MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules.

Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO2 and CH4 adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs.

Improving Predictions of Spin-Crossover Complex Properties through DFT Calculations with a Local Hybrid Functional.

We conducted a study on the performance of the local hybrid exchange-correlation functional PBE0r for a set of 95 experimentally characterized iron spin-crossover (SCO) complexes [Vennelakanti, V.; J. Chem. Phys. 2023, 159, 024120]. The PBE0r functional is a variant of PBE0 where the exchange correction is restricted to on-site terms formulated on the basis of local orbitals. We determine the free parameters of the PBE0r functional against the experimental data and other hybrid functionals. With a Hartree-Fock (HF) exchange factor of 4%, the PBE0r functional accurately reproduces the electronic and free-energy trends predicted in prior DFT studies for these 95 complexes by using the B3LYP functional. Larger values of HF exchange stabilize high-spin states. The PBE0r-predicted bond lengths tend to exceed the experimental bond lengths, although bond lengths are less sensitive to HF exchange than in global hybrids. The predicted SCO transition temperatures T1/2 from PBE0r correlate moderately with the experimental transition temperatures, showing a slight improvement compared to the previous modB3LYP-predicted T1/2. This study suggests that the PBE0r functional is computationally cost-effective and offers the possibility of simulating larger complexes with accuracy comparable to global hybrid functionals, provided the HF-exchange parameter is carefully optimized.

Caged AIEgens: Multicolor and White Emission Triggered by Mechanical Activation.

Aggregation-induced emission luminogens (AIEgens) that respond to mechanical force are increasingly used as force probes, memory devices, and advanced security systems. Most of the known mechanisms to modulate mechanoresponsive AIEgens have been based on changes in aggregation states, involving only physical alterations. Instances that employ covalent bond cleavage are still rare. We have developed a novel mechanochemical uncaging strategy to unveil AIEgens with diverse emission characteristics using engineered norborn-2-en-7-one (NEO) mechanophores. These NEO mechanophores were covalently integrated into polymer molecules and activated in both the solution and solid states. This activation resulted in highly tunable fluorescence upon immobilization through solidification or aggregation, producing blue, green, yellow, and orange-red emissions. By designing the caged and uncaged forms as donor-acceptor pairs for Förster resonance energy transfer (FRET), we achieved multicolor mechanofluorescence, effectively broadening the color spectrum to include white emission. Additionally, we computationally explored the electronic structures of activated NEOs, providing insights into the observed regiochemical effects of the substituents. This understanding, together with the novel luminogenic characteristics of the caged and activated species, provides a highly tunable reporter that traces progress with continuous color evolution. This advancement paves the way for future applications of mechanoresponsive materials in areas like damage detection and bioimaging.

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes.

The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure-property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21 631 compounds in tmCAT, 4599 in tmPHOTO, 2782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure-property relationships with machine learning.

Enzymatic synthesis of azide by a promiscuous N-nitrosylase.

Azides are energy-rich compounds with diverse representation in a broad range of scientific disciplines, including material science, synthetic chemistry, pharmaceutical science and chemical biology. Despite ubiquitous usage of the azido group, the underlying biosynthetic pathways for its formation remain largely unknown. Here we report the characterization of an enzymatic route for de novo azide construction. We demonstrate that Tri17, a promiscuous ATP- and nitrite-dependent enzyme, catalyses organic azide synthesis through sequential N-nitrosation and dehydration of aryl hydrazines. Through biochemical, structural and computational analyses, we further propose a plausible molecular mechanism for azide synthesis that sets the stage for future biocatalytic applications and biosynthetic pathway engineering.

CH-π Interactions Are Required for Human Galectin-3 Function.

