Tetraborylation of p-Benzynes Generated by the Masamune-Bergman Cyclization through Reaction Design Based on the Reaction Path Network

Designing the reactant molecule of an initial reaction, based on quantum chemical pathway exploration, enabled us to access a new reaction, i.e., the tetraborylation reaction of p-benzynes generated from 1,2-diethynylbenzene derivatives, using bis(pinacolato)diborane(4) (B2pin2). Based on the reaction path network generated via the artificial-force-induced reaction (AFIR) method, desired and undesired paths were identified and used to modify the chemical structure of the reactant. After the in silico screening, the optimal structure of the reactant was determined to be a 1,2-diethynylbenzene derivative with a butylene linker. The reaction of the optimized reactant and its derivatives with an excess of B2pin2 gave the tetraborylated products in good yields (up to 58%). It is quite intriguing that the two carbons of p-benzyne behave formally as dicarbenes in this reaction.

Nanoscale chemical reaction exploration with a quantum magnifying glass

Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a "quantum magnifying glass" that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability.

SCINE-Software for chemical interaction networks

The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt, or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator are as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available as open source and free of charge.

TS-tools: Rapid and automated localization of transition states based on a textual reaction SMILES input

Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95% already at xTB level of theory. For tri- and multimolecular reaction pathways - which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity - TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.

Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks

Exploring large chemical reaction networks with automated exploration approaches and accurate quantum chemical methods can require prohibitively large computational resources. Here, we present an automated exploration approach that focuses on the kinetically relevant part of the reaction network by interweaving (i) large-scale exploration of chemical reactions, (ii) identification of kinetically relevant parts of the reaction network through microkinetic modeling, (iii) quantification and propagation of uncertainties, and (iv) reaction network refinement. Such an uncertainty-aware exploration of kinetically relevant parts of a reaction network with automated accuracy improvement has not been demonstrated before in a fully quantum mechanical approach. Uncertainties are identified by local or global sensitivity analysis. The network is refined in a rolling fashion during the exploration. Moreover, the uncertainties are considered during kinetically steering of a rolling reaction network exploration. We demonstrate our approach for Eschenmoser-Claisen rearrangement reactions. The sensitivity analysis identifies that only a small number of reactions and compounds are essential for describing the kinetics reliably, resulting in efficient explorations without sacrificing accuracy and without requiring prior knowledge about the chemistry unfolding.

Photoinduced dual bond rotation of a nitrogen-containing system realized by chalcogen substitution

Photoinduced concerted multiple-bond rotation has been proposed in some biological systems. However, the observation of such phenomena in synthetic systems, in other words, the synthesis of molecules that undergo photoinduced multiple-bond rotation upon photoirradiation, has been a challenge in the photochemistry field. Here we describe a chalcogen-substituted benzamide system that exhibits photoinduced dual bond rotation in heteroatom-containing bonds. Introduction of the chalcogen substituent into a sterically hindered benzamide system provides sufficient kinetic stability and photosensitivity to enable the photoinduced concerted rotation. The presence of two different substituents on the phenyl ring in the thioamide derivative enables the generation of a pair of enantiomers and E/Z isomers. Using these four stereoisomers as indicators of which bonds are rotated, we monitor the photoinduced C-N/C-C concerted bond rotation in the thioamide derivative depending on external stimuli such as temperature and photoirradiation. Theoretical calculations provide insight on the mechanism of this selective photoinduced C-N/C-C concerted rotation.

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.

Conformational Sampling over Transition-Metal-Catalyzed Reaction Pathways: Toward Revealing Atroposelectivity

The Py-Conformational-Sampling (PyCoSa) technique is introduced as a systematic computational means to sample the configurational space of transition-metal-catalyzed stereoselective reactions. When applied to atroposelective Suzuki-Miyaura coupling to create axially chiral biaryl products, the results show a range of mechanistic possibilities that include multiple low-energy channels through which C-C bonds can be formed.

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.

