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.

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.

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.

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.

Highly chemoselective ligands for Suzuki-Miyaura cross-coupling reaction based on virtual ligand-assisted screening.

Ligand screening is a crucial step in the development of transition metal catalysis, as it involves identifying the optimal ligand for a particular reaction from a large pool of candidate molecules. Conventionally, this process is performed through an experimental trial-and-error, which can be time-consuming and resource-intensive in many cases. One of the ideal strategies for streamlining this process is a transition state theory (TST)-based approach, which aims to design optimal catalysts that results in the best energy profile for the desired reaction. However, the implementation of TST-based ligand screening remains challenging mainly due to the large number of potential ligands that need to be individually evaluated through quantum chemical calculations. In this study, we experimentally demonstrated a practical TST-based ligand screening in accordance with our virtual ligand-assisted (VLA) screening strategy. As a case study, the electronic anc steric features of phosphine ligands that maximize chemoselectivity in the Suzuki-Miyaura cross-coupling (SMC) reaction of p-chlorophenyl triflate were determined through quantum chemical calculations using virtual ligands, and several phosphine ligands were suggested to exhibit high chemoselectivity. Based on this suggestion, we successfully found that tri(1-adamantyl)phosphine and tri(neopentyl)phosphine show high to excellent selectivity for the C-Cl bond activation. This case study suggests that the VLA screening strategy could be a useful tool for ligand screening.

Toward Ab Initio Reaction Discovery Using the Artificial Force Induced Reaction Method.

Predicting the whole process of a chemical reaction while solving kinetic equations presents an opportunity to realize an on-the-fly kinetic simulation that directly discovers chemical reactions with their product yields. Such a simulation avoids the combinatorial explosion of reaction patterns to be examined by narrowing the search space based on the kinetic analysis of the reaction path network, and would open a new paradigm beyond the conventional two-step approach, which requires a reaction path network prior to performing a kinetic simulation. The authors addressed this issue and developed a practical method by combining the artificial force induced reaction method with the rate constant matrix contraction method. Two algorithms are available for this purpose: a forward mode with reactants as the input and a backward mode with products as the input. This article first numerically verifies these modes for known reactions and then demonstrates their application to the actual reaction discovery. Finally, the challenges of this method and the prospects for ab initio reaction discovery are discussed.

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.

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 theory-driven synthesis of symmetric and unsymmetric 1,2-bis(diphenylphosphino)ethane analogues via radical difunctionalization of ethylene.

1,2-Bis(diphenylphosphino)ethane (DPPE) and its synthetic analogues are important structural motifs in organic synthesis, particularly as diphosphine ligands with a C2-alkyl-linker chain. Since DPPE is known to bind to many metal centers in a bidentate fashion to stabilize the corresponding metal complex via the chelation effect originating from its entropic advantage over monodentate ligands, it is often used in transition-metal-catalyzed transformations. Symmetric DPPE derivatives (Ar12P-CH2-CH2-PAr12) are well-known and readily prepared, but electronically and sterically unsymmetric DPPE (Ar12P-CH2-CH2-PAr22; Ar1≠Ar2) ligands have been less explored, mostly due to the difficulties associated with their preparation. Here we report a synthetic method for both symmetric and unsymmetric DPPEs via radical difunctionalization of ethylene, a fundamental C2 unit, with two phosphine-centered radicals, which is guided by the computational analysis with the artificial force induced reaction (AFIR) method, a quantum chemical calculation-based automated reaction path search tool. The obtained unsymmetric DPPE ligands can coordinate to several transition-metal salts to form the corresponding complexes, one of which exhibits distinctly different characteristics than the corresponding symmetric DPPE-metal complex.

Delayed fluorescence from inverted singlet and triplet excited states.

Hund's multiplicity rule states that a higher spin state has a lower energy for a given electronic configuration1. Rephrasing this rule for molecular excited states predicts a positive energy gap between spin-singlet and spin-triplet excited states, as has been consistent with numerous experimental observations over almost a century. Here we report a fluorescent molecule that disobeys Hund's rule and has a negative singlet-triplet energy gap of -11 ± 2 meV. The energy inversion of the singlet and triplet excited states results in delayed fluorescence with short time constants of 0.2 µs, which anomalously decrease with decreasing temperature owing to the emissive singlet character of the lowest-energy excited state. Organic light-emitting diodes (OLEDs) using this molecule exhibited a fast transient electroluminescence decay with a peak external quantum efficiency of 17%, demonstrating its potential implications for optoelectronic devices, including displays, lighting and lasers.

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.

Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks.

Selecting diverse molecules from unexplored areas of chemical space is one of the most important tasks for discovering novel molecules and reactions. This paper proposes a new approach for selecting a subset of diverse molecules from a given molecular list by using two existing techniques studied in machine learning and mathematical optimization: graph neural networks (GNNs) for learning vector representation of molecules and a diverse-selection framework called submodular function maximization. Our method, called SubMo-GNN, first trains a GNN with property prediction tasks, and then the trained GNN transforms molecular graphs into molecular vectors, which capture both properties and structures of molecules. Finally, to obtain a subset of diverse molecules, we define a submodular function, which quantifies the diversity of molecular vectors, and find a subset of molecular vectors with a large submodular function value. This can be done efficiently by using the greedy algorithm, and the diversity of selected molecules measured by the submodular function value is mathematically guaranteed to be at least 63% of that of an optimal selection. We also introduce a new evaluation criterion to measure the diversity of selected molecules based on molecular properties. Computational experiments confirm that our SubMo-GNN successfully selects diverse molecules from the QM9 dataset regarding the property-based criterion, while performing comparably to existing methods regarding standard structure-based criteria. We also demonstrate that SubMo-GNN with a GNN trained on the QM9 dataset can select diverse molecules even from other MoleculeNet datasets whose domains are different from the QM9 dataset. The proposed method enables researchers to obtain diverse sets of molecules for discovering new molecules and novel chemical reactions, and the proposed diversity criterion is useful for discussing the diversity of molecular libraries from a new property-based perspective.

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) (λmax = 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.

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.

Invited for the cover of this issue are Satoshi Maeda, Tsuyoshi Mita, and co-workers at ICReDD (Hokkaido University). The image depicts an Artificial Force Induced Reaction (AFIR) conducted on a supercomputer, which predicts a new chemical transformation and its application. Read the full text of the article at 10.1002/chem.202100812.

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.