Subsequently, 167 new membranes are designed from 40 monomers and their OSN performance is predicted by the ML models for common solvents (methanol, acetone, dimethylformamide, and n-hexane). New top-performing membranes are identified with methanol permeance superior to that of existing membranes. Particularly, nitrogen-containing heterocyclic monomers are found to enhance microporosity and contribute to higher permeance. Finally, one new membrane is experimentally synthesized and tested to validate the ML predictions. Based on the chemical structures of monomers, the ML approach developed here provides a bottom-up strategy toward the rational design of new membranes for high-performance solvent recovery and many other technologically important applications.

Early detection of lung cancer in a real-world cohort via tumor-associated immune autoantibody and imaging combination.

Background: Efficient early detection methods for lung cancer can significantly decrease patient mortality. One promising approach is the use of tumor-associated autoantibodies (TAABs) as a diagnostic tool. In this study, the researchers aimed to evaluate the potential of seven TAABs in detecting lung cancer within a population undergoing routine health examinations. The results of this study could provide valuable insights into the utility of TAABs for lung cancer screening and diagnosis. Methods: In this study, the serum concentrations of specific antibodies were measured using enzyme-linked immunosorbent assay (ELISA) in a cohort of 15,430 subjects. The efficacy of both a 7-TAAB panel and LDCT for lung cancer detection were evaluated through receiver operating characteristic (ROC) analyses, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) being assessed and compared. These results could have significant implications for the development of improved screening methods for lung cancer. Results: Over the 12-month observation period, 26 individuals were diagnosed with lung cancer. The 7-TAAB panel demonstrated promising sensitivity (61.5%) and a high degree of specificity (88.5%). The panel's area under the receiver operating characteristic (ROC) curve was 0.8062, which was superior to that of any individual TAAB. In stage I patients, the sensitivity of the panel was 50%. In our cohort, there was no gender or age bias observed. This 7-TAAB panel showed a sensitivity of approximately 60% in detecting lung cancer, regardless of histological subtype or lesion size. Notably, ground-glass nodules had a higher diagnostic rate than solid nodules (83.3% vs. 36.4%, P = 0.021). The ROC analyses further revealed that the combination of LDCT with the 7-TAAB assay exhibited a significantly superior diagnostic efficacy than LDCT alone. Conclusion: In the context of the study, it was demonstrated that the 7-TAAB panel showed improved detective efficacy of LDCT, thus serving as an effective aid for the detection of lung cancer in real-world scenarios.

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.

Rational Design of Metal-Alkoxide-Functionalized Metal-Organic Frameworks for Synergistic Dual Activation of CH4 and CO2 toward Acetic Acid Synthesis.

The concurrent conversion of CH4 and CO2 into acetic acid is an ideal route to migrate the two greenhouse gases and manufacture a high-value-added C2 product with an atom economy of 100% but remains challenging due to the chemical inertness of both gases. By leveraging density functional theory (DFT) calculations, we report herein the computational design of metal-alkoxide-functionalized metal-organic framework (MOF) UiO-67 with well-defined dual sites that can activate CH4 and CO2 cooperatively to boost acetic acid synthesis. The dual sites are distributed on two adjacent functionalized organic linkers originating from the same node and feature a metal-metal distance of about 6-7 Å. Initially, a total of 13 single-site metal-alkoxide-functionalized UiO-67s (including three alkaline earth metals and 10 transition metals) are examined; then, favorable metal-alkoxides are identified and further used to design dual-site metal-alkoxide-functionalized UiO-67s for converting CH4 and CO2 into acetic acid. Detailed mechanistic investigation predicts that the dual-site UiO-67s functionalized with Mn-, Fe-, Co-, Ni-. and Zn-alkoxide are highly promising catalysts for this reaction. Compared to the single-site counterparts, the metal pair-site UiO-67s provide a subtle microenvironment for synergistic dual activation of CH4 and CO2, thus efficiently stabilizing the transition state and substantially reducing the reaction barrier for C-C coupling. The microscopic insights and design strategies in this work might advance the development of efficient MOF-based catalysts with built-in cooperative active sites toward direct acetic acid synthesis from CH4 and CO2.

Fast water transport and molecular sieving through ultrathin ordered conjugated-polymer-framework membranes.

The development of membranes that block solutes while allowing rapid water transport is of great importance. The microstructure of the membrane needs to be rationally designed at the molecular level to achieve precise molecular sieving and high water flux simultaneously. We report the design and fabrication of ultrathin, ordered conjugated-polymer-framework (CPF) films with thicknesses down to 1 nm via chemical vapour deposition and their performance as separation membranes. Our CPF membranes inherently have regular rhombic sub-nanometre (10.3 × 3.7 Å) channels, unlike membranes made of carbon nanotubes or graphene, whose separation performance depends on the alignment or stacking of materials. The optimized membrane exhibited a high water/NaCl selectivity of ∼6,900 and water permeance of ∼112 mol m-2 h-1 bar-1, and salt rejection >99.5% in high-salinity mixed-ion separations driven by osmotic pressure. Molecular dynamics simulations revealed that water molecules quickly and collectively pass through the membrane by forming a continuous three-dimensional network within the hydrophobic channels. The advent of ordered CPF provides a route towards developing carbon-based membranes for precise molecular separation.

Accelerating Discovery of High Fractional Free Volume Polymers from a Data-Driven Approach.

As a fundamental structure characteristic in polymers, fractional free volume (FFV) plays an indispensable role in governing polymer properties and performance. However, the design of new high-FFV polymers is challenging. In this study, we report a data-driven approach and aim to accelerate the discovery of high-FFV polymers. First, a computational method is proposed to calculate FFV, and a two-step fragmentation method is developed to construct a fragment library for digital representation of polymer structures. Data mining is employed to identify promising fragments for high FFV. Subsequently, machine learning (ML) models are trained using a data set with 1683 polymers and their excellent transferability is demonstrated by out-of-sample predictions in another data set with 11,479 polymers. Finally, the ML models are used to screen ∼1 million hypothetical polymers, and 29,482 polymers with FFV > 0.2 are shortlisted; representative high-FFV polymers are validated by molecular simulations, and design strategies are highlighted. To further facilitate the discovery of new high-FFV polymers, we develop an online interactive platform https://ffv-prediction.herokuapp.com, which allows for rapid FFV predictions, given polymer structures. The data-driven approach in this study might advance the development of new high-FFV polymers and further explore quantitative structure-property relationships for polymers.

Growing single crystals of two-dimensional covalent organic frameworks enabled by intermediate tracing study.

Resolving single-crystal structures of two-dimensional covalent organic frameworks (2D COFs) is a great challenge, hindered in part by limited strategies for growing high-quality crystals. A better understanding of the growth mechanism facilitates development of methods to grow high-quality 2D COF single crystals. Here, we take a different perspective to explore the 2D COF growth process by tracing growth intermediates. We discover two different growth mechanisms, nucleation and self-healing, in which self-assembly and pre-arrangement of monomers and oligomers are important factors for obtaining highly crystalline 2D COFs. These findings enable us to grow micron-sized 2D single crystalline COF Py-1P. The crystal structure of Py-1P is successfully characterized by three-dimensional electron diffraction (3DED), which confirms that Py-1P does, in part, adopt the widely predicted AA stacking structure. In addition, we find the majority of Py-1P crystals (>90%) have a previously unknown structure, containing 6 stacking layers within one unit cell.