Exploring Downhill Bifurcations in [3,3]-Sigmatropic Rearrangement by Finding Transitions from an Uphill Bifurcation to a Downhill Bifurcation

Downhill bifurcation is a phenomenon in which an ensemble of trajectories passing through a transition state (TS), called an ambimodal TS, bifurcates into multiple products. Finding downhill bifurcations for unreported pairs of chemical transformations is essential, because they affect reaction selectivity. Marx et al. reported that perturbations such as applying mechanical stress or changing a substituent cause a transition from an uphill bifurcation to a downhill bifurcation in the ring-opening reaction of cyclopropane derivatives (ChemPhysChem, 2018, 19, 837-847). Investigating the occurrence of this phenomenon in other reactions, especially in pericyclic reactions, is interesting for understanding and controlling the reaction selectivity considering downhill bifurcations. In this study, we proposed a method for finding perturbation-induced downhill bifurcations and applied it to three pericyclic reactions. The transition from an uphill bifurcation to a downhill bifurcation occurred in two of the three pericyclic reactions, one of which was previously unreported. Interestingly, the occurrence of a downhill bifurcation by a perturbation depended on the directions of the intrinsic reaction coordinate paths of the two TSs when they emerged from the reactant minimum. Our method can be applied in mechanistic studies to avoid the risk of overlooking downhill bifurcations.

Azobenzene as a photoswitchable mechanophore

Azobenzene has been widely explored as a photoresponsive element in materials science. Although some studies have investigated the force-induced isomerization of azobenzene, the effect of force on the rupture of azobenzene has not been explored. Here we show that the light-induced structural change of azobenzene can also alter its rupture forces, making it an ideal light-responsive mechanophore. Using single-molecule force spectroscopy and ultrasonication, we found that cis and trans para-azobenzene isomers possess contrasting mechanical properties. Dynamic force spectroscopy experiments and quantum-chemical calculations in which azobenzene regioisomers were pulled from different directions revealed that the distinct rupture forces of the two isomers are due to the pulling direction rather than the energetic difference between the two isomers. These mechanical features of azobenzene can be used to rationally control the macroscopic fracture behaviours of polymer networks by photoillumination. The use of light-induced conformational changes to alter the mechanical response of mechanophores provides an attractive way to engineer polymer networks of light-regulatable mechanical properties.

Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics

In recent years, first-principles exploration of chemical reaction space has provided valuable insights into intricate reaction networks. Here, we introduce ab initio hyperreactor dynamics, which enables rapid screening of the accessible chemical space from a given set of initial molecular species, predicting new synthetic routes that can potentially guide subsequent experimental studies. For this purpose, different hyperdynamics derived bias potentials are applied along with pressure-inducing spherical confinement of the molecular system in ab initio molecular dynamics simulations to efficiently enhance reactivity under mild conditions. To showcase the advantages and flexibility of the hyperreactor approach, we present a systematic study of the method's parameters on a HCN toy model and apply it to a recently introduced experimental model for the prebiotic formation of glycinal and acetamide in interstellar ices, which yields results in line with experimental findings. In addition, we show how the developed framework enables the study of complicated transitions like the first step of a nonenzymatic DNA nucleoside synthesis in an aqueous environment, where the molecular fragmentation problem of earlier nanoreactor approaches is avoided.

Using Mechanochemistry to Activate Commodity Plastics as Initiators for Radical Chain Reactions of Small Organic Molecules

Radical initiators such as azo compounds and organic peroxides have been widely used to facilitate numerous transformations of free radicals, which enable the efficient synthesis of structurally complex molecules, natural products, polymers, and functional materials. However, these high-energy reagents are potentially explosive and thus often require special precautions or delicate operating conditions. We postulated that a more convenient and safer alternative for radical chain initiation could be developed by mechanical activation of thermodynamically stable covalent bonds. Here, we show that commodity plastics such as polyethylene and poly(vinyl acetate) are capable of acting as efficient initiators for radical chain reactions under solvent-free mechanochemical conditions. In this approach, polymeric mechanoradicals, which are generated by homolytic cleavage of the polymer chains in response to the applied mechanical energy provided by ball milling, react with tris(trimethylsilyl)silane to initiate radical chain dehalogenation of organic halides. Preliminary calculations support our proposed force-induced radical chain mechanism.

