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.

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.

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. We demonstrate a hybridization strategy synergistically deploying conductive MOFs (cMOFs) and conductive polymers (cPs) as two complementary mixed ionic-electronic conductors in high-performing stand-alone chemiresistors. Our work presents significant improvement in i) sensor recovery kinetics, ii) cycling stability, and iii) dynamic range at room temperature. We demonstrate the effect of hybridization across well-studied cMOFs based on 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) and 2,3,6,7,10,11-hexaiminotripphenylene (HITP) ligands with varied metal nodes (Co, Cu, Ni). We conduct a comprehensive mechanistic study to relate energy band alignments at the heterojunctions between the MOFs and the polymer with sensing thermodynamics and binding kinetics. Our 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 was further supported by density functional theory calculations on sorbate-analyte interactions. We also find 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. This article is protected by copyright. All rights reserved.

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.

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.

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.

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.

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).

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.

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.

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.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures-reactant, transition state and product-in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol-1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.

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.

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.

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.

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.

Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes

We survey more than 240 000 crystallized mononuclear transition metal complexes (TMCs) to identify trends in preferred geometric structure and metal coordination. While we observe that an increased level of d filling correlates with a lower coordination number preference, we note exceptions, and we observe undersampling of 4d/5d transition metals and 3p-coordinating ligands. For the one-third of mononuclear TMCs that are octahedral, analysis of the 67 symmetry classes of their ligand environments reveals that complexes often contain monodentate ligands that may be removable, forming an open site amenable to catalysis. Due to their use in catalysis, we analyze trends in coordination by tetradentate ligands in terms of the capacity to support multiple metals and the variability of coordination geometry. We identify promising tetradentate ligands that co-occur in crystallized complexes with labile monodentate ligands that would lead to reactive sites. Literature mining suggests that these ligands are untapped as catalysts, motivating proposal of a promising octa-functionalized porphyrin.

Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis

The challenge of direct partial oxidation of methane to methanol has motivated the targeted search of metal-organic frameworks (MOFs) as a promising class of materials for this transformation because of their site-isolated metals with tunable ligand environments. Thousands of MOFs have been synthesized, yet relatively few have been screened for their promise in methane conversion. We developed a high-throughput virtual screening workflow that identifies MOFs from a diverse space of experimental MOFs that have not been studied for catalysis, yet are thermally stable, synthesizable, and have promising unsaturated metal sites for C-H activation via a terminal metal-oxo species. We carried out density functional theory calculations of the radical rebound mechanism for methane-to-methanol conversion on models of the secondary building units (SBUs) from 87 selected MOFs. While we showed that oxo formation favorability decreases with increasing 3d filling, consistent with prior work, previously observed scaling relations between oxo formation and hydrogen atom transfer (HAT) are disrupted by the greater diversity in our MOF set. Accordingly, we focused on Mn MOFs, which favor oxo intermediates without disfavoring HAT or leading to high methanol release energies─a key feature for methane hydroxylation activity. We identified three Mn MOFs comprising unsaturated Mn centers bound to weak-field carboxylate ligands in planar or bent geometries with promising methane-to-methanol kinetics and thermodynamics. The energetic spans of these MOFs are indicative of promising turnover frequencies for methane to methanol that warrant further experimental catalytic studies.

Synthesis and Ring-Opening Metathesis Polymerization of a Strained trans-Silacycloheptene and Single-Molecule Mechanics of Its Polymer

The cis- and trans-isomers of a silacycloheptene were selectively synthesized by the alkylation of a silyl dianion, a novel approach to strained cycloalkenes. The trans-silacycloheptene (trans-SiCH) was significantly more strained than the cis isomer, as predicted by quantum chemical calculations and confirmed by crystallographic signatures of a twisted alkene. Each isomer exhibited distinct reactivity toward ring-opening metathesis polymerization (ROMP), where only trans-SiCH afforded high-molar-mass polymer under enthalpy-driven ROMP. Hypothesizing that the introduction of silicon might result in increased molecular compliance at large extensions, we compared poly(trans-SiCH) to organic polymers by single-molecule force spectroscopy (SMFS). Force-extension curves from SMFS showed that poly(trans-SiCH) is more easily overstretched than two carbon-based analogues, polycyclooctene and polybutadiene, with stretching constants that agree well with the results of computational simulations.

