Screening of Covalent-Organic Frameworks for Adsorption Heat Pumps

Discovery of a molecular adsorbent for efficient CO2/CH4 separation using a computation-ready experimental database of porous molecular materials

The development and sharing of computational databases for metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have significantly accelerated the exploration and application of these materials. Recently, molecular materials have emerged as a notable subclass of porous materials, characterized by their crystallinity, modularity, and processability. Among these, macrocycles and cages stand out as representative molecules. Experimental discovery of a target molecular material from a vast possibility of structures for defined applications is generally impractical due to high experimental costs. This study presents the most extensive Computation-ready Experimental (CoRE) database of macrocycles and cages (MCD) to date, comprising 7939 structures. Using the MCD, we conducted simulations of binary CO2/CH4 competitive adsorption under conditions relevant to industrial applications. These simulations established a structure-property-function relationship, enabling the identification of materials with potential for CO2/CH4 separation. Among them, a macrocycle, NDI-Δ, exhibited promising CO2 adsorption capacity and selectivity, as confirmed by gas sorption and breakthrough experiments.

Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO2 capture, CH4 storage, gas separation, catalysis, etc. Traditional methods of material research, which mainly rely on manual experimentation, are not particularly efficient, while with advancements in computer science, high-throughput computational screening methods based on molecular simulation have become crucial in material discovery, yet they face limitations in terms of computational resources and time. Currently, machine learning (ML) has emerged as a transformative tool in many fields, capable of analyzing large data sets, identifying underlying patterns, and predicting material performance efficiently and accurately. This approach, termed "materials genomics", combines high-throughput computational screening with ML to predict and design high-performance materials, significantly speeding up the discovery process compared to traditional methods. This review discusses the functions of ML in the screening, design, and performance prediction of COFs and highlights their applications across various domains like CO2 capture, CH4 storage, gas separation, and catalysis, thereby providing new research directions and enhancing the understanding of COF materials and their applications.

pyMSER─An Open-Source Library for Automatic Equilibration Detection in Molecular Simulations

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point at which a steady state is reached, ensuring that figures of merit are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.

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

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

Revolutionizing the structural design and determination of covalent-organic frameworks: principles, methods, and techniques

Covalent organic frameworks (COFs) represent an important class of crystalline porous materials with designable structures and functions. The interconnected organic monomers, featuring pre-designed symmetries and connectivities, dictate the structures of COFs, endowing them with high thermal and chemical stability, large surface area, and tunable micropores. Furthermore, by utilizing pre-functionalization or post-synthetic functionalization strategies, COFs can acquire multifunctionalities, leading to their versatile applications in gas separation/storage, catalysis, and optoelectronic devices. Our review provides a comprehensive account of the latest advancements in the principles, methods, and techniques for structural design and determination of COFs. These cutting-edge approaches enable the rational design and precise elucidation of COF structures, addressing fundamental physicochemical challenges associated with host-guest interactions, topological transformations, network interpenetration, and defect-mediated catalysis.

Covalent organic frameworks for CO2 capture: from laboratory curiosity to industry implementation

CO2 concentration in the atmosphere has increased by about 40% since the 1960s. Among various technologies available for carbon capture, adsorption and membrane processes have been receiving tremendous attention due to their potential to capture CO2 at low costs. The kernel for such processes is the sorbent and membrane materials, and tremendous progress has been made in designing and fabricating novel porous materials for carbon capture. Covalent organic frameworks (COFs), a class of porous crystalline materials, are promising sorbents for CO2 capture due to their high surface area, low density, controllable pore size and structure, and preferable stabilities. However, the absence of synergistic developments between materials and engineering processes hinders achieving the qualitative leap for net-zero emissions. Considering the lack of a timely review on the combination of state-of-the-art COFs and engineering processes, in this Tutorial Review, we emphasize the developments of COFs for meeting the challenges of carbon capture and disclose the strategies of fabricating COFs for realizing industrial implementation. Moreover, this review presents a detailed and basic description of the engineering processes and industrial status of carbon capture. It highlights the importance of machine learning in integrating simulations of molecular and engineering levels. We aim to stimulate both academia and industry communities for joined efforts in bringing COFs to practical carbon capture.

Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N2 and O2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Qst is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Qst is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.