Automatic Potential Energy Surface Exploration by Accelerated Reactive Molecular Dynamics Simulations: From Pyrolysis to Oxidation Chemistry

Automatic potential energy surface (PES) exploration is important to a better understanding of reaction mechanisms. Existing automatic PES mapping tools usually rely on predefined knowledge or computationally expensive on-the-fly quantum-chemical calculations. In this work, we have developed the PESmapping algorithm for discovering novel reaction pathways and automatically mapping out the PES using merely one starting species is present. The algorithm explores the unknown PES by iteratively spawning new reactive molecular dynamics (RMD) simulations for species that it has detected within previous RMD simulations. We have therefore extended the RMD simulation tool ChemTraYzer2.1 (Chemical Trajectory Analyzer, CTY) for this PESmapping algorithm. It can generate new seed species, automatically start replica simulations for new pathways, and stop the simulation when a reaction is found, reducing the computational cost of the algorithm. To explore PESs with low-temperature reactions, we applied the acceleration method collective variable (CV)-driven hyperdynamics. This involved the development of tailored CV templates, which are discussed in this study. We validate our approach for known pathways in various pyrolysis and oxidation systems: hydrocarbon isomerization and dissociation (C4H7 and C8H7 PES), mostly dominant at high temperatures and low-temperature oxidation of n-butane (C4H9O2 PES) and cyclohexane (C6H11O2 PES). As a result, in addition to new pathways showing up in the simulations, common isomerization and dissociation pathways were found very fast: for example, 44 reactions of butenyl radicals including major isomerizations and decompositions within about 30 min wall time and low-temperature chemistry such as the internal H-shift of RO2 → QO2H within 1 day wall time. Last, we applied PESmapping to the oxidation of the recently proposed biohybrid fuel 1,3-dioxane and validated that the tool could be used to discover new reaction pathways of larger molecules that are of practical use.

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol-Mediated Morita-Baylis-Hillman Reactions

Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate-determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery.

Pynta─An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics

Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.

Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst Case

Ab initio kinetic studies are important to understand and design novel chemical reactions. While the Artificial Force Induced Reaction (AFIR) method provides a convenient and efficient framework for kinetic studies, accurate explorations of reaction path networks incur high computational costs. In this article, we are investigating the applicability of Neural Network Potentials (NNP) to accelerate such studies. For this purpose, we are reporting a novel theoretical study of ethylene hydrogenation with a transition metal complex inspired by Wilkinson's catalyst, using the AFIR method. The resulting reaction path network was analyzed by the Generative Topographic Mapping method. The network's geometries were then used to train a state-of-the-art NNP model, to replace expensive ab initio calculations with fast NNP predictions during the search. This procedure was applied to run the first NNP-powered reaction path network exploration using the AFIR method. We discovered that such explorations are particularly challenging for general purpose NNP models, and we identified the underlying limitations. In addition, we are proposing to overcome these challenges by complementing NNP models with fast semiempirical predictions. The proposed solution offers a generally applicable framework, laying the foundations to further accelerate ab initio kinetic studies with Machine Learning Force Fields, and ultimately explore larger systems that are currently inaccessible.

Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application

Autonomously exploring chemical reaction networks with first-principles methods can generate vast data. Especially autonomous explorations without tight constraints risk getting trapped in regions of reaction networks that are not of interest. In many cases, these regions of the networks are only exited once fully searched. Consequently, the required human time for analysis and computer time for data generation can make these investigations unfeasible. Here, we show how simple reaction templates can facilitate the transfer of chemical knowledge from expert input or existing data into new explorations. This process significantly accelerates reaction network explorations and improves cost-effectiveness. We discuss the definition of the reaction templates and their generation based on molecular graphs. The resulting simple filtering mechanism for autonomous reaction network investigations is exemplified with a polymerization reaction.

High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation

Quantum chemical calculations on atomistic systems have evolved into a standard approach to studying molecular matter. These calculations often involve a significant amount of manual input and expertise, although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput electronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject them to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all components of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow with the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

Exploring the Quantum Chemical Energy Landscape with GNN-Guided Artificial Force

Artificial force has been proven useful to get over energy barriers and quickly search a large portion of the energy landscape. This work proposes a method based on graph neural networks to optimize the choice of transformation patterns to examine and accelerate energy landscape exploration. In open search from glutathione, the search efficiency was largely improved in comparison to random selection. We also applied transfer learning from glutathione to tuftsin, resulting in further efficiency gains.

