Computational Catalysis-Past, Present, and Future

Ichor: A Python library for computational chemistry data management and machine learning force field development

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States

The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations but also due to the exponential relationship between effective activation energies and rate constants. To account for molecular flexibility, an increasing number of methods exist that generate conformational ensembles of transition state (TS) structures. Typically, these TS ensembles are Boltzmann weighted and used to compute selectivity assuming Curtin-Hammett conditions. This strategy, however, can lead to erroneous predictions if the appropriate filtering of the conformer ensembles is not conducted. Here, we demonstrate how any possible selectivity can be obtained by processing the same sets of TS ensembles for a model reaction. To address the burdensome filtering task in a consistent and automated way, we introduce marc, a tool for the modular analysis of representative conformers that aids in avoiding human errors while minimizing the number of reoptimization computations needed to obtain correct reaction selectivity.

The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models

Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling - a one-feature linear regression model - has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e. g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO2 reduction or wastewater remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We also find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies through, e. g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Differentiating the Yield of Chemical Reactions Using Parameters in First-Order Kinetic Equations to Identify Elementary Steps That Control the Reactivity from Complicated Reaction Path Networks

The yield of a chemical reaction is obtained by solving its rate equation. This study introduces an approach for differentiating yields by utilizing the parameters of the rate equation, which is expressed as a first-order linear differential equation. The yield derivative for a specific pair of reactants and products is derived by mathematically expressing the rate constant matrix contraction method, which is a simple kinetic analysis method. The parameters of the rate equation are the Gibbs energies of the intermediates and transition states in the reaction path network used to formulate the rate equation. Thus, our approach for differentiating the yield allows a numerical evaluation of the contribution of energy variation to the yield for each intermediate and transition state in the reaction path network. In other words, a comparison of these values automatically extracts the factors affecting the yield from a complicated reaction path network consisting of numerous reaction paths and intermediates. This study verifies the behavior of the proposed approach through numerical experiments on the reaction path networks of a model system and the Rh-catalyzed hydroformylation reaction. Moreover, the possibility of using this approach for designing ligands in organometallic catalysts is discussed.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.

Reference-Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory

For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation-RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer-Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling.

Transition Metal-Doped C20 Fullerene-Based Single-Atom Catalysts with High Catalytic Activity for Hydrogen Dissociation Reaction

Hydrogen dissociation is a key step in almost all hydrogenation reactions; therefore, an efficient and cost-effective catalyst with a favorable band structure for this step is highly desirable. In the current work, transition metal-based C₂₀ (M@C₂₀) complexes are designed and evaluated as single-atom catalysts (SACs) for hydrogen dissociation reaction (HDR). Interaction energy (E _int) analysis reveals that all the M@C₂₀ complexes are thermodynamically stable, whereas the highest stability is observed for the Ni@C₂₀ complex (E _int = -6.14 eV). Moreover, the best catalytic performance for H₂ dissociation reaction is computed for the Zn@C₂₀ catalyst (E _ads = 0.53 eV) followed by Ti@C₂₀ (E _ads = 0.65 eV) and Sc@C₂₀ (E _ads = 0.76 eV) among all considered catalysts. QTAIM analyses reveal covalent or shared shell interactions in H₂* + M@C₂₀ systems, which promote the process of H₂ dissociation over M@C₂₀ complexes. NBO and EDD analyses declare that transfer of charge from the metal atom to the antibonding orbital of H₂ causes dissociation of the H-H bond. Overall outcomes of this study reveal that the Zn@C₂₀ catalyst can act as a highly efficient, low-cost, abundant, and precious metal-free SAC to effectively catalyze HDR.

