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.

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.

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.

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.

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.

Classification of Hemilabile Ligands Using Machine Learning.

Hemilabile ligands have the capacity to partially disengage from a metal center, providing a strategy to balance stability and reactivity in catalysis, but they are not straightforward to identify. We identify ligands in the Cambridge Structural Database that have been crystallized with distinct denticities and are thus identifiable as hemilabile ligands. We implement a semi-supervised learning approach using a label-spreading algorithm to augment a small negative set that is supported by heuristic rules of ligand and metal co-occurrence. We show that a heuristic based on coordinating atom identity alone is not sufficient to identify whether a ligand is hemilabile, and our trained machine-learning classification models are instead needed to predict whether a bi-, tri-, or tetradentate ligand is hemilabile with high accuracy and precision. Feature importance analysis of our models shows that the second, third, and fourth coordination spheres all play important roles in ligand hemilability.

Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning.

The design of ion-selective materials with improved separation efficacy and efficiency is paramount, as current technologies fail to meet real-world deployment challenges. Selectivity in these materials can be informed by local ion binding in confined membrane ion channels. In this study, we utilize a data-driven approach to investigate design features in small molecular complexes coordinating ions as simplified models of ion channels. We curate a data set of 563 alkali metal coordinating molecular complexes (i.e., with Li+, Na+, or K+) from the Cambridge Structural Database and calculate differential ion binding energies using density functional theory. Using this information, we probe when and why structures favor exchange with alternate ions. Our analysis reveals that energetic preferences are related to ion size but are largely due to chemical interactions rather than structural reorganization. We identify unique trends in the selectivity for Li+ over other alkali ions, including the presence of N coordination atoms, planar coordination geometry, and small coordinating ring sizes. We use machine learning models to identify the key contributions of both geometric and electronic features in predicting selective ion binding. These physical insights offer preliminary guidance into the design of optimal membranes for ion selectivity.

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets.

With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet to be established and different studies on uncertainties generally uses different metrics to evaluate them. We compare three of the most popular validation metrics (Spearman's rank correlation coefficient, the negative log likelihood (NLL) and the miscalibration area) to the error-based calibration introduced by Levi et al. (Sensors 2022, 22, 5540). Importantly, metrics such as the negative log likelihood (NLL) and Spearman's rank correlation coefficient bear little information in themselves. We therefore introduce reference values obtained through errors simulated directly from the uncertainty distribution. The different metrics target different properties and we show how to interpret them, but we generally find the best overall validation to be done based on the error-based calibration plot introduced by Levi et al. Finally, we illustrate the sensitivity of ranking-based methods (e.g. Spearman's rank correlation coefficient) towards test set design by using the same toy model ferent test sets and obtaining vastly different metrics (0.05 vs. 0.65).

Why Nonheme Iron Halogenases Do Not Fluorinate C-H Bonds: A Computational Investigation.

Selective halogenation is necessary for a range of fine chemical applications, including the development of therapeutic drugs. While synthetic processes to achieve C-H halogenation require harsh conditions, enzymes such as nonheme iron halogenases carry out some types of C-H halogenation, i.e., chlorination or bromination, with ease, while others, i.e., fluorination, have never been observed in natural or engineered nonheme iron enzymes. Using density functional theory and correlated wave function theory, we investigate the differences in structural and energetic preferences of the smaller fluoride and the larger chloride or bromide intermediates throughout the catalytic cycle. Although we find that the energetics of rate-limiting hydrogen atom transfer are not strongly impacted by fluoride substitution, the higher barriers observed during the radical rebound reaction for fluoride relative to chloride and bromide contribute to the difficulty of C-H fluorination. We also investigate the possibility of isomerization playing a role in differences in reaction selectivity, and our calculations reveal crucial differences in terms of isomer energetics of the key ferryl intermediate between fluoride and chloride/bromide intermediates. While formation of monodentate isomers believed to be involved in selective catalysis is shown for chloride and bromide intermediates, we find that formation of the fluoride monodentate intermediate is not possible in our calculations, which lack additional stabilizing interactions with the greater protein environment. Furthermore, the shorter Fe-F bonds are found to increase isomerization reaction barriers, suggesting that incorporation of residues that form a halogen bond with F and elongate Fe-F bonds could make selective C-H fluorination possible in nonheme iron halogenases. Our work highlights the differences between the fluoride and chloride/bromide intermediates and suggests potential steps toward engineering nonheme iron halogenases to enable selective C-H fluorination.

Tailoring Dynamic Hydrogels by Controlling Associative Exchange Rates.

Dithioalkylidenes are a newly-developed class of conjugate acceptors that undergo thiol exchange via an associative mechanism, enabling decoupling of key material properties for sustainability, biomedical, and sensing applications. Here, we show that the exchange rate is highly sensitive to the structure of the acceptor and tunable over four orders of magnitude in aqueous environments. Cyclic acceptors exchange rapidly, from 0.95 to 15.6 M-1s-1, while acyclic acceptors exchange between 3.77x10-3 and 2.17x10-2 M-1s-1. Computational, spectroscopic, and structural data suggest that cyclic acceptors are more reactive than their acyclic counterparts because of resonance stabilization of the tetrahedral exchange intermediate. We parametrize molecular reactivity with respect to computed descriptors of the electrophilic site and leverage this insight to design a compound with intermediate characteristics. Lastly, we incorporate this dynamic bond into hydrogels and demonstrate that the characteristic stress relaxation time (τ) is directly proportional to molecular kex.

Gas-phase and solid-state electronic structure analysis and DFT benchmarking of HfCO.

