AUTOGRAPH: Chemical Reaction Networks in 3D

Understanding and analyzing large-scale reaction networks is a fundamental challenge due to their complexity and size, often beyond human comprehension. In this paper, we introduce AUTOGRAPH, the first web-based tool designed for the interactive three-dimensional (3D) visualization and construction of reaction networks. AUTOGRAPH emphasizes ease of use, allowing users to intuitively build, modify, and explore individual reaction networks in real time. The platform supports a wide range of formats, including CHEMKIN, ensuring compatibility and seamless integration with existing data. Key features of AUTOGRAPH include advanced 3D visualization techniques combined with a fast force-directed algorithm, shortest-path searching, and filtering, facilitating the in-depth exploration of reaction networks. By providing detailed and interactive visualizations, our tool enhances users' ability to comprehend, analyze, and present complex reaction networks, making it a valuable resource for researchers dealing with intricate chemical systems.

CuH-Catalyzed Regio- and Enantioselective Hydroallylation of 1-Trifluoromethylthioalkenes: Leaving Group-Dependent Stereochemistry

A copper-catalyzed regio- and enantioselective hydroallylation of 1-trifluoromethylthioalkenes with hydrosilanes and allylic electrophiles has been developed. The judicious choice of chiral ligands successfully promotes the enantioselective C(sp3)-C(sp3) bond formation at the α-position of trifluoromethylthio (SCF3) group, which is otherwise difficult to perform by other means. Experimental and computational mechanistic studies reveal that leaving groups of allylic electrophiles significantly influence the enantioselectivity as well as chemoselectivity.

Microkinetic Assessment of Ligand-Exchanging Catalytic Cycles

Computational chemistry has become a fundamental part of the understanding and optimization of catalytic processes. Among these, the characterization of homogeneous organometallic catalysts, combining an active transition metal atom and set of ligands, is one of the main fields of application of these kinds of studies. More recently, microkinetic studies have been employed to bridge the gap between experimental measurements such as conversion or selectivity and the Gibbs free energies gathered by computations. In this work, we have developed an automated framework (MicroKatc) for microkinetic analysis, to tackle the yet understudied effect of ligand exchange processes that modify the nature of the catalytic scaffold in situ. We report the application of such a framework to the rhodium-catalyzed hydroformylation of ethylene, confirming the acceleration of the reaction as trimethylphosphine (PMe3) displaces the carbonyl ligands in the catalyst by means of simulations at variable phosphine concentrations, as well as the determination of the degree of rate control (DRC) and apparent activation energies throughout the catalytic process.

Facilitating Transition State Search with Minimal Conformational Sampling Using Reaction Graph

Elucidating transition states (TSs) is crucial for understanding chemical reactions. The reliability of traditional TS search approaches depends on input conformations that require significant effort to prepare. Previous automated methods for generating input reaction conformations typically involve extensive exploration of a large conformational space. Such exhaustive search can be complicated by the rapid growth of the conformational space, especially for reactions involving many rotatable bonds, multiple reacting molecules, and numerous bond formations and dissociations. To address this problem, we propose a new approach that generates reaction conformations for TS searches with minimal reliance on sampling. This method constructs a pseudo-TS structure based on a reaction graph containing bond formation and dissociation information and modifies it to produce reactant and product conformations. Tested on three different benchmarks, our method consistently generated suitable conformations without necessitating extensive sampling, demonstrating its potential to significantly improve the applicability of automated TS searches. This approach offers a valuable tool for a broad range of applications such as reaction mechanism analysis and network exploration.

Exploration of the Global Minimum and Conical Intersection with Bayesian Optimization

Conventional molecular geometry searches on a potential energy surface (PES) utilize energy gradients from quantum chemical calculations. However, replacing energy calculations with noisy quantum computer measurements generates errors in the energies, which makes geometry optimization using the energy gradient difficult. One gradient-free optimization method that can potentially solve this problem is Bayesian optimization (BO). To use BO in geometry search, an acquisition function (AF), which involves an objective variable, must be defined suitably. In this study, we propose a strategy for geometry searches using BO and examine the appropriate AFs to explore two critical structures: the global minimum (GM) on the singlet ground state (S0) and the most stable conical intersection (CI) point between S0 and the singlet excited state. We applied our strategy to two molecules and located the GM and the most stable CI geometries with high accuracy for both molecules. We also succeeded in the geometry searches even when artificial random noises were added to the energies to simulate geometry optimization using noisy quantum computer measurements.