Glycan-binding proteins, or lectins, recognize distinct structural elements of polysaccharides, to mediate myriad biological functions. Targeting glycan-binding proteins involved in human disease has been challenging due to an incomplete understanding of the molecular mechanisms that govern protein-glycan interactions. Bioinformatics and structural studies of glycan-binding proteins indicate that aromatic residues with the potential for CH-π interactions are prevalent in glycan-binding sites. However, the contributions of these CH-π interactions to glycan binding and their relevance in downstream function remain unclear. An emblematic lectin, human galectin-3, recognizes lactose and N-acetyllactosamine-containing glycans by positioning the electropositive face of a galactose residue over the tryptophan 181 (W181) indole forming a CH-π interaction. We generated a suite of galectin-3 W181 variants to assess the importance of these CH-π interactions to glycan binding and function. As determined experimentally and further validated with computational modeling, variants with smaller or less electron-rich aromatic side chains (W181Y, W181F, W181H) or sterically similar but nonaromatic residues (W181M, W181R) showed poor or undetectable binding to lactose and attenuated ability to bind mucins or agglutinate red blood cells. The latter functions depend on multivalent binding, highlighting that weakened CH-π interactions cannot be overcome by avidity. Two galectin-3 variants with disrupted hydrogen bonding interactions (H158A and E184A) showed similarly impaired lactose binding. Molecular simulations demonstrate that all variants have decreased binding orientation stability relative to native galectin-3. Thus, W181 collaborates with the endogenous hydrogen bonding network to enhance binding affinity for lactose, and abrogation of these CH-π interactions is as deleterious as eliminating key hydrogen bonding interactions. These findings underscore the critical roles of CH-π interactions in carbohydrate binding and lectin function and will aid the development of novel lectin inhibitors.

Metal-Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set.

Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve the generality of predictions but require data. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semiautomated manuscript analysis to curate the new data set WS24. The additional data are shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with a previously developed activation stability model in combination with genetic algorithms to quickly screen ∼10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (upon activation, in water, and in acid). We uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment.

Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L612- Nanocage.

Metal-organic cages form well-defined microenvironments that can enhance the catalytic proficiency of encapsulated transition metal complexes (TMCs). We introduce a screening protocol to efficiently identify TMCs that are promising candidates for encapsulation in the Ga4L612- nanocage. We obtain TMCs from the Cambridge Structural Database with geometric and electronic characteristics amenable to encapsulation and mine the text of associated manuscripts to curate TMCs with documented catalytic functionality. By docking candidate TMCs inside the nanocage cavity and carrying out electronic structure calculations, we identify a subset of successfully optimized candidates (TMC-34) and observe that encapsulated guests occupy an average of 60% of the cavity volume, in line with previous observations. Notably, some guests occupy as much as 72% of the cavity as a result of linker rotation. Encapsulation has a universal effect on the electrostatic potential (ESP), systematically decreasing the ESP at the metal center of each TMC in the TMC-34 data set, while minimally altering TMC metal partial charges. Collectively these observations support geometry-based screening of potential guests and suggest that encapsulation in Ga4L612- cages could electrostatically stabilize diverse cationic or electropositive intermediates. We highlight candidate guests with associated known reactivity and solubility most amenable for encapsulation in experimental follow-up studies.

Visible light-mediated aza Paternò-Büchi reaction of acyclic oximes and alkenes to azetidines.

The aza Paternò-Büchi reaction is a [2+2]-cycloaddition reaction between imines and alkenes that produces azetidines, four-membered nitrogen-containing heterocycles. Currently, successful examples rely primarily on either intramolecular variants or cyclic imine equivalents. To unlock the full synthetic potential of aza Paternò-Büchi reactions, it is essential to extend the reaction to acyclic imine equivalents. Here, we report that matching of the frontier molecular orbital energies of alkenes with those of acyclic oximes enables visible light-mediated aza Paternò-Büchi reactions through triplet energy transfer catalysis. The utility of this reaction is further showcased in the synthesis of epi-penaresidin B. Density functional theory computations reveal that a competition between the desired [2+2]-cycloaddition and alkene dimerization determines the success of the reaction. Frontier orbital energy matching between the reactive components lowers transition-state energy (ΔGǂ) values and ultimately promotes reactivity.

Internal Catalysis in Dynamic Hydrogels with Associative Thioester Cross-Links.

Thioesters are an essential functional group in biosynthetic pathways, which has motivated their development as reactive handles in probes and peptide assembly. Thioester exchange is typically accelerated by catalysts or elevated pH. Here, we report the use of bifunctional aromatic thioesters as dynamic covalent cross-links in hydrogels, demonstrating that at physiologic pH in aqueous conditions, transthioesterification facilitates stress relaxation on the time scale of hundreds of seconds. We show that intramolecular hydrogen bonding is responsible for accelerated exchange, evident in both molecular kinetics and macromolecular stress relaxation. Drawing from concepts in the vitrimer literature, this system exemplifies how dynamic cross-links that exchange through an associative mechanism enable tunable stress relaxation without altering stiffness.

Nested non-covalent interactions expand the functions of supramolecular polymer networks.