Fluids and Electrolytes under Confinement in Single-Digit Nanopores

Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.

Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models

Virtual high-throughput screening (VHTS) and machine learning (ML) with density functional theory (DFT) suffer from inaccuracies from the underlying density functional approximation (DFA). Many of these inaccuracies can be traced to the lack of derivative discontinuity that leads to a curvature in the energy with electron addition or removal. Over a dataset of nearly one thousand transition metal complexes typical of VHTS applications, we computed and analyzed the average curvature (i.e., deviation from piecewise linearity) for 23 density functional approximations spanning multiple rungs of "Jacob's ladder". While we observe the expected dependence of the curvatures on Hartree-Fock exchange, we note limited correlation of curvature values between different rungs of "Jacob's ladder". We train ML models (i.e., artificial neural networks or ANNs) to predict the curvature and the associated frontier orbital energies for each of these 23 functionals and then interpret differences in curvature among the different DFAs through analysis of the ML models. Notably, we observe spin to play a much more important role in determining the curvature of range-separated and double hybrids in comparison to semi-local functionals, explaining why curvature values are weakly correlated between these and other families of functionals. Over a space of 187.2k hypothetical compounds, we use our ANNs to pinpoint DFAs for which representative transition metal complexes have near-zero curvature with low uncertainty, demonstrating an approach to accelerate screening of complexes with targeted optical gaps.

1D Hybrid Semiconductor Silver 2,6-Difluorophenylselenolate

Organic-inorganic hybrid materials present new opportunities for creating low-dimensional structures with unique light-matter interaction. In this work, we report a chemically robust yellow emissive one-dimensional (1D) semiconductor, silver 2,6-difluorophenylselenolate─AgSePhF₂(2,6), a new member of the broader class of hybrid low-dimensional semiconductors, metal-organic chalcogenolates. While silver phenylselenolate (AgSePh) crystallizes as a two-dimensional (2D) van der Waals semiconductor, introduction of fluorine atoms at the (2,6) position of the phenyl ring induces a structural transition from 2D sheets to 1D chains. Density functional theory calculations reveal that AgSePhF₂ (2,6) has strongly dispersive conduction and valence bands along the 1D crystal axis. Visible photoluminescence centered around λ_p ≈ 570 nm at room temperature exhibits both prompt (110 ps) and delayed (36 ns) components. The absorption spectrum exhibits excitonic resonances characteristic of low-dimensional hybrid semiconductors, with an exciton binding energy of approximately 170 meV as determined by temperature-dependent photoluminescence. The discovery of an emissive 1D silver organoselenolate highlights the structural and compositional richness of the chalcogenolate material family and provides new insights for molecular engineering of low-dimensional hybrid organic-inorganic semiconductors.

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

Transition-metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and nontoxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have well-defined ground states and optimal target absorption energies in the visible region. Machine learning (ML) accelerated discovery could overcome such challenges by enabling the screening of a larger space but is limited by the fidelity of the data used in ML model training, which is typically from a single approximate density functional. To address this limitation, we search for consensus in predictions among 23 density functional approximations across multiple rungs of "Jacob's ladder". To accelerate the discovery of complexes with absorption energies in the visible region while minimizing the effect of low-lying excited states, we use two-dimensional (2D)efficient global optimization to sample candidate low-spin chromophores from multimillion complex spaces. Despite the scarcity (i.e., ∼0.01%) of potential chromophores in this large chemical space, we identify candidates with high likelihood (i.e., >10%) of computational validation as the ML models improve during active learning, representing a 1000-fold acceleration in discovery. Absorption spectra of promising chromophores from time-dependent density functional theory verify that 2/3 of candidates have the desired excited-state properties. The observation that constituent ligands from our leads have demonstrated interesting optical properties in the literature exemplifies the effectiveness of our construction of a realistic design space and active learning approach.