Emerging Metabolic Profiles of Sulfonamide Antibiotics by Cytochromes P450: A Computational-Experimental Synergy Study on Emerging Pollutants

Metabolism, especially by CYP450 enzymes, is the main reason for mediating the toxification and detoxification of xenobiotics in humans, while some uncommon metabolic pathways, especially for emerging pollutants, probably causing idiosyncratic toxicity are easily overlooked. The pollution of sulfonamide antibiotics in aqueous system has attracted increasing public attention. Hydroxylation of the central amine group can trigger a series of metabolic processes of sulfonamide antibiotics in humans; however, this work parallelly reported the coupling and fragmenting initiated by amino H-abstraction of sulfamethoxazole (SMX) catalyzed by human CYP450 enzymes. Elucidation of the emerging metabolic profiles was mapped via a multistep synergy between computations and experiments, involving preliminary DFT computations and in vitro and in vivo assays, profiling adverse effects, and rationalizing the fundamental factors via targeted computations. Especially, the confirmed SMX dimer was shown to potentially act as a metabolism disruptor in humans, while spin aromatic delocalization resulting in the low electron donor ability of amino radicals was revealed as the fundamental factor to enable coupling of sulfonamide antibiotics by CYP450 through the nonconventional nonrebound pathway. This work may further strengthen the synergistic use of computations prior to experiments to avoid wasteful experimental screening efforts in environmental chemistry and toxicology.

Hydrogen Dissociation Reaction on First-Row Transition Metal Doped Nanobelts

Zigzag molecular nanobelts have recently captured the interest of scientists because of their appealing aesthetic structures, intriguing chemical reactivities, and tantalizing features. In the current study, first-row transition metals supported on an H₆-N₃-belt[6]arene nanobelt are investigated for the electrocatalytic properties of these complexes for the hydrogen dissociation reaction (HDR). The interaction of the doped transition metal atom with the nanobelt is evaluated through interaction energy analysis, which reveals the significant thermodynamic stability of TM-doped nanobelt complexes. Electronic properties such as frontier molecular orbitals and natural bond orbitals analyses are also computed, to estimate the electronic perturbation upon doping. The highest reduction in the HOMO-LUMO energy gap compared to the bare nanobelt is seen in the case of the Zn@NB catalyst (4.76 eV). Furthermore, for the HDR reaction, the Sc@NB catalyst displays the best catalytic activity among the studied catalysts, with a hydrogen dissociation barrier of 0.13 eV, whereas the second-best catalytic activity is observed for the Zn@NB catalyst (0.36 eV). It is further found that multiple active sites, i.e., the presence of the metal atom and nitrogen atom moiety, help to facilitate the dissociation of the hydrogen molecule. These key findings of this study enhance the understanding of the relative stability, electronic features, and catalytic bindings of various TM@NB catalysts.

Oxidation and Reduction Pathways in the Knowles Hydroamination via a Photoredox-Catalyzed Radical Reaction

Systematic reaction path exploration revealed the entire mechanism of Knowles's light-promoted catalytic intramolecular hydroamination. Bond formation/cleavage competes with single electron transfer (SET) between the catalyst and substrate. These processes are described by adiabatic processes through transition states in an electronic state and non-radiative transitions through the seam of crossings (SX) between different electronic states. This study determined the energetically favorable SET path by introducing a practical computational model representing SET as non-adiabatic transitions via SXs between substrate's potential energy surfaces for different charge states adjusted based on the catalyst's redox potential. Calculations showed that the reduction and proton shuttle process proceeded concertedly. Also, the relative importance of SET paths (giving the product and leading back to the reactant) varies depending on the catalyst's redox potential, affecting the yield.

Remarkable Single Atom Catalyst of Transition Metal (Fe, Co & Ni) Doped on C2N Surface for Hydrogen Dissociation Reaction

Currently, hydrogen is recognized as the best alternative for fossil fuels because of its sustainable nature and environmentally friendly processing. In this study, hydrogen dissociation reaction is studied theoretically on the transition metal doped carbon nitride (C₂N) surface through single atom catalysis. Each TMs@C₂N complex is evaluated to obtain the most stable spin state for catalytic reaction. In addition, electronic properties (natural bond orbital NBO & frontier molecular orbital FMO) of the most stable spin state complex are further explored. During dissociation, hydrogen is primarily adsorbed on metal doped C₂N surface and then dissociated heterolytically between metal and nitrogen atom of C₂N surface. Results revealed that theFe@C₂N surface is the most suitable catalyst for H₂ dissociation reaction with activation barrier of 0.36 eV compared with Ni@C₂N (0.40 eV) and Co@C₂N (0.45 eV) complexes. The activation barrier for H₂ dissociation reaction is quite low in case of Fe@C₂N surface, which is comparatively better than already reported noble metal catalysts.