Acceleration of Diffusion in Ab Initio Nanoreactor Molecular Dynamics and Application to Hydrogen Sulfide Oxidation

The computational description of chemical reactivity can become extremely complex when multiple different reaction products and intermediates come into play, forming a chemical reaction network. Therefore, computational methods for the automated construction of chemical reaction networks have been developed in the last decades. One of these methods, ab initio nanoreactor molecular dynamics (NMD), is based on external forces enhancing reactivity by e.g., periodically compressing the system and allowing it to relax. However, during the relaxation process, a significant simulation time is required to allow energy to dissipate and molecules to diffuse, making this part of the NMD simulation computationally intensive. This work aims to improve NMD by accelerating the diffusion process in the relaxation phase. We systematically investigate the speedup of reaction discovery gained by diffusion acceleration, leading to a factor of up to 28 in discovery frequency. Diffusion-accelerated nanoreactor molecular dynamics (DA-NMD) is then used to construct a reaction network of hydrogen sulfide oxidation under atmospheric conditions, where reactions are automatically detected by a change in the bond order and bond distance. A reaction network of 108 molecular species and 399 elementary reactions was constructed starting from hydrogen sulfide, hydroxy radicals, and molecular oxygen covering a broad variety of sulfur-oxygen chemistry and oxidation states of the sulfur atom ranging from -II to +VI.

Discovery of New Synthetic Routes of Amino Acids in Prebiotic Chemistry

The origin of life on Earth remains one of the most perplexing challenges in biochemistry. While numerous bottom-up experiments under prebiotic conditions have provided valuable insights into the spontaneous chemical genesis of life, there remains a significant gap in the theoretical understanding of the complex reaction processes involved. In this study, we propose a novel approach using a roto-translationally invariant potential (RTIP) formulated with pristine Cartesian coordinates to facilitate the simulation of chemical reactions. By employing RTIP pathway sampling to explore the reactivity of primitive molecules, we identified several low-energy reaction mechanisms, such as two-hydrogen-transfer hydrogenation and HCOOH-catalyzed hydration and amination. This led to the construction of a comprehensive reaction network, illustrating the synthesis pathways for glycine, serine, and alanine. Further thermodynamic analysis highlights the pivotal role of formaldimine as a key precursor in amino acid synthesis, owing to its more favorable reactivity in coupling reactions compared to the traditionally recognized hydrogen cyanide. Our study demonstrates that the RTIP methodology, coupled with a divide-and-conquer strategy, provides new insights into the simulation of complex reaction processes, offering promising applications for advancing organic design and synthesis.

Modeling conformational changes in alginic acid oligomers induced by external forces

In this study, the mechanism and nature of mechanical force-induced conformational transitions of alginate oligomers with different ratios of β-d-mannuronic acid (M unit) and α-l-guluronic acid (G unit) units were investigated. The influence of the type of glycosidic linkage in either homo- or heterooligomers on the nature of conformational transitions was also considered. For this purpose, two different theoretical methods were used: quantum mechanics (QM) at the DFT level with the EGO (Enforced Geometry Optimization) approach previously tested also for other saccharide systems, and molecular dynamics (MD) simulations within hybrid interaction potentials, which take into account both the ab initio (QM) level of theory and classical molecular mechanics (MM) force fields. This allowed to characterize in detail the structural and energetic properties of the conformational transition occurring upon the influence of external, mechanical forces (e.g. ring conformations at the path of ring-inversion process as well as the energies corresponding to initial, final and intermediate states). The results indicate qualitatively various responses against the applied force, depending on the G:M ratio, which have their source in differing topologies of glycosidic linkage in either G or M units. This is of potential relevance for determining the content of naturally heterogeneous alginate chains by the AFM experimental studies. The effects of explicit solvent and non-zero temperature are not of primarily importance in the context of determined stretching properties.