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors

Prediction of the excited state properties of photoactive iridium complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from the perspective of accuracy and of computational cost, complicating high-throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models and experimental data for 1380 iridium complexes to perform these prediction tasks. We find the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional tight binding calculations. Using artificial neural network (ANN) models, we predict the mean emission energy of phosphorescence, the excited state lifetime, and the emission spectral integral for iridium complexes with accuracy competitive with or superseding that of TDDFT. We conduct feature importance analysis to determine that high cyclometalating ligand ionization potential correlates to high mean emission energy, while high ancillary ligand ionization potential correlates to low lifetime and low spectral integral. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and use uncertainty-controlled predictions to identify promising ligands for the design of new phosphors while retaining confidence in the quality of the ANN predictions.

Emergence of a proton exchange-based isomerization and lactonization mechanism in the plant coumarin synthase COSY

Plants contain rapidly evolving specialized enzymes that support the biosynthesis of functionally diverse natural products. In coumarin biosynthesis, a BAHD acyltransferase-family enzyme COSY was recently discovered to accelerate coumarin formation as the only known BAHD enzyme to catalyze an intramolecular acyl transfer reaction. Here we investigate the structural and mechanistic basis for COSY's coumarin synthase activity. Our structural analyses reveal an unconventional active-site configuration adapted to COSY's specialized activity. Through mutagenesis studies and deuterium exchange experiments, we identify a unique proton exchange mechanism at the α-carbon of the o-hydroxylated trans-hydroxycinnamoyl-CoA substrates during the catalytic cycle of COSY. Quantum mechanical cluster modeling and molecular dynamics further support this key mechanism for lowering the activation energy of the rate-limiting trans-to-cis isomerization step in coumarin production. This study unveils an unconventional catalytic mechanism mediated by a BAHD-family enzyme, and sheds light on COSY's evolutionary origin and its recruitment to coumarin biosynthesis in eudicots.

Remolding and Deconstruction of Industrial Thermosets via Carboxylic Acid-Catalyzed Bifunctional Silyl Ether Exchange

Convenient strategies for the deconstruction and reprocessing of thermosets could improve the circularity of these materials, but most approaches developed to date do not involve established, high-performance engineering materials. Here, we show that bifunctional silyl ether, i.e., R'O-SiR₂-OR'', (BSE)-based comonomers generate covalent adaptable network analogues of the industrial thermoset polydicyclopentadiene (pDCPD) through a novel BSE exchange process facilitated by the low-cost food-safe catalyst octanoic acid. Experimental studies and density functional theory calculations suggest an exchange mechanism involving silyl ester intermediates with formation rates that strongly depend on the Si-R₂ substituents. As a result, pDCPD thermosets manufactured with BSE comonomers display temperature- and time-dependent stress relaxation as a function of their substituents. Moreover, bulk remolding of pDCPD thermosets is enabled for the first time. Altogether, this work presents a new approach toward the installation of exchangeable bonds into commercial thermosets and establishes acid-catalyzed BSE exchange as a versatile addition to the toolbox of dynamic covalent chemistry.

DFT-Based Multireference Diagnostics in the Solid State: Application to Metal-Organic Frameworks

When a many-body wave function of a system cannot be captured by a single determinant, high-level multireference (MR) methods are required to properly explain its electronic structure. MR diagnostics to estimate the magnitude of such static correlation have been primarily developed for molecular systems and range from low in computational cost to as costly as the full MR calculation itself. We report the first application of low-cost MR diagnostics based on the fractional occupation number calculated with finite-temperature DFT to solid-state systems. To compare the behavior of the diagnostics on solids and molecules, we select metal-organic frameworks (MOFs) as model materials because their reticular nature provides an intuitive way to identify molecular derivatives. On a series of closed-shell MOFs, we demonstrate that the DFT-based MR diagnostics are equally applicable to solids as to their molecular derivatives. The magnitude and spatial distribution of the MR character of a MOF are found to have a good correlation with those of its molecular derivatives, which can be calculated much more affordably in comparison to those of the full MOF. The additivity of MR character discussed here suggests the set of molecular derivatives to be a good representation of a MOF for both MR detection and ultimately for MR corrections, facilitating accurate and efficient high-throughput screening of MOFs and other porous solids.