Multistructural microiteration combined with QM/MM-ONIOM electrostatic embedding

Multistructural microiteration (MSM) is a method to take account of contributions of multiple surrounding structures in a geometrical optimization or reaction path calculation using the quantum mechanics/molecular mechanics (QM/MM) ONIOM method. In this study, we combined MSM with the electrostatic embedding (EE) scheme of the QM/MM-ONIOM method by extending its original formulation for mechanical embedding (ME). MSM-EE takes account of the polarization in the QM region induced by point charges assigned to atoms in the multiple surrounding structures, where the point charges are scaled by the weight factor of each surrounding structure determined through MSM. The performance of MSM-EE was compared with that of the other methods, i.e., ONIOM-ME, ONIOM-EE, and MSM-ME, by applying them to three chemical processes: (1) chorismate-to-prephenate transformation in aqueous solution, (2) the same transformation as (1) in an enzyme, and (3) hydroxylation in p-hydroxybenzoate hydroxylase. These numerical tests of MSM-EE yielded barriers and reaction energies close to experimental values with computational costs comparable to those of the other three methods.

The (not so) simple prediction of enantioselectivity - a pipeline for high-fidelity computations

The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful means to refine catalyst design strategies. Accurately establishing the selectivity of reactions facilitated by molecular catalysts, however, remains a challenging task for computational chemistry. The small free energy differences that lead to large variations in the enantiomeric ratio (er) represent particularly tricky quantities to predict with sufficient accuracy to be helpful for prioritizing experiments. Further complicating this problem is the fact that standard approaches typically consider only one or a handful of conformers identified through human intuition as pars pro toto of the conformational space. Obviously, this assumption can potentially lead to dramatic failures should key energetic low-lying structures be missed. Here, we introduce a multi-level computational pipeline leveraging the graph-based Molassembler library to construct an ensemble of molecular catalysts. The manipulation and interpretation of molecules as graphs provides a powerful and direct route to tailored functionalization and conformer generation that facilitates high-throughput mechanistic investigations of chemical reactions. The capabilities of this approach are validated by examining a Rh(iii) catalyzed asymmetric C-H activation reaction and assessing the limitations associated with the underlying ligand design model. Specifically, the presence of remarkably flexible chiral Cp ligands, which induce the experimentally observed high level of selectivity, present a rich configurational landscape where multiple unexpected conformations contribute to the reported enantiomeric ratios (er). Using Molassembler, we show that considering about 20 transition state conformations per catalysts, which are generated with little human intervention and are not tied to "back-of-the-envelope" models, accurately reproduces experimental er values with limited computational expense.

A detailed theoretical investigation to unravel the molecular mechanism of the ligand-free copper-catalyzed Suzuki cross-coupling reaction

The Suzuki-Miyaura coupling (SMC) represents a very efficacious method for constructing C-C bonds in organic synthesis. The ligand-free variants of SMC have been grabbing attention these days. Despite this momentousness, the mechanistic details of the ligand-free variants are scant in the literature. Herein, we have carried out a detailed mechanistic investigation into the ligand-free Cu-catalyzed SMC of unsaturated organic halides with aryl boronic acid with the aid of density functional theory (DFT) calculations employing the conductor-like polarizable continuum model (CPCM) method. The present study elucidates that in the absence of ancillary ligands on the metal, the substrates, base, and solvent molecules could act as pseudo-ancillary ligands to facilitate the cross-coupling reaction. The investigation further revealed that unsaturated halides like alkynyl halides/vinyl halides could act as good ancillary ligands for copper by forming a Cu-π intermediate and promoting a facile transmetalation process. However, regarding the oxidative addition and reductive elimination steps, a concerted pathway is observed contrary to Pd catalyzed Suzuki coupling, owing to the instability of Cu(III) species and the favourability of Csp²-Csp bond formation. In the whole set of mechanisms explored, oxidative addition/oxidative nucleophilic substitution was the rate-determining step in all the cases. A thermodynamically stable π-coordinated intermediate species where the substrate and base molecule are coordinated to the metal center is identified as the rate-determining species for the ligand-free Suzuki cross-coupling reaction. The presence of the aforesaid intermediate increases the energy span and consequently the activation barrier for the rate-determining step. This study unveiled a theoretical rationale for the high-temperature requirement in the ligand-free Cu-catalyzed SMC reaction.

Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants

The long-due development of a computational method for the ab initio prediction of chemical reactants that provide a target compound has been hampered by the combinatorial explosion that occurs when reactions consist of multiple elementary reaction processes. To address this challenge, we have developed a quantum chemical calculation method that can enumerate the reactant candidates from a given target compound by combining an exhaustive automated reaction path search method with a kinetics method for narrowing down the possibilities. Two conventional name reactions were then assessed by tracing back the reaction paths using this new method to determine whether the known reactants could be identified. Our method is expected to be a powerful tool for the prediction of reactants and the discovery of new reactions.

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.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

Predicting Effects of Site-Directed Mutagenesis on Enzyme Kinetics by QM/MM and QM Calculations: A Case of Glutamate Carboxypeptidase II

Quantum and molecular mechanics (QM/MM) and QM-only (cluster model) modeling techniques represent the two workhorses in mechanistic understanding of enzyme catalysis. One of the stringent tests for QM/MM and/or QM approaches is to provide quantitative answers to real-world biochemical questions, such as the effect of single-point mutations on enzyme kinetics. This translates into predicting the relative activation energies to 1-2 kcal·mol^-1 accuracy; such predictions can be used for the rational design of novel enzyme variants with desired/improved characteristics. Herein, we employ glutamate carboxypeptidase II (GCPII), a dizinc metallopeptidase, also known as the prostate specific membrane antigen, as a model system. The structure and activity of this major cancer antigen have been thoroughly studied, both experimentally and computationally, which makes it an ideal model system for method development. Its reaction mechanism is quite well understood: the reaction coordinate comprises a "tetrahedral intermediate" and two transition states and experimental activation Gibbs free energy of ∼17.5 kcal·mol^-1 can be inferred for the known k_cat ≈ 1 s^-1. We correlate experimental kinetic data (including the E424H variant, newly characterized in this work) for various GCPII mutants (k_cat = 8.6 × 10^-5 s^-1 to 2.7 s^-1) with the energy profiles calculated by QM/MM and QM-only (cluster model) approaches. We show that the near-quantitative agreement between the experimental values and the calculated activation energies (ΔH^⧧) can be obtained and recommend the combination of the two protocols: QM/MM optimized structures and cluster model (QM) energetics. The trend in relative activation energies is mostly independent of the QM method (DFT functional) used. Last but not least, a satisfactory correlation between experimental and theoretical data allows us to provide qualitative and fairly simple explanations of the observed kinetic effects which are thus based on a rigorous footing.

Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis

Enhanced sampling ab initio simulations enable to study chemical phenomena in catalytic systems including thermal effects & anharmonicity, & collective dynamics describing enthalpic & entropic contributions, which can significantly impact on reaction free energy landscapes.

A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis

Theoretical reaction screening based on Gibbs energy barriers would be promising to accelerate chemical reactions mining. The number of quantum chemical calculations can be reduced by using an optimization algorithm such as genetic algorithm (GA) and Bayesian optimization (BO). The focus of this study is to generate a dataset of reaction barriers of size ∼100000. Such a dataset would be useful to quickly evaluate various implementations of an optimization algorithm such as GA and BO. The dataset includes Gibbs energy barriers of the Claisen rearrangement for ∼100000 molecules computed on the basis of a semiempirical theory PM7. After evaluating its chemical and numerical features, it is found that the dataset well reflects chemical trends of various substitutions and is useful in testing various implementations of an optimization algorithm. The dataset is available in the supplementary material of this paper.

