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.

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.

An encompassed representation of timescale hierarchies in first-order reaction network

Complex networks are pervasive in various fields such as chemistry, biology, and sociology. In chemistry, first-order reaction networks are represented by a set of first-order differential equations, which can be constructed from the underlying energy landscape. However, as the number of nodes increases, it becomes more challenging to understand complex kinetics across different timescales. Hence, how to construct an interpretable, coarse-graining scheme that preserves the underlying timescales of overall reactions is of crucial importance. Here, we develop a scheme to capture the underlying hierarchical subsets of nodes, and a series of coarse-grained (reduced-dimensional) rate equations between the subsets as a function of time resolution from the original reaction network. Each of the coarse-grained representations guarantees to preserve the underlying slow characteristic timescales in the original network. The crux is the construction of a lumping scheme incorporating a similarity measure in deciphering the underlying timescale hierarchy, which does not rely on the assumption of equilibrium. As an illustrative example, we apply the scheme to four-state Markovian models and Claisen rearrangement of allyl vinyl ether (AVE), and demonstrate that the reduced-dimensional representation accurately reproduces not only the slowest but also the faster timescales of overall reactions although other reduction schemes based on equilibrium assumption well reproduce the slowest timescale but fail to reproduce the second-to-fourth slowest timescales with the same accuracy. Our scheme can be applied not only to the reaction networks but also to networks in other fields, which helps us encompass their hierarchical structures of the complex kinetics over timescales.

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.

Quantum chemical calculations for reaction prediction in the development of synthetic methodologies

Quantum chemical calculations have been used in the development of synthetic methodologies to analyze the reaction mechanisms of the developed reactions. Their ability to estimate chemical reaction pathways, including transition state energies and connected equilibria, has led researchers to embrace their use in predicting unknown reactions. This perspective highlights strategies that leverage quantum chemical calculations for the prediction of reactions in the discovery of new methodologies. Selected examples demonstrate how computation has driven the development of unknown reactions, catalyst design, and the exploration of synthetic routes to complex molecules prior to often laborious, costly, and time-consuming experimental investigations.

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.

Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.

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.

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.

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.

Leveraging algorithmic search in quantum chemical reaction path finding

Reaction path finding methods construct a graph connecting reactants and products in a quantum chemical energy landscape. They are useful in elucidating various reactions and provide footsteps for designing new reactions. Their enormous computational cost, however, limits their application to relatively simple reactions. This paper engages in accelerating reaction path finding by introducing the principles of algorithmic search. A new method called RRT/SC-AFIR is devised by combining rapidly exploring random tree (RRT) and single component artificial force induced reaction (SC-AFIR). Using 96 cores, our method succeeded in constructing a reaction graph for Fritsch-Buttenberg-Wiechell rearrangement within a time limit of 3 days, while the conventional methods could not. Our results illustrate that the algorithm theory provides refreshing and beneficial viewpoints on quantum chemical methodologies.

Enhancement of the mechanical and thermal transport properties of carbon nanotube yarns by boundary structure modulation

Carbon nanotubes (CNTs) exhibit extremely high nanoscopic thermal/electrical transport and mechanical properties. However, the macroscopic properties of assembled CNTs are significantly lower than those of CNTs because of the boundary structure between the CNTs. Therefore, it is crucial to understand the relationship between the nanoscopic boundary structure in CNTs and the macroscopic properties of the assembled CNTs. Previous studies have shown that the nanoscopic phonon transport and macroscopic thermal transport in CNTs are improved by Joule annealing because of the improved boundary Van-der-Waals interactions between CNTs via the graphitization of amorphous carbon. In this study, we investigate the mechanical strength and thermal/electrical transport properties of CNT yarns with and without Joule annealing at various temperatures, analyzing the phenomena occurring at the boundaries of CNTs. The obtained experimental and theoretical results connect the nanoscopic boundary interaction of CNTs in CNT yarns and the macroscopic mechanical and transport properties of CNT yarns.

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.

Kinetic Analysis of a Reaction Path Network Including Ambimodal Transition States: A Case Study of an Intramolecular Diels-Alder Reaction

This study proposes a methodology for the kinetic analysis of a reaction path network including ambimodal transition states (TSs), through which an ensemble of trajectories bifurcates to multiple minima in a phenomenon called dynamical bifurcation. The proposed methodology consists of three techniques: an automated reaction path search to construct a reaction path network including ambimodal TSs, an ab initio molecular dynamics simulation to evaluate the branching ratio, and the definition of rate constants incorporating this ratio. Applying the procedure to a Diels-Alder reaction, it was found that the inclusion of dynamical bifurcations is necessary to explain the experimental reaction yield of a byproduct. In addition, it was verified that the products take 10¹³ s to reach thermal equilibrium and that the experimental selectivity is determined by the dynamical bifurcations.