A transferable recommender approach for selecting the best density functional approximations in chemical discovery

Approximate density functional theory has become indispensable owing to its balanced cost-accuracy trade-off, including in large-scale screening. To date, however, no density functional approximation (DFA) with universal accuracy has been identified, leading to uncertainty in the quality of data generated from density functional theory. With electron density fitting and Δ-learning, we build a DFA recommender that selects the DFA with the lowest expected error with respect to the gold standard (but cost-prohibitive) coupled cluster theory in a system-specific manner. We demonstrate this recommender approach on the evaluation of vertical spin splitting energies of transition metal complexes. Our recommender predicts top-performing DFAs and yields excellent accuracy (about 2 kcal mol-1) for chemical discovery, outperforming both individual Δ-learning models and the best conventional single-functional approach from a set of 48 DFAs. By demonstrating transferability to diverse synthesized compounds, our recommender potentially addresses the accuracy versus scope dilemma broadly encountered in computational chemistry.

Light Emission in 2D Silver Phenylchalcogenolates

Silver phenylselenolate (AgSePh, also known as "mithrene") and silver phenyltellurolate (AgTePh, also known as "tethrene") are two-dimensional (2D) van der Waals semiconductors belonging to an emerging class of hybrid organic-inorganic materials called metal-organic chalcogenolates. Despite having the same crystal structure, AgSePh and AgTePh exhibit a strikingly different excitonic behavior. Whereas AgSePh exhibits narrow, fast luminescence with a minimal Stokes shift, AgTePh exhibits comparatively slow luminescence that is significantly broadened and red-shifted from its absorption minimum. Using time-resolved and temperature-dependent absorption and emission microspectroscopy, combined with subgap photoexcitation studies, we show that exciton dynamics in AgTePh films are dominated by an intrinsic self-trapping behavior, whereas dynamics in AgSePh films are dominated by the interaction of band-edge excitons with a finite number of extrinsic defect/trap states. Density functional theory calculations reveal that AgSePh has simple parabolic band edges with a direct gap at Γ, whereas AgTePh has a saddle point at Γ with a horizontal splitting along the Γ-N₁ direction. The correlation between the unique band structure of AgTePh and exciton self-trapping behavior is unclear, prompting further exploration of excitonic phenomena in this emerging class of hybrid 2D semiconductors.

Ligand additivity relationships enable efficient exploration of transition metal chemical space

To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuclear octahedral transition metal complexes deposited in an experimental database confirms an under-representation of lower-symmetry complexes. From a set of around 1000 previously studied Fe(II) complexes, we show that the theoretical space of synthetically accessible complexes formed from the relatively small number of unique ligands is significantly (ca. 816k) larger. For the properties of these complexes, we validate the concept of ligand additivity by inferring heteroleptic properties from a stoichiometric combination of homoleptic complexes. An improved interpolation scheme that incorporates information about cis and trans isomer effects predicts the adiabatic spin-splitting energy to around 2 kcal/mol and the HOMO level to less than 0.2 eV. We demonstrate a multi-stage strategy to discover leads from the 816k Fe(II) complexes within a targeted property region. We carry out a coarse interpolation from homoleptic complexes that we refine over a subspace of ligands based on the likelihood of generating complexes with targeted properties. We validate our approach on 9 new binary and ternary complexes predicted to be in a targeted zone of discovery, suggesting opportunities for efficient transition metal complex discovery.

Non-Native Anionic Ligand Binding and Reactivity in Engineered Variants of the Fe(II)- and α-Ketoglutarate-Dependent Oxygenase, SadA

Mononuclear non-heme Fe(II)- and α-ketoglutarate-dependent oxygenases (FeDOs) catalyze a site-selective C-H hydroxylation. Variants of these enzymes in which a conserved Asp/Glu residue in the Fe(II)-binding facial triad is replaced by Ala/Gly can, in some cases, bind various anionic ligands and catalyze non-native chlorination and bromination reactions. In this study, we explore the binding of different anions to an FeDO facial triad variant, SadX, and the effects of that binding on HO• vs X• rebound. We establish not only that chloride and bromide enable non-native halogenation reactions but also that all anions investigated, including azide, cyanate, formate, and fluoride, significantly accelerate and influence the site selectivity of SadX hydroxylation catalysis. Azide and cyanate also lead to the formation of products resulting from N3•, NCO•, and OCN• rebound. While fluoride rebound is not observed, the rate acceleration provided by this ligand leads us to calculate barriers for HO• and F• rebound from a putative Fe(III)(OH)(F) intermediate. These calculations suggest that the lack of fluorination is due to the relative barriers of the HO• and F• rebound transition states rather than an inaccessible barrier for F• rebound. Together, these results improve our understanding of the FeDO facial triad variant tolerance of different anionic ligands, their ability to promote rebound involving these ligands, and inherent rebound preferences relative to HO• that will aid efforts to develop non-native catalysis using these enzymes.

