In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis

Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models

A machine learning (ML) model is developed for predicting useable methane (CH4) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH4. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH4 capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH4 capacity for a prescribed operating condition.

Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations

Bringing advances in machine learning to chemical science is leading to a revolutionary change in the way of accelerating materials discovery and atomic-scale simulations. Currently, most successful machine learning schemes can be largely traced to the use of localized atomic environments in the structural representation of materials and molecules. However, this may undermine the reliability of machine learning models for mapping complex systems and describing long-range physical effects because of the lack of non-local correlations between atoms. To overcome such limitations, here we report a graph attention neural network as a unified framework to map materials and molecules into a generalizable and interpretable representation that combines local and non-local information of atomic environments from multiple scales. As an exemplary study, our model is applied to predict the electronic structure properties of metal-organic frameworks (MOFs) which have notable diversity in compositions and structures. The results show that our model achieves the state-of-the-art performance. The clustering analysis further demonstrates that our model enables high-level identification of MOFs with spatial and chemical resolution, which would facilitate the rational design of promising reticular materials. Furthermore, the application of our model in predicting the heat capacity of complex nanoporous materials, a critical property in a carbon capture process, showcases its versatility and accuracy in handling diverse physical properties beyond electronic structures.

Supramolecular isomerism and structural flexibility in coordination networks sustained by cadmium rod building blocks

Bifunctional N-donor carboxylate linkers generally afford dia and sql topology coordination networks of general formula ML₂ that are based upon the MN₂(CO₂)₂ molecular building block (MBB). Herein, we report on a new N-donor carboxylate linker, β-(3,4-pyridinedicarboximido)propionate (PyImPr), which afforded Cd(PyImPr)₂via reaction of PyImPrH with Cd(acetate)₂·2H₂O. We observed that, depending upon whether Cd(PyImPr)₂ was prepared by layering or solvothermal methods, 2D or 3D supramolecular isomers, respectively, of Cd(PyImPr)₂ were isolated. Single crystal X-ray diffraction studies revealed that both supramolecular isomers are comprised of the same carboxylate bridged rod building block, RBB. We were interested to determine if the ethylene moiety of PyImPr could enable structural flexibility. Indeed, open-to-closed structural transformations occurred upon solvent removal for both phases, but they were found to be irreversible. A survey of the Cambridge Structural Database (CSD) was conducted to analyse the relative frequency of RBB topologies in related ML₂ coordination networks in order to provide insight from a crystal engineering perspective.

A review on development of metal-organic framework-derived bifunctional electrocatalysts for oxygen electrodes in metal-air batteries

Worldwide demand for oil, coal, and natural gas has increased recently because of odd weather patterns and economies recovering from the pandemic. By using these fuels at an astonishing rate, their reserves are running low with each passing decade. Increased reliance on these sources is contributing significantly to both global warming and power shortage problems. It is vital to highlight and focus on using renewable energy sources for power production and storage. This review aims to discuss one of the cutting-edge technologies, metal-air batteries, which are currently being researched for energy storage applications. A battery that employs an external cathode of ambient air and an anode constructed of pure metal in which an electrolyte can be aqueous or aprotic electrolyte is termed as a metal-air battery (MAB). Due to their reportedly higher energy density, MABs are frequently hailed as the electrochemical energy storage of the future for applications like grid storage or electric car energy storage. The demand of the upcoming energy storage technologies can be satisfied by these MABs. The usage of metal-organic frameworks (MOFs) in metal-air batteries as a bi-functional electrocatalyst has been widely studied in the last decade. Metal ions or arrays bound to organic ligands to create one, two, or three-dimensional structures make up the family of molecules known as MOFs. They are a subclass of coordination polymers; metal nodes and organic linkers form different classes of these porous materials. Because of their modular design, they offer excellent synthetic tunability, enabling precise chemical and structural control that is highly desirable in electrode materials of MABs.

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.

PoreMatMod.jl: Julia Package for in Silico Postsynthetic Modification of Crystal Structure Models

PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.jl functions as a find-and-replace algorithm on crystal structures by leveraging (i) Ullmann's algorithm to search for subgraphs of the crystal structure graph that are isomorphic to the graph of a query fragment and (ii) the orthogonal Procrustes algorithm to align a replacement fragment with a targeted substructure of the crystal structure for installation. The prominent application of PoreMatMod.jl is to generate libraries of hypothetical structures for virtual screenings. For example, one can install functional groups on the linkers of a parent MOF, mimicking postsynthetic modification. Other applications of PoreMatMod.jl to modify crystal structure models include introducing defects with precision and correcting artifacts of X-ray structure determination (adding missing hydrogen atoms, resolving disorder, and removing guest molecules). The find-and-replace operations implemented by PoreMatMod.jl can be applied broadly to diverse atomistic systems for various in silico structural modification tasks.