Ab initio multi-reference configuration interaction (MRCI) and coupled cluster singles doubles and perturbative triples [CCSD(T)] levels of theory were used to study ground and excited electronic states of HfCO. We report potential energy curves, dissociation energies (De), excitation energies, harmonic vibrational frequencies, and chemical bonding patterns of HfCO. The 3Σ- ground state of HfCO has an 1σ22σ21π2 electron configuration and a ∼30 kcal mol-1 dissociation energy with respect to its lowest-energy fragments Hf(3F) + CO(X1Σ+). We further evaluated the De of its isovalent HfCX (X = S, Se, Te, Po) series and observed that they increase linearly from the lighter HfCO to the heavier HfCPo with the dipole moment of the CX ligand. The same linear relationship was observed for TiCX and ZrCX. We utilized the CCSD(T) benchmark values of De, excitation energy, and ionization energy (IE) values to evaluate density functional theory (DFT) errors with 23 exchange-correlation functionals spanning GGA, meta-GGA, global GGA hybrid, meta-GGA hybrid, range-separated hybrid, and double-hybrid functional families. The global GGA hybrid B3LYP and range-separated hybrid ωB97X performed well at representing the ground state properties of HfCO (i.e., De and IE). Finally, we extended our DFT analysis to the interaction of a CO molecule with a Hf surface and observed that the surface chemisorption energy and the gas-phase molecular dissociation energy are very similar for some DFAs but not others, suggesting moderate transferability of the benchmarks on these molecules to the solid state.

Polymer Networks with Cubic, Mixed Pd(II) and Pt(II) M6L12 Metal-Organic Cage Junctions: Synthesis and Stress Relaxation Behavior.

Metal-organic cages/polyhedra (MOCs) are versatile building blocks for advanced polymer networks with properties that synergistically blend those of traditional polymers and crystalline frameworks. Nevertheless, constructing polyMOCs from very stable Pt(II)-based MOCs or mixtures of metal ions such as Pd(II) and Pt(II) has not, to our knowledge, been demonstrated, nor has exploration of how the dynamics of metal-ligand exchange at the MOC level may impact bulk polyMOC energy dissipation. Here, we introduce a new class of polymer metal-organic cage (polyMOC) gels featuring polyethylene glycol (PEG) strands of varied length cross-linked through bis-pyridyl-carbazole-based M6L12 cubes, where M is Pd(II), Pt(II), or mixtures thereof. We show that, while polyMOCs with varied Pd(II) content have similar network structures, their average stress-relaxation rates are tunable over 3 orders of magnitude due to differences in Pd(II)- and Pt(II)-ligand exchange rates at the M6L12 junction level. Moreover, mixed-metal polyMOCs display relaxation times indicative of intrajunction cooperative interactions, which stands in contrast to previous materials based on point metal junctions. Altogether, this work (1) introduces a novel MOC architecture for polyMOC design, (2) shows that polyMOCs can be prepared from mixtures of Pd(II)/Pt(II), and (3) demonstrates that polyMOCs display unique relaxation behavior due to their multivalent junctions, offering a strategy for controlling polyMOC properties independently of their polymer components.

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes.

Spin crossover (SCO) complexes, which exhibit changes in spin state in response to external stimuli, have applications in molecular electronics and are challenging materials for computational design. We curate a dataset of 95 Fe(II) SCO complexes (SCO-95) from the Cambridge Structural Database that have available low- and high-temperature crystal structures and, in most cases, confirmed experimental spin transition temperatures (T1/2). We study these complexes using density functional theory (DFT) with 30 functionals spanning across multiple rungs of "Jacob's ladder" to understand the effect of exchange-correlation functional on electronic and Gibbs free energies associated with spin crossover. We specifically assess the effect of varying the Hartree-Fock exchange fraction (aHF) in structures and properties within the B3LYP family of functionals. We identify three best-performing functionals, a modified version of B3LYP (aHF = 0.10), M06-L, and TPSSh, that accurately predict SCO behavior for the majority of the complexes. While M06-L performs well, MN15-L, a more recently developed Minnesota functional, fails to predict SCO behavior for all complexes, which could be the result of differences in datasets used for parametrization of M06-L and MN15-L and also the increased number of parameters for MN15-L. Contrary to observations from prior studies, double-hybrids with higher aHF values are found to strongly stabilize high-spin states and therefore exhibit poor performance in predicting SCO behavior. Computationally predicted T1/2 values are consistent among the three functionals but show limited correlation to experimentally reported T1/2 values. These failures are attributed to the lack of crystal packing effects and counter-anions in the DFT calculations that would be needed to account for phenomena such as hysteresis and two-step SCO behavior. The SCO-95 set thus presents opportunities for method development, both in terms of increasing model complexity and method fidelity.

Reversible O-O Bond Scission and O2 Evolution at MOF-Supported Tetramanganese Clusters.

The scission of the O-O bond in O2 during respiration and the formation of the O-O bond during photosynthesis are the engines of aerobic life. Likewise, the reduction of O2 and the oxidation of reduced oxygen species to form O2 are indispensable components for emerging renewable technologies, including energy storage and conversion, yet discrete molecule-like systems that promote these fundamental reactions are rare. Herein, we report a square-planar tetramanganese cluster formed by self-assembly within a metal-organic framework that reversibly reduces O2 by four electrons, facilitating the interconversion between molecular O2 and metal-oxo species. The tetranuclear cluster spontaneously cleaves the O-O bond of O2 at room temperature to generate a tetramanganese-bis(µ2-oxo) species, which, in turn, is competent for O-O bond reformation and O2 evolution at elevated temperatures, enabled by the head-to-head orientation of two oxo species. This study demonstrates the viability of four-electron interconversion between molecular O2 and metal-oxo species and highlights the importance of site isolation for achieving multi-electron chemistry at polynuclear metal clusters.