Kinetic Analysis of a Cysteine-Derived Thiyl-Catalyzed Asymmetric Vinylcyclopropane Cycloaddition Reflects Numerous Attractive Noncovalent Interactions

Kinetic studies of a vinylcyclopropane (VCP) cycloaddition, catalyzed by peptide-based thiyl radicals, are described. Reactions were analyzed by using reaction progress kinetic analysis, revealing that ring-opening of the VCP is both rate- and enantio-determining. These conclusions are further corroborated by studies involving racemic and enantiopure VCP starting material. Noncovalent interactions play key roles throughout: both the peptide catalyst and VCP exhibit unproductive self-aggregation, which appears to be disrupted by binding between the catalyst and VCP. This in turn explains the requirement for the key catalyst feature, a substituent at the 4-position of the proline residue, which is required for both turnover/rate and selectivity.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

New insights in chemical reactivity from quantum chemical topology

Based on the quantum chemical topology of the modified electron localization function ELF_x , an efficient and robust mechanistic methodology designed to identify the favorable reaction pathway between two reactants is proposed. We first recall and reshape how the supermolecular interaction energy can be evaluated from only three distinct terms, namely the intermolecular coulomb energy, the intermolecular exchange-correlation energy and the intramolecular energies of reactants. Thereafter, we show that the reactivity between the reactants is driven by the first-order variation in the coulomb intermolecular energy defined in terms of the response to changes in the number of electrons. Illustrative examples with the formation of the dative bond B-N involved in the BH₃ NH₃ molecule and the typical formation of the hydrogen bond in the canonical water dimer are presented. For these selected systems, our approach unveils a noticeable mimicking of E_dual onto the DFT intermolecular interaction energy surface calculated between the both reactants. An automated reaction-path algorithm aimed to determine the most favorable relative orientations when the two molecules approach each other is also outlined.

Mechanism, kinetics and selectivity of a Williamson ether synthesis: elucidation under different reaction conditions

New mechanistic understanding and the quantification of reaction kinetics shed light on the large impact of the solvent on selectivity.

An Iterative Fragment Scheme for the ACKS2 Electronic Polarization Model: Application to Molecular Dimers and Chains

The treatment of electrostatic interactions is a key ingredient in the force field-based simulation of condensed phase systems. Most approaches used fixed, site-specific point charges. Yet, it is now clear that many applications of force fields (FFs) demand more sophisticated treatments, prompting the implementation of charge equilibration methods in polarizable FFs to allow the redistribution of charge within the system. One approach allowing both, charge redistribution and site-specific polarization, while at the same time solving methodological shortcomings of earlier methods, is the first-principles-derived atom-condensed Kohn-Sham density functional theory method approximated to the second order (ACKS2). In this work, we present two fragment approaches to ACKS2, termed f-ACKS2 and a self-consistent version, scf-ACKS2, that treat condensed phase systems as a collection of electronically polarizable molecular fragments. The fragmentation approach to ACKS2 not only leads to a more transferable and less system-specific collection of electronic response parameters but also opens up the method to large condensed phase systems. We validate the accuracies of f-ACKS2 and scf-ACKS2 by comparing polarization energies and induced dipole moments for a number of charged hydrocarbon dimers against DFT reference calculations. Finally, we also apply both fragmented ACKS2 variants to calculate the polarization energy for electron-hole pair separation along a chain of anthracene molecules and find excellent agreement with reference DFT calculations.