Assembly of a rod indium–organic framework with fluorescence properties for selective sensing of Cu2+, Fe3+ and nitroaromatics in water

A chiral fluorescent In-MOF with two types of unique homochiral In–O–In helical chains was synthesized. Then it was developed as a highly sensitive fluorescence sensor for detecting Cu2+, Fe3+ and nitroaromatics in water.

Predicting hydrogen storage in MOFs via machine learning

The H2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (>5,300 m2 g-1), void fractions (∼0.90), and pore volumes (>3.3 cm3 g-1). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature.

Analytical Approach to Screen Semiconducting MOFs Using Bloch Mode Analysis and Spectroscopic Measurements

A rapid and simple analytical approach is developed to screen the semiconducting properties of metal organic frameworks (MOFs) by modeling the band structure and predicting the density of state of isoreticular MOFs (IRMOFs). One can consider the periodic arrangement of metal nodes linked by organic subunits as a 1D periodic array crystal model, which can be aligned with any unit-cell axis included in the IRMOF's primitive cubic lattice. In such a structure, each valence electron of a metal atom feels the potential field of the entire periodic array. We allocate the 1D periodic array in a crystal unit cell to three IRMOFs-n (n = 1, 8, and 10) of the Zn₄O(L)₃ IRMOF series and apply the model to their crystal lattices with unit-cell constants a = 25.66, 30.09, and 34.28 Å, respectively. By solving Schrödinger's equation with a Kronig-Penney periodic potential and fitting the computed energy spectra to IRMOFs' experimental spectroscopic data, we model electronic band structures and obtain densities of state. The band diagram of each IRMOF reveals the nature of its electronic structures and density of state, allowing one to identify its n- or p-type semiconducting behavior. This novel analytical approach serves as a predictive and rapid screening tool to search the MOF database to identify potential semiconducting MOFs.

Effect of axial molecules and linker length on CO2 adsorption and selectivity of CAU-8: a combined DFT and GCMC simulation study

Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) calculations are performed to study the structures and carbon dioxide (CO₂) adsorption properties of the newly designed metal–organic framework based on the CAU-8 (CAU stands for Christian-Albrechts Universität) prototype. In the new MOFs, the 4,4′-benzophenonedicarboxylic acid (H₂BPDC) linker of CAU-8 is substituted by 4,4′-oxalylbis(azanediyl)dibenzoic acid (H₂ODA) and 4,4′-teraphthaloylbis(azanediyl)dibenzoic acid (H₂TDA) containing amide groups (–CO–NH- motif). Furthermore, MgO₆ octahedral chains where dimethyl sulfoxide (DMSO) decorating the axial position bridged two Mg²⁺ ions are considered. The formation energies indicate that modified CAU-8 is thermodynamically stable. The reaction mechanisms between the metal clusters and the linkers to form the materials are also proposed. GCMC calculations show that CO₂ adsorptions and selectivities of Al-based MOFs are better than those of Mg-based MOFs, which is due to DMSO. Amide groups made CO₂ molecules more intensively distributed besides organic linkers. CO₂ uptakes and selectivities of MOFs containing H₂TDA linkers are better in comparison with those of MOFs containing H₂BPDC linkers or H₂ODA linkers.

Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) calculations are performed to study the structures and CO₂ adsorption properties of the newly designed metal–organic framework based on the CAU-8 prototype.

The Role of Machine Learning in the Understanding and Design of Materials

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model's robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

Understanding the diversity of the metal-organic framework ecosystem

Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-Off in Multireference Diagnostics

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %E_corr. None of the DFT-based diagnostics are nearly as predictive of %E_corr as the best WFT-based diagnostics. To overcome the limitation of this cost-accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

