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.

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.

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.

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.

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.

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.