DFT Study on Mechanisms of the N2O Direct Catalytic Decomposition over Cu-ZSM-5: The Detailed Investigation on NO Formation Mechanism

Nitrous oxide (N2O) is an industrial emission that causes the greenhouse effect and damages the ozone layer. Density functional theory study on the N2O direct catalytic decomposition over Cu–ZSM-5 has been performed in this paper. Two possible reaction mechanisms for N2O direct catalytic decomposition over Cu-ZSM-5 were proposed (O2 formation mechanism and Nitric oxide (NO) formation mechanism). The geometrical parameters, vibration frequency and thermodynamic data of the intermediate states in each step have been examined. The results indicate that N2O can be adsorbed on active site Cu in two ways (O-terminal or N-terminal), and N2O decomposition reactions can occur in both cases. The NO formation mechanism exhibits higher N2O dissociation reaction due to lower energy barrier.

Discovery of a synthesis method for a difluoroglycine derivative based on a path generated by quantum chemical calculations

The systematic exploration of synthetic pathways to afford a desired product through quantum chemical calculations remains a considerable challenge. In 2013, Maeda et al. introduced 'quantum chemistry aided retrosynthetic analysis' (QCaRA), which uses quantum chemical calculations to search systematically for the decomposition paths of a target product and proposes a synthesis method. However, until now, no new reactions suggested by QCaRA have been reported to lead to experimental discoveries. Using a difluoroglycine derivative as a target, this study investigated the ability of QCaRA to suggest various synthetic paths to the target without relying on previous data or the knowledge and experience of chemists. Furthermore, experimental verification of the most promising path chosen by an organic chemist among the predicted paths led to the discovery of a synthesis method for a difluoroglycine derivative. We emphasize that the purpose of this study is not to propose a fully automated workflow. Therefore, the extent of the hands-on expertise of chemists required during the verification process was also evaluated. These insights are expected to advance the applicability of QCaRA to the discovery of viable experimental synthetic routes.

Catalysis-Enabled Access to Cryptic Geldanamycin Oxides

Catalytic, selective modifications of natural products can be a fertile platform for not only unveiling new natural product analogues with altered biological activity, but also for revealing new reactivity and selectivity hierarchies for embedded functional groups in complex environments. Motivated by these intersecting aims, we report site- and stereoselective oxidation reactions of geldanamycin facilitated by aspartyl-peptide catalysts. Through the isolation and characterization of four new geldanamycin oxides, we discovered a synergistic effect between lead peptide-based catalysts and geldanamycin, resulting in an unexpected reaction pathway. Curiously, our discoveries would likely not have been possible absent the attractive noncovalent interactions intrinsic to both the catalysts and the natural product. The result is a set of new "meta" catalytic reactions that deliver both unknown and previously incompletely characterized geldanamycin analogues. Enabled by the catalytic, site-selective epoxidation of geldanamycin, biological assays were carried out to document the bioactivities of the new compounds.

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists

In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided.

Conformational Dynamics in Asymmetric Catalysis: Is Catalyst Flexibility a Design Element?

Traditionally, highly selective low molecular weight catalysts have been designed to contain rigidifying structural elements. As a result, many proposed stereochemical models rely on steric repulsion for explaining the observed selectivity. Recently, as is the case for enzymatic systems, it has become apparent that some flexibility can be beneficial for imparting selectivity. Dynamic catalysts can reorganize to maximize attractive non-covalent interactions that stabilize the favored diastereomeric transition state, while minimizing repulsive non-covalent interactions for enhanced selectivity. This Short Review discusses catalyst conformational dynamics and how these effects have proven beneficial for a variety of catalyst classes, including tropos ligands, cinchona alkaloids, hydrogen-bond donating catalysts, and peptides.

Mechanistic understanding of catalysis by combining mass spectrometry and computation

The combination of mass spectrometry and computational chemistry has been proven to be powerful for exploring reaction mechanisms. The former provides information of reaction intermediates, while the latter gives detailed reaction energy profiles.