Nanoscale metal-organic frameworks in detecting cancer biomarkers

Following the efficient performance of metal-organic frameworks (MOFs) as recognition elements in gas sensors, biosensors based on MOFs are now being investigated to capture and quantify potential cancer biomarkers, such as circulating tumor cells (CTCs), nucleic acids and proteins. The current status of MOF-based biosensors in the detection of early stages of cancer is in its infancy, although it has significantly emerged since the beginning of this decade. That said, salient research has been conducted in the past five years to utilize the distinctive porous crystalline structure of MOFs for highly sensitive and selective detection of cancer biomarkers. In this pursual, MOFs designed with bimetallic assembly, doped with magnetic nanoparticles, coated with polymers, and even conjugated with peptides or oligonucleotides have shown promising outcomes in detecting CTCs, nucleic acids and proteins. In particular, aptamer-conjugated MOFs are able to perform at a lower limit of detection down to the femtomolar, implying their efficacy for the point of care testing in clinical trials. In this way, aptasensors based on aptamer-conjugated MOFs present a newer sub-branch, to be coined as a MOFTA sensor in the current review. Considering the emerging progress and promising outcomes of MOFTA sensors as well as a variety of MOF-based techniques of detecting cancer biomarkers, this review will highlight their significant advances and related aspects in the recent five years on the context of detecting CTCs, nucleic acids and proteins for the early-stage detection of cancer.

Fine-Tuning the Coordinatively Unsaturated Metal Sites of Metal-Organic Frameworks by Plasma Engraving for Enhanced Electrocatalytic Activity

Metal-organic frameworks (MOFs) have recently emerged as promising electrocatalysts because of their atomically dispersed metal sites and porous structures. The active sites of MOF catalysts largely exist as coordinatively unsaturated metal sites (CUMSs). In this study, facile microwave-induced plasma engraving is applied to fine-tune the CUMSs of cobalt-based MOF (Co-MOF-74) without destroying its phase integrity by controlling the plasma-engraving species, intensity, and duration. The electrochemical activity of the engraved MOF is found to be quantitatively correlated to the coordination geometry of the metal centers corresponding to CUMSs. Specifically, the hydrogen plasma-engraved Co-MOF-74 shows an enhanced catalytic activity of oxygen evolution reaction, which exhibits a low overpotential (337 mV at 15 mA cm^-2), high turnover frequency (0.0219 s^-1), and large mass activity (54.3 A g^-1). The developed CUMS control strategy and the revealed CUMSs activity correlation can inspire the further microstructure tuning of MOFs for various applications.

Can Metal-Organic Frameworks Be Used for Cannabis Breathalyzers?

Δ⁹-Tetrahydrocannabinol (THC) is the principal psychoactive component of cannabis, and there is an urgent need to build low-cost and portable devices that can detect its presence from breath. Similarly to alcohol detectors, these tools can be used by law enforcement to determine driver intoxication and enforce safer and more regulated use of cannabis. In this work, we propose to use a class of microporous crystals, metal-organic frameworks (MOFs), to selectively adsorb THC that can be later detected using optical, electrochemical, or fluorescence-based sensing methods. We computationally screened more than 5000 MOFs, highlighting the materials that have the largest affinity with THC, as well as the highest selectivity against water, showing that it is thermodynamically feasible for MOFs to adsorb THC from humid breath. We propose and compare different models for THC and different computational protocols to rank the promising materials, also presenting a novel approach to assess the permeability of a porous framework to nonspherical molecules. We identified three adsorption motifs in MOFs with high affinity to THC, which we refer to as "narrow channels", "thick walls", and "parking spots". Therefore, we expect our protocols and our findings to be generalizable for different classes of microporous materials and also for investigating the adsorption properties of other large molecules that, like THC, have a nonspherical shape.

Frustrated Lewis pair-mediated fixation of CO2 within a metal-organic framework

Finding pragmatic solutions to curb the accumulation of atmospheric CO2 and tackle the associated greenhouse effect is challenging. Herein, we demonstrate the use of an in situ formed frustrated Lewis pair (FLP) within a metal-organic framework (MOF) to effectively hydrogenate CO2 to methoxide at a relatively low temperature and pressure. The work presents a step toward the discovery of practical catalysts for CO2 reduction and conversion.

Ab Initio Flexible Force Field for Metal-Organic Frameworks Using Dummy Model Coordination Bonds

We present force fields developed from periodic density functional theory (DFT) calculations that can be used in classical molecular simulations to model M-MOF-74 (M = Co, Fe, Mg, Mn, Ni, Zn) and its extended linker analogs. Our force fields are based on cationic dummy models (CDMs). These dummy models simplify the methodology required to tune the parameters and improve the accuracy of the force fields. We used our force fields to compare mechanical properties across the M-MOF-74 series and determine that increasing the size of the linker decreases the framework rigidity. In addition, we applied our force fields to an extended linker analog of Mg-MOF-74 and characterized the free energy of a previously reported deformation pattern in which the one-dimensional hexagonal channels of the framework become irregular. The free energy profiles confirm that the deformation is adsorbate induced and impossible to access solely by a pressure stimulus. On the basis of our results, we conclude that the force fields presented here and others that may be developed using our methodology are transferable across metal-organic framework series that share a metal center topology. Finally, we believe that these force fields have the potential to be adapted for the study of complex problems in MOF chemistry, including defects and crystal growth, that have thus far been beyond the scope of classical molecular simulations.