Evaluation of ZIF-8 and ZIF-90 as Heat Storage Materials by Using Water, Methanol and Ethanol as Working Fluids

The increasing demand for heating/cooling is of grave concern due to the ever-increasing population. One method that addresses this issue and uses renewable energy is Thermochemical Energy Storage (TCES), which is based on the reversible chemical reactions and/or sorption processes of gases in solids or liquids. Zeolitic imidazolate frameworks (ZIFs), composed of transition metal ions (Zn, Co, etc.) and imidazolate linkers, have gained significant interest recently as porous adsorbents in low temperature sorption-based TES (sun/waste heat). In this study, we examined two different sodalite-type ZIF structures (ZIF-8 and ZIF-90) for their potential heat storage applications, based on the adsorption of water, methanol and ethanol as adsorbates. Both ZIF structures were analysed using PXRD, TGA, SEM and N2 physisorption while the % adsorbate uptake and desorption enthalpy was evaluated using TGA and DSC analysis, respectively. Among the studied adsorbent–adsorbate pairs, ZIF-90-water showed the highest desorption enthalpy, the fastest sorption kinetics and, therefore, the best potential for use in heat storage/reallocation applications. This was due to its significantly smaller particle size and higher specific surface area, and the presence of mesoporosity as well as polar groups in ZIF-90 when compared to ZIF-8.

Chemically Stable Carbazole-Based Imine Covalent Organic Frameworks with Acidochromic Response for Humidity Control Applications

Isoreticular chemically stable two-dimensional imine covalent organic frameworks (COFs), further denoted as DUT-175 and DUT-176, are obtained in a reaction of 4,4′-bis(9H-carbazol-9-yl)biphenyl tetraaldehyde with phenyldiamine and benzidine. The crystal structures, solved and refined from the powder X-ray diffraction data and confirmed by high-resolution transmission electron microscopy, indicate AA-stacked layer structures. Both structures feature distorted hexagonal channel pores, assuring remarkable porosity (S_BET = 1071 m² g^–1 for DUT-175 and S_BET = 1062 m² g^–1 for DUT-176), as confirmed by adsorption of gases and vapors. The complex conjugated π system of the COFs involves electron-rich carbazole building units, which in combination with the imine groups allow reversible pH-dependent protonation of the frameworks, accompanied by charge transfer and shift of the absorption bands in the UV–vis spectrum. The sigmoidal shape of the water vapor adsorption and desorption isotherms with a steep adsorption step at p/p₀ = 0.4–0.6 in combination with excellent stability over dozens of adsorption and desorption cycles ranks these COFs among the best materials for indoor humidity control applications.

Digital-intellectual design of microporous organic polymers

Microporous organic polymers (MOPs) are a new class of microporous materials. Due to their high porosity, large pore volume, and large surface area, MOPs exhibit excellent performance in gas adsorption and storage, membrane separation, ion capture, heterogeneous catalysis, light energy conversion and storage, capacitance, and other fields. However, selecting high-performance materials for specific applications from thousands of candidate MOPs is a key problem. Traditional design strategies for new materials with targeted properties, including trial-and-error and relying on the experiences of domain experts, are time- and cost-consuming. With the rapid development of computation technology and theoretical chemistry, the discovery of new materials is no longer a purely experimental subject. Breaking away from the traditional trial-and-error strategy for materials discovery, materials design is emerging and gaining increasing attention. In addition, the ability to collect "big data" has greatly improved and has further stimulated the development of new methods for materials design and discovery. In this perspective, we examine how data-driven techniques combine artificial intelligence (AI) and human expertise, playing a significant role in the design of MOPs. Such analytics can significantly reduce time-to-insight and accelerate the cost-effective materials discovery, which is the goal for designing future MOPs.

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M-N _x O _y (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M-N _x O _y were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni-N₂O₂ unit is subsequently employed to synthesize the corresponding Ni-COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni-N₂O₂ coordination environment. The studies of electrocatalytic activities confirm that Ni-COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm^-2 at a low overpotential of 335 mV. Furthermore, Ni-COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts.

Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal-Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal-organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO₂. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal-Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows.

Devising Materials Manufacturing Toward Lab-to-Fab Translation of Flexible Electronics

Flexible electronics have witnessed exciting progress in academia over the past decade, but most of the research outcomes have yet to be translated into products or gain much market share. For mass production and commercialization, industrial adoption of newly developed functional materials and fabrication techniques is a prerequisite. However, due to the disparate features of academic laboratories and industrial plants, translating materials and manufacturing technologies from labs to fabs is notoriously difficult. Therefore, herein, key challenges in the materials manufacturing of flexible electronics are identified and discussed for its lab-to-fab translation, along the four stages in product manufacturing: design, materials supply, processing, and integration. Perspectives on industry-oriented strategies to overcome some of these obstacles are also proposed. Priorities for action are outlined, including standardization, iteration between basic and applied research, and adoption of smart manufacturing. With concerted efforts from academia and industry, flexible electronics will bring a bigger impact to society as promised.

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

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