Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation

Recent development of metal-organic frameworks and their composites in electromagnetic wave absorption and shielding applications

With the rapid development of information and communication industries, the usage of electromagnetic waves has caused the hazard of human health and misfunction of devices. The adsorption and shielding of electromagnetic waves have been achieved in various materials. The unique adjustable spatial structure makes metal-organic frameworks (MOFs) promising for electromagnetic shielding and adsorbing. As MOFs research advances, various large-scale MOF-based materials have been developed. For instance, MOFs spatial structure has been expanded from 2D to 3D to load more ligands. Progress in synthetic methods for MOFs and their derivatives is advancing, with priority on large-scale preparation and green synthesis. This review summarizes the methods for synthesizing MOFs and their derivatives, and explores the effects of MOFs spatial structure on electromagnetic interference (EMI) shielding and electromagnetic wave absorption capabilities. At the same time, detailed examples are used to focus on the applications of five different MOFs composites in electromagnetic shielding and electromagnetic wave absorption. Finally, the current challenges and prospects of MOFs in the electromagnetic field are introduced, providing a useful reference for the preparation and design of MOFs and their composites for electromagnetic wave processing applications.

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.

Accelerated Discovery of Metal-Organic Frameworks for CO2 Capture by Artificial Intelligence

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture.

Solvent-Directed Construction of a Nanoporous Metal-Organic Framework with Potential in Selective Adsorption and Separation of Gas Mixtures Studied by Grand Canonical Monte Carlo Simulations

In this report, a microporous metal-organic framework of [Ca(TDC)(DMA)]n (1) and a two-dimensional coordination polymer of [Ca(TDC)(DMF)2 ]n (2), (TDC2- =Thiophene-2,5-dicarboxylate, DMA=N, N'-dimethylacetamide and DMF=N, N'-dimethylformamide) based on Ca(II) were designed by the effect of solvent, and X-ray analysis was performed for the single crystals of 1 and 2. Then, compound 1 was synthesized in three different methods and identified with a set of analyses. Compared to other adsorbents, MOFs are widely used in the field of adsorption and separation of various gases due to a series of distinctive features such as diverse and adjustable structures pores with different dimensions, high porosity and surface area with regular distribution of active sites. Therefore, the ability of 1 to uptake single gases (CH4 , CO2 , C2 H2 , H2, and N2 ) and separation of several binary mixtures of gases (CO2 /CH4 , CO2 /N2 , CO2 /H2 and CO2 /C2 H2 ), were investigated using Grand Canonical Monte Carlo simulations. Volumetric and gravimetric adsorption isotherms in various operating conditions, the isosteric heat of adsorption (qst ), the chemical potential for each thermodynamic state, and snapshots during the simulation process were reported in all cases. The results obtained from the adsorption simulation indicate that compound 1 has a high capacity for uptake of H2 (16 mmol g-1 ) and N2 (12.5 mmol g-1 ), CO2 (6.6 mmol g-1 ), C2 H2 (5 mmol g-1 ) and CH4 (1.5 mmol g-1 ) gases at 1 bar. It also performs well in separating CO2 in binary mixtures, which can be attributed to the presence of open metal sites in nodes of 1 and their electrostatic tendency to interact with CO2 containing the higher quadrupole dipole moment compared to other components of the mixture.

MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal-Organic Frameworks

The remarkable versatility of metal-organic frameworks (MOFs) stems from their rich chemical information, leading to numerous successful applications. However, identifying optimal MOFs for specific tasks necessitates a thorough assessment of their chemical attributes. Conventional machine learning approaches for MOF prediction have relied on intricate chemical and structural details, hampering rapid evaluations. Drawing inspiration from recent advancements exemplified by Snurr et al., wherein a text string was used to represent a MOF (MOFid), we introduce a MOFid-aided deep learning model, named the MOF-GRU model. This model, founded on natural language processing principles and utilizing the gated recurrent unit architecture, leverages the serialized text string representation of metal-organic frameworks (MOFs) to forecast gas separation performance. Through a focused study on CH4/N2 separation, we substantiate the efficacy of this approach. Comparative assessments against traditional machine learning techniques underscore our model's superior predictive accuracy and its capacity to handle extensive data sets adeptly. The MOF-GRU model remarkably uncovers latent structure-performance relationships with only MOF sequences, obviating the necessity for intricate three-dimensional (3D) structural information. Overall, this model's judicious design empowers efficient data utilization, thereby hastening the discovery of high-performance materials tailored for gas separation applications.

Accelerating Metal-Organic Framework Selection for Type III Porous Liquids by Synergizing Machine Learning and Molecular Simulation

MOF-based type III porous liquids, comprising porous MOFs dissolved in a liquid solvent, have attracted increasing attention in carbon capture. However, discovering appropriate MOFs to prepare porous liquids was still limited in experiments, wasting time and energy. In this study, we have used the density functional theory and molecular dynamics simulation methods to identify 4530 MOF candidates as the core database based on the idea of prohibiting the pore occupancy of porous liquids by the solvent, [DBU-PEG][NTf2] ionic liquid. Based on high-throughput molecular simulation, random forest machine learning models were first trained to predict the CO2 sorption and the CO2/N2 sorption selectivity of MOFs to screen the MOFs to prepare porous liquids. The feature importance was inferred based on Shapley Additive Explanations (SHAP) interpretation, and the ranking of the top 5 descriptors for sorption/selectivity trade-off (TSN) was gravimetric surface area (GSA) > porosity > density > metal fraction > pore size distribution (PSD, 3.5-4 Å). RICBEM was predicted to be one candidate for preparing porous liquid with CO2 sorption capacity of 20.87 mmol/g and CO2/N2 sorption selectivity of 16.75. The experimental results showed that the RICBEM-based porous liquid was successfully synthesized with CO2 sorption capacity of 2.21 mmol/g and CO2/N2 sorption selectivity of 63.2, the best carbon capture performance known to date. Such a screening method would advance the screening of cores and solvents for preparing type III porous liquids with different applications by addressing corresponding factors.