Metal Substitution as the Method of Modifying Electronic Structure of Metal-Organic Frameworks

Targeted modification of electronic structure is an important step in the optimization of metal–organic frameworks (MOFs) for photovoltaic, sensing, and photocatalytic applications. The key parameters to be controlled include the band gap, the absolute energy position of band edges, the excited state charge separation, and degree of hybridization between metal and ligand sites. Partial metal replacement, or metal doping, within secondary building units is a promising, yet relatively unexplored route to modulate these properties in MOFs. Therefore, in the present study, a general method for selecting metal dopant is worked out in theory and validated by experiment, retaining MIL-125 and UiO-66 as the model systems. Metal mixing enables targeted optimization of key electronic structure parameters. This method is applicable to any MOF architecture and can serve as a roadmap for future synthesis of MOFs with predefined properties.

In Situ Formation of Frustrated Lewis Pairs in a Water-Tolerant Metal-Organic Framework for the Transformation of CO2

Frustrated Lewis pairs (FLPs) consist of sterically hindered Lewis acids and Lewis bases, which provide high catalytic activity towards non-metal-mediated activation of "inert" small molecules, including CO₂ among others. One critical issue of homogeneous FLPs, however, is their instability upon recycling, leading to catalytic deactivation. Herein, we provide a solution to this issue by incorporating a bulky Lewis acid-functionalized ligand into a water-tolerant metal-organic framework (MOF), named SION-105, and employing Lewis basic diamine substrates for the in situ formation of FLPs within the MOF. Using CO₂ as a C1-feedstock, this combination allows for the efficient transformation of a variety of diamine substrates into benzimidazoles. SION-105 can be easily recycled by washing with MeOH and reused multiple times without losing its identity and catalytic activity, highlighting the advantage of the MOF approach in FLP chemistry.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.

Tailoring of the electronic property of Zn-BTC metal–organic framework via ligand functionalization: an ab initio investigation

Metal–organic frameworks (MOFs) are porous materials of recent interest due to their promising properties for technological applications. In this paper, the structure–property relationships of pristine and functionalized Zn-BTC (Zn₃(BTC)₂) MOFs are investigated. The results based on density functional theory (DFT) find that MOFs with coordinatively saturated secondary building units (SBU) are metallic, and MOFs with coordinatively unsaturated SBU are semi-conducting. The ligand functionalization with electron acceptor (cyano-) and electron donor (amino-) groups appears to tailor the electronic properties of Zn-BTC MOFs; amino-functionalization led to a significant upward shift of the band-edges whereas cyano-functionalization yields shifting of band-edges in the opposite direction, which led to a narrowing of the band gap. Modifying the electronic properties through such ligand functionalization design principles can be useful in engineering MOFs for gas sensing and device applications.

The structure–property relationships of pristine and functionalized Zn-BTC (Zn₃(BTC)₂) metal–organic frameworks are investigated.

Boosting Transport Distances for Molecular Excitons within Photoexcited Metal-Organic Framework Films

Herein, we describe the fabrication of porphyrin-containing metal-organic framework thin films with 1,4-diazabicyclo[2.2.2]octane (DABCO) pillaring linkers and investigate exciton transport within the films. Steady-state emission spectroscopy indicates that the exciton can traverse up to 26 porphyrin layers when DABCO is used as a pillaring linker, whereas on average only 9-11 layers can be traversed when either 4,4'-bipyridine (a pillaring linker) or pyridine (a nonpillaring, layer-interdigitating ligand) is used. These results can be understood by taking into account the decreased separation distances between transition dipoles associated with chromophores (porphyrins) sited in adjacent layers. Shorter distances translate into faster Förster-type exciton hopping and, therefore, more hops within the few nanosecond lifetime of the porphyrin's singlet excited-state. The findings have favorable implications for the development of MOF-based photoelectrodes and photoelectrochemical energy-conversion devices.

Computer-aided discovery of a metal-organic framework with superior oxygen uptake

Current advances in materials science have resulted in the rapid emergence of thousands of functional adsorbent materials in recent years. This clearly creates multiple opportunities for their potential application, but it also creates the following challenge: how does one identify the most promising structures, among the thousands of possibilities, for a particular application? Here, we present a case of computer-aided material discovery, in which we complete the full cycle from computational screening of metal–organic framework materials for oxygen storage, to identification, synthesis and measurement of oxygen adsorption in the top-ranked structure. We introduce an interactive visualization concept to analyze over 1000 unique structure–property plots in five dimensions and delimit the relationships between structural properties and oxygen adsorption performance at different pressures for 2932 already-synthesized structures. We also report a world-record holding material for oxygen storage, UMCM-152, which delivers 22.5% more oxygen than the best known material to date, to the best of our knowledge.