Molecular Dynamics Simulations of a Conformationally Mobile Peptide-Based Catalyst for Atroposelective Bromination

It is widely accepted that structural rigidity is required to achieve high levels of asymmetric induction in catalytic, enantioselective reactions. This fundamental design principle often does not apply to highly selective catalytic peptides that often exhibit conformational heterogeneity. As a result, these complex systems are particularly challenging to study both experimentally and computationally. Herein, we utilize molecular dynamics simulations to investigate the role of conformational mobility on the reactivity and selectivity exhibited by a catalytic, β-turn-biased peptide in an atroposelective bromination reaction. By means of cluster analysis, multiple distinct conformers of the peptide and a catalyst-substrate complex were identified in the simulations, all of which were corroborated by experimental NMR measurements. The simulations also revealed that a shift in the conformational equilibrium of the peptidic catalyst occurs upon addition of substrate, and the degree of change varies among different substrates. On the basis of these data, we propose a correlation between the composition of the peptide conformational ensemble and its catalytic properties. Moreover, these findings highlight the importance of conformational dynamics in catalytic, asymmetric reactions mediated by oligopeptides, unveiled through high-level, state-of-the-art computational modeling.

A Comparison of QM/MM Simulations with and without the Drude Oscillator Model Based on Hydration Free Energies of Simple Solutes

Maintaining a proper balance between specific intermolecular interactions and non-specific solvent interactions is of critical importance in molecular simulations, especially when predicting binding affinities or reaction rates in the condensed phase. The most rigorous metric for characterizing solvent affinity are solvation free energies, which correspond to a transfer from the gas phase into solution. Due to the drastic change of the electrostatic environment during this process, it is also a stringent test of polarization response in the model. Here, we employ both the CHARMM fixed charge and polarizable force fields to predict hydration free energies of twelve simple solutes. The resulting classical ensembles are then reweighted to obtain QM/MM hydration free energies using a variety of QM methods, including MP2, Hartree⁻Fock, density functional methods (BLYP, B3LYP, M06-2X) and semi-empirical methods (OM2 and AM1 ). Our simulations test the compatibility of quantum-mechanical methods with molecular-mechanical water models and solute Lennard⁻Jones parameters. In all cases, the resulting QM/MM hydration free energies were inferior to purely classical results, with the QM/MM Drude force field predictions being only marginally better than the QM/MM fixed charge results. In addition, the QM/MM results for different quantum methods are highly divergent, with almost inverted trends for polarizable and fixed charge water models. While this does not necessarily imply deficiencies in the QM models themselves, it underscores the need to develop consistent and balanced QM/MM interactions. Both the QM and the MM component of a QM/MM simulation have to match, in order to avoid artifacts due to biased solute⁻solvent interactions. Finally, we discuss strategies to improve the convergence and efficiency of multi-scale free energy simulations by automatically adapting the molecular-mechanics force field to the target quantum method.

Conformational Properties of a Peptidic Catalyst: Insights from NMR Spectroscopic Studies

Peptides have become valuable as catalysts for a variety of different reactions, but little is known about the conformational properties of peptidic catalysts. We investigated the conformation of the peptide H-dPro-Pro-Glu-NH₂, a highly reactive and stereoselective catalyst for conjugate addition reactions, and the corresponding enamine intermediate in solution by NMR spectroscopy and computational methods. The combination of nuclear Overhauser effects (NOEs), residual dipolar couplings (RDCs), J-couplings, and temperature coefficients revealed that the tripeptide adopts a single predominant conformation in its ground state. The structure is a type I β-turn, which gains stabilization from three hydrogen bonds that are cooperatively formed between all functional groups (secondary amine, carboxylic acid, amides) within the tripeptide. In contrast, the conformation of the enamine intermediate is significantly more flexible. The conformational ensemble of the enamine is still dominated by the β-turn, but the backbone and the side chain of the glutamic acid residue are more dynamic. The key to the switch between rigidity and flexibility of the peptidic catalyst is the CO₂H group in the side chain of the glutamic acid residue, which acts as a lid that can open and close. As a result, the peptidic catalyst is able to adapt to the structural requirements of the intermediates and transition states of the catalytic cycle. These insights might explain the robustness and high reactivity of the peptidic catalyst, which exceeds that of other secondary amine-based organocatalysts. The data suggest that a balance between rigidity and flexibility, which is reminiscent of the dynamic nature of enzymes, is beneficial for peptidic catalysts and other synthetic catalysts.