Supramolecular polymer networks contain non-covalent cross-links that enable access to broadly tunable mechanical properties and stimuli-responsive behaviors; the incorporation of multiple unique non-covalent cross-links within such materials further expands their mechanical responses and functionality. To date, however, the design of such materials has been accomplished through discrete combinations of distinct interaction types in series, limiting materials design logic. Here we introduce the concept of leveraging "nested" supramolecular crosslinks, wherein two distinct types of non-covalent interactions exist in parallel, to control bulk material functions. To demonstrate this concept, we use polymer-linked Pd2L4 metal-organic cage (polyMOC) gels that form hollow metal-organic cage junctions through metal-ligand coordination and can exhibit well-defined host-guest binding within their cavity. In these "nested" supramolecular network junctions, the thermodynamics of host-guest interactions within the junctions affect the metal-ligand interactions that form those junctions, ultimately translating to substantial guest-dependent changes in bulk material properties that could not be achieved in traditional supramolecular networks with multiple interactions in series.

Robust Chemiresistive Behavior in Conductive Polymer/MOF Composites.

Metal-organic frameworks (MOFs) are promising materials for gas sensing but are often limited to single-use detection. A hybridization strategy is demonstrated synergistically deploying conductive MOFs (cMOFs) and conductive polymers (cPs) as two complementary mixed ionic-electronic conductors in high-performing stand-alone chemiresistors. This work presents significant improvement in i) sensor recovery kinetics, ii) cycling stability, and iii) dynamic range at room temperature. The effect of hybridization across well-studied cMOFs is demonstrated based on 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) and 2,3,6,7,10,11-hexaiminotriphenylene (HITP) ligands with varied metal nodes (Co, Cu, Ni). A comprehensive mechanistic study is conducted to relate energy band alignments at the heterojunctions between the MOFs and the polymer with sensing thermodynamics and binding kinetics. The findings reveal that hole enrichment of the cMOF component upon hybridization leads to selective enhancement in desorption kinetics, enabling significantly improved sensor recovery at room temperature, and thus long-term response retention. This mechanism is further supported by density functional theory calculations on sorbate-analyte interactions. It is also found that alloying cPs and cMOFs enables facile thin film co-processing and device integration, potentially unlocking the use of these hybrid conductors in diverse electronic applications.

Angle-strained sila-cycloalkynes.

Second row elements in small- and medium-rings modulate strain. Herein we report the synthesis of two novel oligosilyl-containing cycloalkynes that exhibit angle-strain, as observed by X-ray crystallography. However, the angle-strained sila-cyclooctynes are sluggish participants in cycloadditions with benzyl azide. A distortion-interaction model analysis based on density functional theory calculations was performed.

A Thermally Stable SO2-Releasing Mechanophore: Facile Activation, Single-Event Spectroscopy, and Molecular Dynamic Simulations.

Polymers that release small molecules in response to mechanical force are promising candidates as next-generation on-demand delivery systems. Despite advancements in the development of mechanophores for releasing diverse payloads through careful molecular design, the availability of scaffolds capable of discharging biomedically significant cargos in substantial quantities remains scarce. In this report, we detail a nonscissile mechanophore built from an 8-thiabicyclo[3.2.1]octane 8,8-dioxide (TBO) motif that releases one equivalent of sulfur dioxide (SO2) from each repeat unit. The TBO mechanophore exhibits high thermal stability but is activated mechanochemically using solution ultrasonication in either organic solvent or aqueous media with up to 63% efficiency, equating to 206 molecules of SO2 released per 143.3 kDa chain. We quantified the mechanochemical reactivity of TBO by single-molecule force spectroscopy and resolved its single-event activation. The force-coupled rate constant for TBO opening reaches ∼9.0 s-1 at ∼1520 pN, and each reaction of a single TBO domain releases a stored length of ∼0.68 nm. We investigated the mechanism of TBO activation using ab initio steered molecular dynamic simulations and rationalized the observed stereoselectivity. These comprehensive studies of the TBO mechanophore provide a mechanically coupled mechanism of multi-SO2 release from one polymer chain, facilitating the translation of polymer mechanochemistry to potential biomedical applications.

Improving gas adsorption modeling for MOFs by local calibration of Hubbard U parameters.

