Realizing the data-driven, computational discovery of metal-organic framework catalysts

A hydrophilic porphyrin-based COF to reduce CO2 and nitrite in situ for photoelectrochemical catalysis for the synthesis of urea

The process of industrial synthesis of urea produces high energy consumption and high pollution, which is bad for the environment. Photoelectrocatalytic CO2 reduction to generate urea can avoid the aggravation of environmental pollution, but metal catalysts are commonly used for photoelectrocatalysis, and the use of metal catalysts can lead to environmental heavy metal pollution. Porphyrin-based photoelectrocatalysts with excellent photoelectronic properties were synthesized from aldolized tetrabromophenyl porphyrin (PP), and copolymers of dopamine (DA) and resveratrol (Res). The photoelectrocatalyst (COF_PP-DRS) was synthesized by the Schiff base reaction. The photoelectrocatalyst exhibited good CO2 reduction reaction (CO2R) performance under photoelectrocatalytic conditions with a catalytic efficiency 25% higher than that of PP. The photocatalyst enabled the synthesis of urea (NH2CONH2) from CO2 and NO2-. NH3, HCOOH and CO were also generated in this process. The application of COF_PP-DRS can solve the problem of environmental pollution caused by the industrial synthesis of urea and the use of metal catalysts. The results, combined with infrared data, elucidated the synthesis mechanism of NH2CONH2.

Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation

The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of empirical force fields while retaining a large computational speed-up compared to first-principles calculations. Despite these advancements, the calculation of Hessian matrices for large systems remains challenging, in particular because analytical second-order derivatives are often not implemented. This necessitates the use of computationally expensive finite-difference methods, which can furthermore display low precision in some cases. Automatic differentiation (AD) offers a promising alternative to reduce this computational effort and makes the calculation of Hessian matrices more efficient and accurate. Here, we present the implementation of AD-based second-order derivatives for the popular MACE equivariant graph neural network architecture. The benefits of this method are showcased via a high-throughput prediction of heat capacities of porous materials with the MACE-MP-0 foundation model. This is essential for precisely describing gas adsorption in these systems and was previously possible only with bespoke ML models or expensive first-principles calculations. We find that the availability of foundation models and accurate analytical Hessian matrices offers comparable accuracy to bespoke ML models in a zero-shot manner and additionally allows for the investigation of finite-size and rounding errors in the first-principles data.

Conductive Metal-Organic Frameworks and Their Electrocatalysis Applications

Recently, electrically conductive metal-organic frameworks (EC-MOFs) have emerged as a wealthy library of porous frameworks with unique properties, allowing their use in diverse applications of energy conversion, including electrocatalysis. In this review, the electron conduction mechanisms in EC-MOFs are examined, while their electrical conductivities are considered. There have been various strategies to enhance the conductivities of MOFs including ligand modification, the incorporation of conducting materials, and the construction of multidimensional architectures. With sufficient conductivities being established for EC-MOFs, there have been extensive pursuits in their electrocatalysis applications, such as in the hydrogen evolution reaction, oxygen reduction reaction, oxygen evolution reaction, N2 reduction reaction, and CO2 reduction reaction. In addition, computational modeling of EC-MOFs also exerts an important impact on revealing the synthesis-structure-performance relationships. Finally, the prospects and current challenges are discussed to provide guidelines for designing promising framework materials.

Bibliometric and visualized analysis of metal-organic frameworks in biomedical application

Background: Metal-organic frameworks (MOFs) are hybrid materials composed of metal ions or clusters and organic ligands that spontaneously assemble via coordination bonds to create intramolecular pores, which have recently been widely used in biomedicine due to their porosity, structural, and functional diversity. They are used in biomedical applications, including biosensing, drug delivery, bioimaging, and antimicrobial activities. Our study aims to provide scholars with a comprehensive overview of the research situations, trends, and hotspots in biomedical applications of MOFs through a bibliometric analysis of publications from 2002 to 2022. Methods: On 19 January 2023, the Web of Science Core Collection was searched to review and analyze MOFs applications in the biomedical field. A total of 3,408 studies published between 2002 and 2022 were retrieved and examined, with information such as publication year, country/region, institution, author, journal, references, and keywords. Research hotspots were extracted and analyzed using the Bibliometrix R-package, VOSviewer, and CiteSpace. Results: We showed that researchers from 72 countries published articles on MOFs in biomedical applications, with China producing the most publications. The Chinese Academy of Science was the most prolific contributor to these publications among 2,209 institutions that made contributions. Reference co-citation analysis classifies references into 8 clusters: synergistic cancer therapy, efficient photodynamic therapy, metal-organic framework encapsulation, selective fluorescence, luminescent probes, drug delivery, enhanced photodynamic therapy, and metal-organic framework-based nanozymes. Keyword co-occurrence analysis divided keywords into 6 clusters: biosensors, photodynamic therapy, drug delivery, cancer therapy and bioimaging, nanoparticles, and antibacterial applications. Research frontier keywords were represented by chemodynamic therapy (2020-2022) and hydrogen peroxide (2020-2022). Conclusion: Using bibliometric methods and manual review, this review provides a systematic overview of research on MOFs in biomedical applications, filling an existing gap. The burst keyword analysis revealed that chemodynamic therapy and hydrogen peroxide are the prominent research frontiers and hot spots. MOFs can catalyze Fenton or Fenton-like reactions to generate hydroxyl radicals, making them promising materials for chemodynamic therapy. MOF-based biosensors can detect hydrogen peroxide in various biological samples for diagnosing diseases. MOFs have a wide range of research prospects for biomedical applications.