The emergence of thousands of metal–organic frameworks (MOFs) has created the challenge of finding promising structures for particular applications. Here, the authors present a tool for computer-aided material discovery where a large number of MOFs are screened, with the top-ranked structure synthesized for oxygen storage applications.

Distinguishing Metal-Organic Frameworks

We consider two metal-organic frameworks as identical if they share the same bond network respecting the atom types. An algorithm is presented that decides whether two metal-organic frameworks are the same. It is based on distinguishing structures by comparing a set of descriptors that is obtained from the bond network. We demonstrate our algorithm by analyzing the CoRe MOF database of DFT optimized structures with DDEC partial atomic charges using the program package ToposPro.

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. We hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area can yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.

Predicting Product Distribution of Propene Dimerization in Nanoporous Materials

In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C₆ olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin isomers, which are valued as industrial feedstocks. A pore size commensurate with the size of the desired linear products enhances linear conversion by sterically hindering the branched isomers. Another promising feature is the presence of open metal sites, which interact with the olefin π-bond to provide favorable binding sites for the linear isomers.

The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials

For applications of metal–organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material’s Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework’s intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material’s pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials’ selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

Rational Design of a Low-Cost, High-Performance Metal-Organic Framework for Hydrogen Storage and Carbon Capture

We present the in silico design of a MOF-74 analogue, hereon known as M2(DHFUMA) [M = Mg, Fe, Co, Ni, Zn], with enhanced small-molecule adsorption properties over the original M2(DOBDC) series. Constructed from 2,3-dihydroxyfumarate (DHFUMA), an aliphatic ligand which is smaller than the aromatic 2,5-dioxidobenzene-1,4-dicarboxylate (DOBDC), the M2(DHFUMA) framework has a reduced channel diameter, resulting in higher volumetric density of open metal sites and significantly improved volumetric hydrogen (H2) storage potential. Furthermore, the reduced distance between two adjacent open metal sites in the pore channel leads to a CO2 binding mode of one molecule per two adjacent metals with markedly stronger binding energetics. Through dispersion-corrected density functional theory (DFT) calculations of guest-framework interactions and classical simulation of the adsorption behavior of binary CO2:H2O mixtures, we theoretically predict the M2(DHFUMA) series as an improved alternative for carbon capture over the M2(DOBDC) series when adsorbing from wet flue gas streams. The improved CO2 uptake and humidity tolerance in our simulations is tunable based upon metal selection and adsorption temperature which, combined with the significantly reduced ligand expense, elevates this material's potential for CO2 capture and H2 storage. The dynamical and elastic stabilities of Mg2(DHFUMA) were verified by hybrid DFT calculations, demonstrating its significant potential for experimental synthesis.

Adsorbate-induced lattice deformation in IRMOF-74 series

IRMOF-74 analogues are among the most widely studied metal-organic frameworks (MOFs) for adsorption applications because of their one-dimensional channels and high metal density. Most studies involving the IRMOF-74 series assume that the crystal lattice is rigid. This assumption guides the interpretation of experimental data, as changes in the crystal symmetry have so far been ignored as a possibility in the literature. Here, we report a deformation pattern, induced by the adsorption of argon, for IRMOF-74-V. This work has two main implications. First, we use molecular simulations to demonstrate that the IRMOF-74 series undergoes a deformation that is similar to the mechanism behind breathing MOFs, but is unique because the deformation pattern extends beyond a single unit cell of the original structure. Second, we provide an alternative interpretation of experimental small-angle X-ray scattering profiles of these systems, which changes how we view the fundamentals of adsorption in this MOF series.

Structures of Metal-Organic Frameworks with Rod Secondary Building Units

Rod MOFs are metal-organic frameworks in which the metal-containing secondary building units consist of infinite rods of linked metal-centered polyhedra. For such materials, we identify the points of extension, often atoms, which define the interface between the organic and inorganic components of the structure. The pattern of points of extension defines a shape such as a helix, ladder, helical ribbon, or cylinder tiling. The linkage of these shapes into a three-dimensional framework in turn defines a net characteristic of the original structure. Some scores of rod MOF structures are illustrated and deconstructed into their underlying nets in this way. Crystallographic data for all nets in their maximum symmetry embeddings are provided.