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands

Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multireference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized octahedral mononuclear transition metal complexes in the Cambridge Structural Database (CSD). To accomplish this task, we introduce an iterative approach for consistent ligand charge assignment for ligands in the CSD. Across this set, we observe that the MR character correlates linearly with the inverse value of the averaged bond order over all bonds in the molecule. We then demonstrate that ligand additivity of the MR character holds in TMCs, which suggests that the TMC MR character can be inferred from the sum of the MR character of the ligands. Encouraged by this observation, we leverage ligand additivity and develop a ligand-derived machine learning representation to train neural networks to predict the MR character of TMCs from properties of the constituent ligands. This approach yields models with excellent performance and superior transferability to unseen ligand chemistry and compositions.

Endohedrally Functionalized Metal-Organic Cage-Cross-Linked Polymer Gels as Modular Heterogeneous Catalysts

The immobilization of homogeneous catalysts onto supports to improve recyclability while maintaining catalytic efficiency is often a trial-and-error process limited by poor control of the local catalyst environment and few strategies to append catalysts to support materials. Here, we introduce a modular heterogenous catalysis platform that addresses these challenges. Our approach leverages the well-defined interiors of self-assembled Pd₁₂L₂₄ metal-organic cages/polyhedra (MOCs): simple mixing of a catalyst-ligand of choice with a polymeric ligand, spacer ligands, and a Pd salt induces self-assembly of Pd₁₂L₂₄-cross-linked polymer gels featuring endohedrally catalyst-functionalized junctions. Semi-empirical calculations show that catalyst incorporation into the MOC junctions of these materials has minimal affect on the MOC geometry, giving rise to well-defined nanoconfined catalyst domains as confirmed experimentally using several techniques. Given the unique network topology of these freestanding gels, they are mechanically robust regardless of their endohedral catalyst composition, allowing them to be physically manipulated and transferred from one reaction to another to achieve multiple rounds of catalysis. Moreover, by decoupling the catalyst environment (interior of MOC junctions) from the physical properties of the support (the polymer matrix), this strategy enables catalysis in environments where homogeneous catalyst analogues are not viable, as demonstrated for the Au(I)-catalyzed cyclization of 4-pentynoic acid in aqueous media.

Mechanistic Studies of a Skatole-Forming Glycyl Radical Enzyme Suggest Reaction Initiation via Hydrogen Atom Transfer

Gut microbial decarboxylation of amino acid-derived arylacetates is a chemically challenging enzymatic transformation which generates small molecules that impact host physiology. The glycyl radical enzyme (GRE) indoleacetate decarboxylase from Olsenella uli (Ou IAD) performs the non-oxidative radical decarboxylation of indole-3-acetate (I3A) to yield skatole, a disease-associated metabolite produced in the guts of swine and ruminants. Despite the importance of IAD, our understanding of its mechanism is limited. Here, we characterize the mechanism of Ou IAD, evaluating previously proposed hypotheses of: (1) a Kolbe-type decarboxylation reaction involving an initial 1-e^– oxidation of the carboxylate of I3A or (2) a hydrogen atom abstraction from the α-carbon of I3A to generate an initial carbon-centered radical. Site-directed mutagenesis, kinetic isotope effect experiments, analysis of reactions performed in D₂O, and computational modeling are consistent with a mechanism involving initial hydrogen atom transfer. This finding expands the types of radical mechanisms employed by GRE decarboxylases and non-oxidative decarboxylases, more broadly. Elucidating the mechanism of IAD decarboxylation enhances our understanding of radical enzymes and may inform downstream efforts to modulate this disease-associated metabolism.