Early-Stage Formation of the SIFSIX-3-Zn Metal-Organic Framework: An Automated Computational Study

Metal-organic frameworks (MOFs) have attracted significant attention over the past 2 decades due to their wide applicability as functional materials. However, targeted synthesis of novel MOFs remains problematic as their formation mechanisms are poorly understood, which forces us to rely on serendipity in the synthesis of novel MOFs. Here, we demonstrate a workflow employing the artificial force induced reaction (AFIR) method to investigate the self-assembly process of the node of the SIFSIX-3-Zn MOF, [Zn(pyz)₄(SiF₆)₂]^2- (pyz = pyrazine), in an automated manner. The workflow encompassing AFIR calculations, generation of extensive reaction path networks, propagation simulations of intermediates, and further refinements of identified formation pathways showed that the nodal structure can form through multiple competing pathways involving interconvertible intermediates. This finding provides a plausible rationale for the stochastic multistage processes believed to be key in MOF formation. Furthermore, this work represents the first application of an automated reaction mechanism discovery method to a MOF system using a general workflow that is applicable to study the formation of other MOF motifs as well.

Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach

While the field of first-principles explorations into chemical reaction space has been continuously growing, the development of strategies for analyzing resulting chemical reaction networks (CRNs) is lagging behind. A CRN consists of compounds linked by reactions. Analyzing how these compounds are transformed into one another based on kinetic modeling is a nontrivial task. Here, we present the graph-optimization-driven algorithm and program Pathfinder to allow for such an analysis of a CRN. The CRN for this work has been obtained with our open-source Chemoton reaction network exploration software. Chemoton probes reactive combinations of compounds for elementary steps and sorts them into reactions. By encoding these reactions of the CRN as a graph consisting of compound and reaction vertices and adding information about activation barriers as well as required reagents to the edges of the graph yields a complete graph-theoretical representation of the CRN. Since the probabilities of the formation of compounds depend on the starting conditions, the consumption of any compound during a reaction must be accounted for to reflect the availability of reagents. To account for this, we introduce compound costs to reflect compound availability. Simultaneously, the determined compound costs rank the compounds in the CRN in terms of their probability to be formed. This ranking then allows us to probe easily accessible compounds in the CRN first for further explorations into yet unexplored terrain. We first illustrate the working principle on an abstract small CRN. Afterward, Pathfinder is demonstrated in the example of the disproportionation of iodine with water and the comproportionation of iodic acid and hydrogen iodide. Both processes are analyzed within the same CRN, which we construct with our autonomous first-principles CRN exploration software Chemoton [Unsleber, J. P.; J. Chem. Theory Comput. 2022, 18, 5393-5409] guided by Pathfinder.

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.

Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets

Pericyclic reactions, which involve cyclic concerted transition states without ionic or radical intermediates, have been extensively studied since their definition in the 1960s, and the famous Woodward-Hoffmann rules predict their stereoselectivity and chemoselectivity. Here, we describe the application of a fully automated reaction-path search method, that is, the artificial force induced reaction (AFIR), to trace an input compound back to reasonable starting materials through thermally allowed pericyclic reactions via product-based quantum-chemistry-aided retrosynthetic analysis (QCaRA) without using any a priori experimental knowledge. All categories of pericyclic reactions, including cycloadditions, ene reactions, group-transfer, cheletropic, electrocyclic, and sigmatropic reactions, were successfully traced back via concerted reaction pathways, and starting materials were computationally obtained with the correct stereochemistry. Furthermore, AFIR was used to predict whether the identified reaction pathway can be expected to occur in good yield relative to other possible reactions of the identified starting material. In order to showcase its practical utility, this state-of-the-art technology was also applied to the retrosynthetic analysis of a natural product with a relatively high number of atoms (52 atoms: endiandric acid C methyl ester), which was first synthesized by Nicolaou in 1982 and provided the corresponding starting polyenes with the correct stereospecificity via three pericyclic reaction cascades (one Diels-Alder reaction as well as 6π and 8π electrocyclic reactions). Moreover, not only systems that obey the Woodward-Hoffmann rules but also systems that violate these rules, such as those recently calculated by Houk, can be retrosynthesized accurately.

A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes

In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics - Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.