Quantum Chemical Model Calculations of Adhesion and Dissociation between Epoxy Resin and Si-Containing Molecules

There is no doubt that when solid surfaces are modified, the functional groups and atoms directly bonded to solid atoms play a major role in adsorption interactions with molecules or resins. In this study, the adhesion and dissociation between epoxy resin and molecules containing Si atoms were analyzed. The analysis, conducted in contact with the solid surface of silicon, utilized quantum chemical calculations based on a molecular model. We compared some Si-containing molecular models to test quantum chemical calculations that contribute to the study of adhesion and dissociation between epoxy resins and solid surfaces somehow other than simple potential energy curve calculations. The AFIR (artificial force induced reaction) method, implemented in the GRRM (global reaction route mapping) program, was employed to separate an epoxy resin model molecule and three types of silicon compounds (Si(CH3)2(OH)2, Si(CH3)4, and (CH3)2SiF2) in three directions, determining their minimum dissociation energy when changing the applied energy by 2.5 kJ/mol. In systems with weak hydrogen bonds, such as Si(CH3)4 or (CH3)2SiF2, the energy required for dissociation was not large; however, in systems with strong hydrogen bonds, such as Si(CH3)2(OH)2, dissociation was more difficult in the vertical direction. Although anisotropy due to hydroxyl groups was calculated in the horizontal direction, dissociation remained relatively easy.

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.

Catalytic asymmetric fragmentation of cyclopropanes

The stereoselective activation of alkanes constitutes a long-standing and grand challenge for chemistry. Although metal-containing enzymes oxidize alkanes with remarkable ease and selectivity, chemical approaches have largely been limited to transition metal-based catalytic carbon-hydrogen functionalizations. Alkanes can be protonated to form pentacoordinated carbonium ions and fragmented into smaller hydrocarbons in the presence of strong Brønsted acids. However, catalytic stereocontrol over such reactions has not previously been accomplished. We show here that strong and confined acids catalyze highly enantioselective fragmentations of a variety of cyclopropanes into the corresponding alkenes, expanding the boundaries of catalytic selective alkane activation. Computational studies suggest the involvement of the long-debated cycloproponium ions.

Mechanochemical generation of aryl barium nucleophiles from unactivated barium metal

Organobarium reagents are of interest as homologues of the Grignard reagents based on organomagnesium compounds due to their unique reactivity as well as regio- and stereoselectivity. However, reactions involving organobarium reagents are less developed in comparison to reactions involving Grignard reagents due to the lack of a simple and economical synthetic method and their high reactivity. To the best of our knowledge, there is no established method for the direct synthesis of organobarium compounds from commercially available bulk barium metal and organic halides. So far, the generation of organobarium compounds usually requires the use of activated barium (Rieke barium), which significantly reduces the practical utility of organobarium reagents and hinders the development of new organobarium-mediated transformations. Here, we present a mechanochemical strategy based on ball-milling that facilitates the direct generation of various aryl barium nucleophiles from commercially available unactivated barium metal and aryl halides without complicated pre-activation processes. Our simple mechanochemical protocol allows the rapid development of novel carbon-silicon-bond-forming reactions with hydrosilanes mediated by aryl barium nucleophiles; importantly, these reactions are difficult to achieve using other Grignard-type carbon nucleophiles. To the best of our knowledge, this is the first example of a nucleophilic substitution reaction involving an aryl barium species. Furthermore, this mechanochemical strategy established the first example of a nucleophilic addition to a carbonyl compound involving an aryl barium nucleophile. Preliminary theoretical calculations using the artificial force-induced reaction (AFIR) method to reveal the reaction mechanism of the hydrosilane arylation are also described.

Reactant Discovery with an Ab Initio Nanoreactor: Exploration of Astrophysical N-Heterocycle Precursors and Formation Pathways

The incorporation of nitrogen atoms into cyclic compounds is essential for terrestrial life; nitrogen-containing (N-)heterocycles make up DNA and RNA nucleobases, several amino acids, B vitamins, porphyrins, and other components of biomolecules. The discovery of these molecules on meteorites with non-terrestrial isotopic abundances supports the hypothesis of exogenous delivery of prebiotic material to early Earth; however, there has been no detection of these species in interstellar environments, indicating that there is a need for greater knowledge of their astrochemical formation and destruction pathways. Here, we present results of simulations of gas-phase pyrrole and pyridine formation from an ab initio nanoreactor, a first-principles molecular dynamics simulation method that accelerates reaction discovery by applying non-equilibrium forces that are agnostic to individual reaction coordinates. Using the nanoreactor in a retrosynthetic mode, starting with the N-heterocycle of interest and a radical leaving group, then considering the discovered reaction pathways in reverse, a rich landscape of N-heterocycle-forming reactivity can be found. Several of these reaction pathways, when mapped to their corresponding minimum energy paths, correspond to novel barrierless formation pathways for pyridine and pyrrole, starting from both detected and hypothesized astrochemical precursors. This study demonstrates how first-principles reaction discovery can build mechanistic knowledge in astrochemical environments as well as in early Earth models such as Titan's atmosphere where N-heterocycles have been tentatively detected.