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimizations on the fly, and exploit its good performance and transferability in identifying geometry optimization failures for catalyst design. We show that the dynamic classifier performs well on all reactive intermediates in the representative catalytic cycle of the radical rebound mechanism for the conversion of methane to methanol despite being trained on only one reactive intermediate. The dynamic classifier also generalizes to chemically distinct intermediates and metal centers absent from the training data without loss of accuracy or model confidence. We rationalize this superior model transferability as arising from the use of electronic structure and geometric information generated on-the-fly from density functional theory calculations and the convolutional layer in the dynamic classifier. When used in combination with uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered.

Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: The Case of Formate Dehydrogenase

The Mo/W-containing metalloenzyme formate dehydrogenase (FDH) is an efficient and selective natural catalyst that reversibly converts CO₂ to formate under ambient conditions. In this study, we investigate the impact of the greater protein environment on the electrostatic potential (ESP) of the active site. To model the enzyme environment, we used a combination of classical molecular dynamics and multiscale quantum-mechanical (QM)/molecular-mechanical (MM) simulations. We leverage charge shift analysis to systematically construct QM regions and analyze the electronic environment of the active site by evaluating the degree of charge transfer between the core active site and the protein environment. The contribution of the terminal chalcogen ligand to the ESP of the metal center is substantial and dependent on the chalcogen identity, with similar, less negative ESPs for Se and S terminal chalcogens in comparison to O regardless of whether the metal is Mo or W. The orientation of the side chains and conformations of the cofactor also affect the ESP, highlighting the importance of sampling dynamic fluctuations in the protein. Overall, our observations suggest that the terminal chalcogen ligand identity plays an important role in the enzymatic activity of FDH, suggesting opportunities for a rational bioinspired catalyst design.

Ligand Additivity and Divergent Trends in Two Types of Delocalization Errors from Approximate Density Functional Theory

The predictive accuracy of density functional theory (DFT) is hampered by delocalization errors, especially for correlated systems such as transition-metal complexes. Two complementary strategies have been developed to reduce delocalization error: eliminating the global curvature with change in charge, and applying a linear response Hubbard U as a measure of local curvature at a metal center at fixed charge in a DFT+U framework. We investigate the relationship between the two delocalization error measures as the ligand field strength is varied with the number of strong-field ligands in a series of heteroleptic complexes or by geometrically constraining the metal-ligand bond length in homoleptic octahedral complexes. We show that across these sets of complexes an inverse relationship generally exists between global and local curvatures. We find that effects of ligand substitution on both measures of delocalization are typically additive, but the quantities seldom coincide.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning-accelerated density functional theory in a space of 16 M candidate catalysts including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low-spin Fe(II) compounds paired with strong-field (e.g., P or S-coordinating) ligands have among the best energetic tradeoffs between hydrogen atom transfer (HAT) and methanol release. This observation contrasts with prior efforts that have focused on high-spin Fe(II) with weak-field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and that higher-valency metals [i.e., M(III) vs M(II)] are likely to be rate-limited by slow methanol release. With full characterization of barrier heights, we confirm that optimizing for HAT does not lead to large oxo formation barriers. Energetic span analysis reveals designs for an intermediate-spin Mn(II) catalyst and a low-spin Fe(II) catalyst that are predicted to have good turnover frequencies. Our active learning approach to optimize two distinct reaction energies with efficient global optimization is expected to be beneficial for the search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown.

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations

Knowledge of the chemical bonding of HfO and HfB ground and low-lying electronic states provides essential insights into a range of catalysts and materials that contain Hf-O or Hf-B moieties. Here, we carry out high-level multi-reference configuration interaction theory and coupled cluster quantum chemical calculations on these systems. We compute full potential energy curves, excitation energies, ionization energies, electronic configurations, and spectroscopic parameters with large quadruple-ζ and quintuple-ζ quality correlation consistent basis sets. We also investigate equilibrium chemical bonding patterns and effects of correlating core electrons on property predictions. Differences in the ground state electron configuration of HfB(X⁴Σ^-) and HfO(X¹Σ⁺) lead to a significantly stronger bond in HfO than HfB, as judged by both dissociation energies and equilibrium bond distances. We extend our analysis to the chemical bonding patterns of the isovalent HfX (X = O, S, Se, Te, and Po) series and observe similar trends. We also note a linear trend between the decreasing value of the dissociation energy (D_e) from HfO to HfPo and the singlet-triplet energy gap (ΔE_S-T) of the molecule. Finally, we compare these benchmark results to those obtained using density functional theory (DFT) with 23 exchange-correlation functionals spanning multiple rungs of "Jacob's ladder." When comparing DFT errors to coupled cluster reference values on dissociation energies, excitation energies, and ionization energies of HfB and HfO, we observe semi-local generalized gradient approximations to significantly outperform more complex and high-cost functionals.