Multi-state Energy Landscape for Photoreaction of Stilbene and Dimethyl-stilbene

We have recently developed the reaction space projector (ReSPer) method, which constructs a reduced-dimensionality reaction space uniquely determined from reference reaction paths for a polyatomic molecular system and projects classical trajectories into the same reaction space. In this paper, we extend ReSPer to the analysis of photoreaction dynamics and relaxation processes of stilbene and present the concept of a "multi-state energy landscape," incorporating the ground- and excited-state reaction subspaces. The multi-state energy landscape successfully explains the previously established photoreaction processes of cis-stilbene, such as the cis-trans photoisomerization and photocyclization. In addition, we discuss the difference in the excited-state reaction dynamics between stilbene and 1,1'-dimethyl stilbene based on a common reaction subspace determined from the framework part of reference structures with different number of atoms. This approach allows us to target any molecule with a common framework, greatly expanding the applicability of the ReSPer analysis. The multi-state energy landscape provides fruitful insight into photochemical reactions, exploring the excited- and ground-state potential energy surfaces, as well as comprehensive reaction processes with nonradiative transitions between adiabatic states, within the stage of a reduced-dimensionality reaction space.

Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks

Fueled by advances in hardware and algorithm design, large-scale automated explorations of chemical reaction space have become possible. Here, we present our approach to an open-source, extensible framework for explorations of chemical reaction mechanisms based on the first-principles of quantum mechanics. It is intended to facilitate reaction network explorations for diverse chemical problems with a wide range of goals such as mechanism elucidation, reaction path optimization, retrosynthetic path validation, reagent design, and microkinetic modeling. The stringent first-principles basis of all algorithms in our framework is key for the general applicability that avoids any restrictions to specific chemical systems. Such an agile framework requires multiple specialized software components of which we present three modules in this work. The key module, Chemoton, drives the exploration of reaction networks. For the exploration itself, we introduce two new algorithms for elementary-step searches that are based on Newton trajectories. The performance of these algorithms is assessed for a variety of reactions characterized by a broad chemical diversity in terms of bonding patterns and chemical elements. Chemoton successfully recovers the vast majority of these. We provide the resulting data, including large numbers of reactions that were not included in our reference set, to be used as a starting point for further explorations and for future reference.

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.

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.

Tracing the Primordial Chemical Life of Glycine: A Review from Quantum Chemical Simulations

Glycine (Gly), NH₂CH₂COOH, is the simplest amino acid. Although it has not been directly detected in the interstellar gas-phase medium, it has been identified in comets and meteorites, and its synthesis in these environments has been simulated in terrestrial laboratory experiments. Likewise, condensation of Gly to form peptides in scenarios resembling those present in a primordial Earth has been demonstrated experimentally. Thus, Gly is a paradigmatic system for biomolecular building blocks to investigate how they can be synthesized in astrophysical environments, transported and delivered by fragments of asteroids (meteorites, once they land on Earth) and comets (interplanetary dust particles that land on Earth) to the primitive Earth, and there react to form biopolymers as a step towards the emergence of life. Quantum chemical investigations addressing these Gly-related events have been performed, providing fundamental atomic-scale information and quantitative energetic data. However, they are spread in the literature and difficult to harmonize in a consistent way due to different computational chemistry methodologies and model systems. This review aims to collect the work done so far to characterize, at a quantum mechanical level, the chemical life of Gly, i.e., from its synthesis in the interstellar medium up to its polymerization on Earth.

Reaction Space Projector (ReSPer) for Visualizing Dynamic Reaction Routes Based on Reduced-Dimension Space

To analyze chemical reaction dynamics based on a reaction path network, we have developed the "Reaction Space Projector" (ReSPer) method with the aid of the dimensionality reduction method. This program has two functions: the construction of a reduced-dimensionality reaction space from a molecular structure dataset, and the projection of dynamic trajectories into the low-dimensional reaction space. In this paper, we apply ReSPer to isomerization and bifurcation reactions of the Au₅ cluster and succeed in analyzing dynamic reaction routes involved in multiple elementary reaction processes, constructing complicated networks (called "closed islands") of nuclear permutation-inversion (NPI) isomerization reactions, and elucidating dynamic behaviors in bifurcation reactions with reference to bundles of trajectories. Interestingly, in the second application, we find a correspondence between the contribution ratios in the ability to visualize and the symmetry of the morphology of closed islands. In addition, the third application suggests the existence of boundaries that determine the selectivity in bifurcation reactions, which was discussed in the phase space. The ReSPer program is a versatile and robust tool to clarify dynamic reaction mechanisms based on the reduced-dimensionality reaction space without prior knowledge of target reactions.