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 reaction route network for methanol decomposition on a Pt(111) surface

A reaction route network for the decomposition reaction of methanol on a Pt(111) surface was constructed by using the artificial force-induced reaction (AFIR) method, which can search for reaction paths automatically and systematically. Then, the network was kinetically analyzed by applying the rate constant matrix contraction (RCMC) method. Specifically, the time hierarchy of the network, the time evolution of the population initially given to CH₃ OH to the other species on the network, and the most favorable route from CH₃ OH to major and minor products were investigated by the RCMC method. Consistently to previous studies, the major product on the network was CO+4H, and the most favorable route proceeded through the following steps: CH₃ OH → CH₂ OH+H → HCOH+2H → HCO+3H → CO+4H. Furthermore, paths to byproducts found on the network and their kinetic importance were discussed. The present procedure combining AFIR and RCMC was thus successful in explaining the title reaction without using any information on its product or the reaction mechanism.

Visualization of reaction route map and dynamical trajectory in reduced dimension

In the quantum chemical approach, chemical reaction mechanisms are investigated based on a potential energy surface (PES). Automated reaction path search methods enable us to construct a global reaction route map containing multiple reaction paths corresponding to a series of elementary reaction processes. The on-the-fly molecular dynamics (MD) method provides a classical trajectory exploring the full-dimensional PES based on electronic structure calculations. We have developed two reaction analysis methods, the on-the-fly trajectory mapping method and the reaction space projector (ReSPer) method, by introducing a structural similarity to a pair of geometric structures and revealed dynamic aspects affecting chemical reaction mechanisms. In this review, we will present the details of these analysis methods and discuss the dynamics effects of reaction path curvature and reaction path bifurcation with applications to the CH₃OH + OH^- collision reaction and the Au₅ cluster branching and isomerization reactions.

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 .

Synthesis of Difluoroglycine Derivatives from Amines, Difluorocarbene, and CO2 : Computational Design, Scope, and Applications

A three-component reaction (3CR) for the synthesis of difluoroglycine derivatives has been achieved by using amines, difluorocarbene (generated in situ), and the abundant, inexpensive, and nontoxic C₁ source CO₂ . Various tert-amines and pyridine, (iso)quinoline, imidazole, thiazole, and pyrazole derivatives were incorporated, and the corresponding products were isolated in solid form without purification by column chromatography on silica gel. Detailed reaction profiles of the 3CR were obtained from computational analysis using DFT calculations, and the results critically suggest that simple ammonia is not applicable to this reaction. In addition, as strongly supported by computational predictions, a new reagent that can generate difluorocarbene at 0 °C without any additives was discovered. Finally, radical substitution reactions of the obtained difluoroglycine derivatives under photoredox conditions, as well as a synthetic application as an N-heterocyclic carbene ligand are shown.

Mining hydroformylation in complex reaction network via graph theory

Data science is introduced to identify the reactant, product, and reaction path in the chemical reaction network. Cobalt catalyzed hydroformylation is investigated where the reaction network is built via first principles calculations. The closeness centrality and high frequency node are found to be the reactant cobalt tetracarbonyl hydride. In addition, betweenness centrality uncovers three reaction paths which have the products of aldehyde, CH2O, and CO2, respectively. The energy profile determines that the reaction path leading to aldehyde is energetically favored; thus, the reaction path for cobalt catalyzed hydroformylation is identified without kinetics. Hence, the proposed approach can act as a first step towards understanding the complex chemical reaction network and towards further kinetic understanding of the chemical reaction.

Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data

Presently, quantum chemical calculations are widely used to generate extensive data sets for machine learning applications; however, generally, these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surfaces provides important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural data for both ground and transition states resulting from quantum chemical calculations. Our tool allows one to store, assemble, and analyze reaction pathway data. It combines relational database CGR DB for handling compounds and reactions as molecular graphs with a graph database architecture for pathway analysis by graph algorithms. Original condensed graph of reaction technology is used to store any chemical reaction as a single graph.