An Ecosystem for Digital Reticular Chemistry

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.

Electrochemical aptasensor based on the engineered core-shell MOF nanostructures for the detection of tumor antigens

It is essential to develop ultrasensitive biosensors for cancer detection and treatment monitoring. In the development of sensing platforms, metal-organic frameworks (MOFs) have received considerable attention as potential porous crystalline nanostructures. Core-shell MOF nanoparticles (NPs) have shown different diversities, complexities, and biological functionalities, as well as significant electrochemical (EC) properties and potential bio-affinity to aptamers. As a result, the developed core-shell MOF-based aptasensors serve as highly sensitive platforms for sensing cancer biomarkers with an extremely low limit of detection (LOD). This paper aimed to provide an overview of different strategies for improving selectivity, sensitivity, and signal strength of MOF nanostructures. Then, aptamers and aptamers-modified core-shell MOFs were reviewed to address their functionalization and application in biosensing platforms. Additionally, the application of core-shell MOF-assisted EC aptasensors for detection of several tumor antigens such as prostate-specific antigen (PSA), carbohydrate antigen 15-3 (CA15-3), carcinoembryonic antigen (CEA), human epidermal growth factor receptor-2 (HER2), cancer antigen 125 (CA-125), cytokeratin 19 fragment (CYFRA21-1), and other tumor markers were discussed. In conclusion, the present article reviews the advancement of potential biosensing platforms toward the detection of specific cancer biomarkers through the development of core-shell MOFs-based EC aptasensors.

Role of Counterions in the Structural Stabilisation of Redox-Active Metal-Organic Frameworks

The crystal structures of metal-organic frameworks (MOFs) are typically determined by the strong chemical bonds formed between the organic and inorganic building units. However, the latest generation of redox-active frameworks often rely on counterions in the pores to access specific charge states of the components. Here, we model the crystal structures of three layered MOFs based on the redox-active ligand 2,5-dihydroxybenzoquinone (dhbq): Ti2 (Cl2 dhbq)3 , V2 (Cl2 dhbq)3 and Fe2 (Cl2 dhbq)3 with implicit and explicit counterions. Our full-potential first-principles calculations indicate that while the reported hexagonal structure is readily obtained for Ti and V, the Fe framework is stabilised only by the presence of explicit counterions. For high counterion concentrations, we observe the formation of an electride-like pocket in the pore center. An outlook is provided on the implications of solvent and counterion control for engineering the structures and properties of porous solids.

Connecting the dots for fundamental understanding of structure-photophysics-property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism

Photoactive organic and hybrid organic-inorganic materials such as conjugated polymers, covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and layered perovskites, display intriguing photophysical signatures upon interaction with light. Elucidating structure-photophysics-property relationships across a broad range of functional materials is nontrivial and requires our fundamental understanding of the intricate interplay among excitons (electron-hole pair), polarons (charges), bipolarons, phonons (vibrations), inter-layer stacking interactions, and different forms of structural and conformational defects. In parallel with electronic structure modeling and data-driven science that are actively pursued to successfully accelerate materials discovery, an accurate, computationally inexpensive, and physically-motivated theoretical model, which consistently makes quantitative connections with conceptually complicated experimental observations, is equally important. Within this context, the first part of this perspective highlights a unified theoretical framework in which the electronic coupling as well as the local coupling between the electronic and nuclear degrees of freedom can be efficiently described for a broad range of quasiparticles with similarly structured Holstein-style vibronic Hamiltonians. The second part of this perspective discusses excitonic and polaronic photophysical signatures in polymers, COFs, MOFs, and perovskites, and attempts to bridge the gap between different research fields using a common theoretical construct - the Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art computational approaches with the Multiparticle Holstein Formalism will help identify and establish new, transformative design strategies that will guide the synthesis and characterization of next-generation energy materials optimized for a broad range of optoelectronic, spintronic, and photonic applications.

Synergistic Effect of FeII and MnII Ions in Cyano-Bridged Heterometallic Coordination Polymers on Catalytic Selectivity of Benzene Oxygenation to Phenol

A cyano-bridged heterometallic coordination polymer with partial deficiencies of CN^- ligands, [Mn^II(H₂O)_8/3]_3/2[Fe^II(CN)₅(NH₃)], forms open metal sites both on Mn^II and Fe^II ions by liberation of monodentate ligands such as NH₃ and H₂O. [Mn^II(H₂O)_8/3]_3/2[Fe^II(CN)₅(NH₃)] exhibits high catalytic activity and selectivity of benzene oxygenation to phenol in the presence of m-chloroperoxybenzoic acid as an oxidant. The postcatalytic spectroscopy of [Mn^II(H₂O)_8/3]_3/2[Fe^II(CN)₅(NH₃)] and catalysis comparison with a physical mixture of [Mn^II(H₂O)₃]₂[Fe^II(CN)₆] and [Fe(H₂O)_3/2]_4/3[Fe(CN)₆], which has open metal sites on both Mn^II and Fe ions separately, indicated that the high activity resulted from high oxidation ability and phenol adsorption ability of Fe^II and Mn^II ions, respectively.

A data-science approach to predict the heat capacity of nanoporous materials

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal-organic frameworks and covalent-organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH₃I); as a primary representative of organic iodides, CH₃¹²⁹I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH₃I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.