Electrochemical Dearomative Dicarboxylation of Heterocycles with Highly Negative Reduction Potentials

The dearomative dicarboxylation of stable heteroaromatics using CO₂ is highly challenging but represents a very powerful method for producing synthetically useful dicarboxylic acids, which can potentially be employed as intermediates of biologically active molecules such as natural products and drug leads. However, these types of transformations are still underdeveloped, and concise methodologies with high efficiency (e.g., high yield and high selectivity for dicarboxylations) have not been reported. We herein describe a new electrochemical protocol using the CO₂ radical anion (E_1/2 of CO₂ = -2.2 V in DMF and -2.3 V in CH₃CN vs SCE) that produces unprecedented trans-oriented 2,3-dicarboxylic acids from N-Ac-, Boc-, and Ph-protected indoles that exhibit highly negative reduction potentials (-2.50 to -2.94 V). On the basis of the calculated reduction potentials, N-protected indoles with reduction potentials up to -3 V smoothly undergo the desired dicarboxylation. Other heteroaromatics, including benzofuran, benzothiophene, electron-deficient furans, thiophenes, 1,3-diphenylisobenzofuran, and N-Boc-pyrazole, also exhibit reduction potentials more positive than -3 V and served as effective substrates for such dicarboxylations. The dicarboxylated products thus obtained can be derivatized into useful synthetic intermediates for biologically active compounds in few steps. We also show how the dearomative monocarboxylation can be achieved selectively by choice of the electrolyte, solvent, and protic additive; this strategy was then applied to the synthesis of an octahydroindole-2-carboxylic acid (Oic) derivative, which is a useful proline analogue.

Azo-Crosslinked Double-Network Hydrogels Enabling Highly Efficient Mechanoradical Generation

Double-network (DN) hydrogels have recently been demonstrated to generate numerous radicals by the homolytic bond scission of the brittle first network under the influence of an external force. The mechanoradicals thus generated can be utilized to trigger polymerization inside the gels, resulting in significant mechanical and functional improvements to the material. Although the concentration of mechanoradicals in DN gels is much higher than that in single-network hydrogels, a further increase in the mechanoradical concentration in DN gels will widen their application. In the present work, we incorporate an azoalkane crosslinker into the first network of DN gels. Compared with the traditional crosslinker N,N'-methylenebis(acrylamide), the azoalkane crosslinker causes a decrease in the yield stress but significantly increases the mechanoradical concentration of DN gels after stretching. In the azoalkane-crosslinked DN gels, the concentration of mechanoradicals can reach a maximum of ∼220 μM, which is 5 times that of the traditional crosslinker. In addition, DN gels with the azoalkane crosslinker show a much higher energy efficiency for mechanoradical generation. Interestingly, DN gels crosslinked by a mixture of azoalkane crosslinker and traditional crosslinker also exhibit excellent radical generation performance. The increase in the mechanoradical concentration accelerates polymerization and can broaden the application range of force-responsive DN gels to biomedical devices and soft robots.

Efficient Reaction Space Exploration with ChemTraYzer-TAD

The development of a reaction model is often a time-consuming process, especially if unknown reactions have to be found and quantified. To alleviate the reaction modeling process, automated procedures for reaction space exploration are highly desired. We present ChemTraYzer-TAD, a new reactive molecular dynamics acceleration technique aimed at efficient reaction space exploration. The new method is based on the basin confinement strategy known from the temperature-accelerated dynamics (TAD) acceleration method. Our method features integrated ChemTraYzer bond-order processing steps for the automatic and on-the-fly determination of the positions of virtual walls in configuration space that confine the system in a potential energy basin. We use the example of 1,3-dioxolane-4-hydroperoxide-2-yl radical oxidation to show that ChemTraYzer-TAD finds more than 100 different parallel reactions for the given set of reactants in less than 2 ns of simulation time. Among the many observed reactions, ChemTraYzer-TAD finds the expected typical low-temperature reactions despite the use of extremely high simulation temperatures up to 5000 K. Our method also finds a new concerted β-scission plus O₂ addition with a lower reaction barrier than the literature-known and so-far dominant β-scission.

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.