Computational Modeling of Conformer Stability in Benenodin-1, a Thermally Actuated Lasso Peptide Switch

Benenodin-1 is a thermally actuated lasso peptide rotaxane switch with two primary translational isomers that differ in the relative position of the residue Gln15. The conversion from one conformer to the other involves substantial enthalpy-entropy compensation: one conformer is energetically favored and the other is entropically favored. Here, we take a multi-scale quantum mechanical (QM) and classical molecular dynamic (MD) approach to reveal residue-specific sources of these differences in stability. QM reveals that the two benenodin-1 conformers involve distinct hydrogen bonding networks, with the enthalpically favored conformer having more intra-peptide hydrogen bonds between the Gln15 side chain and nearby residues. The evaluation of configurational entropy over the MD-sampled geometries reveals that the entropically favored conformer has enhanced conformational flexibility. By computing the by-residue-sum entropies, we identify the role of Gln15 and neighboring Glu14 in mediating the entropic variation during the switching process. These computational insights help explain the effects of Glu14Ala and Gln15Ala mutations on the conformational population of benenodin-1 observed experimentally.

Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry

Low-cost, non-empirical corrections to semi-local density functional theory are essential for accurately modeling transition-metal chemistry. Here, we demonstrate the judiciously modified density functional theory (jmDFT) approach with non-empirical U and J parameters obtained directly from frontier orbital energetics on a series of transition-metal complexes. We curate a set of nine representative Ti(III) and V(IV) d1 transition-metal complexes and evaluate their flat-plane errors along the fractional spin and charge lines. We demonstrate that while jmDFT improves upon both DFT+U and semi-local DFT with the standard atomic orbital projectors (AOPs), it does so inefficiently. We rationalize these inefficiencies by quantifying hybridization in the relevant frontier orbitals. To overcome these limitations, we introduce a procedure for computing a molecular orbital projector (MOP) basis for use with jmDFT. We demonstrate this single set of d1 MOPs to be suitable for nearly eliminating all energetic delocalization error and static correlation error. In all cases, MOP jmDFT outperforms AOP jmDFT, and it eliminates most flat-plane errors at non-empirical values. Unlike DFT+U or hybrid functionals, jmDFT nearly eliminates energetic delocalization error and static correlation error within a non-empirical framework.

Probing the Mechanism of Isonitrile Formation by a Non-Heme Iron(II)-Dependent Oxidase/Decarboxylase

The isonitrile moiety is an electron-rich functionality that decorates various bioactive natural products isolated from diverse kingdoms of life. Isonitrile biosynthesis was restricted for over a decade to isonitrile synthases, a family of enzymes catalyzing a condensation reaction between l-Trp/l-Tyr and ribulose-5-phosphate. The discovery of ScoE, a non-heme iron(II) and α-ketoglutarate-dependent dioxygenase, demonstrated an alternative pathway employed by nature for isonitrile installation. Biochemical, crystallographic, and computational investigations of ScoE have previously been reported, yet the isonitrile formation mechanism remains obscure. In the present work, we employed in vitro biochemistry, chemical synthesis, spectroscopy techniques, and computational simulations that enabled us to propose a plausible molecular mechanism for isonitrile formation. Our findings demonstrate that the ScoE reaction initiates with C5 hydroxylation of (R)-3-((carboxymethyl)amino)butanoic acid to generate 1, which undergoes dehydration, presumably mediated by Tyr96 to synthesize 2 in a trans configuration. (R)-3-isocyanobutanoic acid is finally generated through radical-based decarboxylation of 2, instead of the common hydroxylation pathway employed by this enzyme superfamily.