While computational screening with density functional theory (DFT) is frequently employed for the screening of metal-organic frameworks (MOFs) for gas separation and storage, commonly applied generalized gradient approximations (GGAs) exhibit self-interaction errors, which hinder the predictions of adsorption energies. We investigate the Hubbard U parameter to augment DFT calculations for full periodic MOFs, targeting a more precise modeling of gas molecule-MOF interactions, specifically for N2, CO2, and O2. We introduce a calibration scheme for the U parameter, which is tailored for each MOF, by leveraging higher-level calculations on the secondary building unit (SBU) of the MOF. When applied to the full periodic MOF, the U parameter calibrated against hybrid HSE06 calculations of SBUs successfully reproduces hybrid-quality calculations of the adsorption energy of the periodic MOF. The mean absolute deviation of adsorption energies reduces from 0.13 eV for a standard GGA treatment to 0.06 eV with the calibrated U, demonstrating the utility of the calibration procedure when applied to the full MOF structure. Furthermore, attempting to use coupled cluster singles and doubles with perturbative triples calculations of isolated SBUs for this calibration procedure shows varying degrees of success in predicting the experimental heat of adsorption. It improves accuracy for N2 adsorption for cases of overbinding, whereas its impact on CO2 is minimal, and ambiguities in spin state assignment hinder consistent improvements of O2 adsorption. Our findings emphasize the limitations of cluster models and advocate the use of full periodic MOF systems with a calibrated U parameter, providing a more comprehensive understanding of gas adsorption in MOFs.

How Do Differences in Electronic Structure Affect the Use of Vanadium Intermediates as Mimics in Nonheme Iron Hydroxylases?

We study active-site models of nonheme iron hydroxylases and their vanadium-based mimics using density functional theory to determine if vanadyl is a faithful structural mimic. We identify crucial structural and energetic differences between ferryl and vanadyl isomers owing to the differences in their ground electronic states, i.e., high spin (HS) for Fe and low spin (LS) for V. For the succinate cofactor bound to the ferryl intermediate, we predict facile interconversion between monodentate and bidentate coordination isomers for ferryl species but difficult rearrangement for vanadyl mimics. We study isomerization of the oxo intermediate between axial and equatorial positions and find the ferryl potential energy surface to be characterized by a large barrier of ca. 10 kcal/mol that is completely absent for the vanadyl mimic. This analysis reveals even starker contrasts between Fe and V in hydroxylases than those observed for this metal substitution in nonheme halogenases. Analysis of the relative bond strengths of coordinating carboxylate ligands for Fe and V reveals that all of the ligands show stronger binding to V than Fe owing to the LS ground state of V in contrast to the HS ground state of Fe, highlighting the limitations of vanadyl mimics of native nonheme iron hydroxylases.

Self-Amplified HF Release and Polymer Deconstruction Cascades Triggered by Mechanical Force.

Hydrogen fluoride (HF) is a versatile reagent for material transformation, with applications in self-immolative polymers, remodeled siloxanes, and degradable polymers. The responsive in situ generation of HF in materials therefore holds promise for new classes of adaptive material systems. Here, we report the mechanochemically coupled generation of HF from alkoxy-gem-difluorocyclopropane (gDFC) mechanophores derived from the addition of difluorocarbene to enol ethers. Production of HF involves an initial mechanochemically assisted rearrangement of gDFC mechanophore to α-fluoro allyl ether whose regiochemistry involves preferential migration of fluoride to the alkoxy-substituted carbon, and ab initio steered molecular dynamics simulations reproduce the observed selectivity and offer insights into the mechanism. When the alkoxy gDFC mechanophore is derived from poly(dihydrofuran), the α-fluoro allyl ether undergoes subsequent hydrolysis to generate 1 equiv of HF and cleave the polymer chain. The hydrolysis is accelerated via acid catalysis, leading to self-amplifying HF generation and concomitant polymer degradation. The mechanically generated HF can be used in combination with fluoride indicators to generate an optical response and to degrade polybutadiene with embedded HF-cleavable silyl ethers (11 mol %). The alkoxy-gDFC mechanophore thus provides a mechanically coupled mechanism of releasing HF for polymer remodeling pathways that complements previous thermally driven mechanisms.

A Semi-Automated, High-Throughput Approach for the Synthesis and Identification of Highly Photo-Cytotoxic Iridium Complexes.

The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise-and-test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well-tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure-activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi-automated synthesise-and-test protocol to obtain multiplex data within 72 hours.

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify.

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes.

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T1/2) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T1/2 values with a Pearson's r of 0.82. In contrast, DFA-predicted T1/2 values have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.