Expansive Quantum Mechanical Exploration of Chemical Reaction Paths

Quantum mechanical methods have been well-established for the elucidation of reaction paths of chemical processes and for the explicit dynamics of molecular systems. While they are usually deployed in routine manual calculations on reactions for which some insights are already available (typically from experiment), new algorithms and continuously increasing capabilities of modern computer hardware allow for exploratory open-ended computational campaigns that are unbiased and therefore enable unexpected discoveries. Highly efficient and even automated procedures facilitate systematic approaches toward the exploration of uncharted territory in molecular transformations and dynamics. In this work, we elaborate on such explorative approaches that range from reaction network explorations with (stationary) quantum chemical methods to explorative molecular dynamics and migrant wave packet dynamics. The focus is on recent developments that cover the following strategies. (i) Pruning search options for elementary reaction steps by heuristic rules based on the first-principles of quantum mechanics: Rules are required for reducing the combinatorial explosion of potentially reactive atom pairings, and rooting them in concepts derived from the electronic wave function makes them applicable to any molecular system. (ii) Enforcing reactive events by external biases: Inducing a reaction requires constraints that steer and direct elementary-step searches, which can be formulated in terms of forces, velocities, or supplementary potentials. (iii) Manual steering facilitated by interactive quantum mechanics: As ultrafast quantum chemical methods allow for real-time manual interactions with molecular systems, human-intuition-guided paths can be easily explored with suitable human-machine interfaces. (iv) New approaches for transition-state optimization with continuous curve representations can provide stable schemes to be driven in an automated way by allowing for an efficient tuning of the curve's parameters (instead of a manipulation of a collection of structures along the path), and (v) reactive molecular dynamics and direct wave packet propagation exploit the equations of motion of an underlying mechanical theory (usually, classical Newtonian mechanics or Schrödinger quantum mechanics). Explorative approaches are likely to replace the current state of the art in computational chemistry, because they reduce the human effort to be invested in reaction path elucidations, they are less prone to errors and bias-free, and they cover more extensive regions of the relevant configuration space. As a result, computational investigations that rely on these techniques are more likely to deliver surprising discoveries.

Radical Difunctionalization of Gaseous Ethylene Guided by Quantum Chemical Calculations: Selective Incorporation of Two Molecules of Ethylene

Ethylene, of which about 170 million tons are produced annually worldwide, is a fundamental C₂ feedstock that is widely used on an industrial scale for the synthesis of polyethylenes and polyvinylchlorides. Compared to other alkenes, however, the direct use of ethylene for the synthesis of fine chemicals such as pharmaceuticals and agrochemicals is limited, probably due to its small and gaseous character. We, herein, report a new radical difunctionalization strategy of ethylene, aided by quantum chemical calculations. Computationally proposed imidyl and sulfonyl radicals can be introduced into ethylene in the presence of an Ir photocatalyst under irradiation with blue light-emitting diodes (LEDs) (λ_max = 440 nm). The present reaction systems led to the selective incorporation of two molecules of ethylene into the substrate, which could be rationally explained by computational analysis.

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.

Carboxylation of a Palladacycle Formed via C(sp3 )-H Activation: Theory-Driven Reaction Design

Theory-driven organic synthesis is a powerful tool for developing new organic transformations. A palladacycle(II), generated from 8-methylquinoline via C(sp³ )-H activation, is frequently featured in the scientific literature, albeit that the reactivity toward CO₂ , an abundant, inexpensive, and non-toxic chemical, remains elusive. We have theoretically discovered potential carboxylation pathways using the artificial force induced reaction (AFIR) method, a density-functional-theory (DFT)-based automated reaction path search method. The thus obtained results suggest that the reduction of Pd(II) to Pd(I) is key to promote the insertion of CO₂ . Based on these computational findings, we employed various one-electron reductants, such as Cp*₂ Co, a photoredox catalyst under blue LED irradiation, and reductive electrolysis ((+)Mg/(-)Pt), which afforded the desired carboxylated products in high yields. After screening phosphine ligands under photoredox conditions, we discovered that bidentate ligands such as dppe promoted this carboxylation efficiently, which was rationally interpreted in terms of the redox potential of the Pd(II)-dppe complex as well as on the grounds of DFT calculations. We are convinced that these results could serve as future guidelines for the development of Pd(II)-catalyzed C(sp³ )-H carboxylation reactions with CO₂ .