Reaction dynamics of S(3P) with 1,3-butadiene and isoprene: crossed-beam scattering, low-temperature flow experiments, and high-level electronic structure calculations

Sulfur atoms serve as key players in diverse chemical processes, from astrochemistry at very low temperature to combustion at high temperature. Building upon our prior findings, showing cyclization to thiophenes following the reaction of ground-state sulfur atoms with dienes, we here extend this investigation to include many additional reaction products, guided by detailed theoretical predictions. The outcomes highlight the complex formation of products during intersystem crossing (ISC) to the singlet surfaces. Here, we employed crossed-beam velocity map imaging and high-level ab initio methods to explore the reaction of S(3P) with 1,3-butadiene and isoprene under single-collision conditions and in low-temperature flows. For the butadiene reaction, our experimental results show the formation of thiophene via H2 loss, a 2H-thiophenyl radical through H loss, and thioketene through ethene loss at a slightly higher collision energy compared to previous observations. Complementary Chirped-Pulse Fourier-Transform mmWave spectroscopy (CP-FTmmW) measurements in a uniform flow confirmed the formation of thioketene in the reaction at 20 K. For the isoprene reaction, we observed analogous products along with the 2H-thiophenyl radical arising from methyl loss and C3H4S (loss of ethene or H2 + acetylene). CP-FTmmW detected the formation of thioformaldehyde via loss of 1,3-butadiene, again in the 20 K flow. Coupled-cluster calculations on the pathways found by the automated kinetic workflow code KinBot support these findings and indicate ISC to the singlet surface, leading to the generation of various long-lived intermediates, including 5-membered heterocycles.

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

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.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

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.

Ring Expansion of Cyclic Boronates via Oxyboration of Arynes

The oxyboration of arynes was achieved for the first time. A series of 2-aryl-1,3,2-dioxaborolane derivatives were reacted with aryne precursors in the presence of CsF to give the corresponding ring-expanded seven-membered borinic acid esters via selective boron-oxygen bond activation. Preliminary experimental mechanistic studies and density functional theory (DFT) calculations suggest that this unprecedented aryne oxyboration proceeds through the formation of boron ate complexes of arylboronates with CsF, followed by aryne insertion into the boron-oxygen bond.

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.

Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase

Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.

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.

Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations

We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.

Skeletal rearrangement of a boron-containing annulenic molecule into a macrocycle bridged by an electronically stabilized boron cation

An annulenic molecule containing a three-coordinate chloroborane moiety, which exhibits a borane-olefin proximity effect, undergoes a skeletal rearrangement upon chloride abstraction, to generate a three-dimensional macrocyclic molecule featuring a borocenium (η5-cyclopentadienyl-B+-R) structure.

An energy decomposition and extrapolation scheme for evaluating electron transfer rate constants: a case study on electron self-exchange reactions of transition metal complexes

A simple approach to the analysis of electron transfer (ET) reactions based on energy decomposition and extrapolation schemes is proposed. The present energy decomposition and extrapolation-based electron localization (EDEEL) method represents the diabatic energies for the initial and final states using the adiabatic energies of the donor and acceptor species and their complex. A scheme for the efficient estimation of ET rate constants is also proposed. EDEEL is semi-quantitative by directly evaluating the seam-of-crossing region of two diabatic potentials. In a numerical test, EDEEL successfully provided ET rate constants for electron self-exchange reactions of thirteen transition metal complexes with reasonable accuracy. In addition, its energy decomposition and extrapolation schemes provide all the energy values required for activation-strain model (ASM) analysis. The ASM analysis using EDEEL provided rational interpretations of the variation of the ET rate constants as a function of the transition metal complexes. These results suggest that EDEEL is useful for efficiently evaluating ET rate constants and obtaining a rational understanding of their magnitudes.

A Combined Reaction Path Search and Hybrid Solvation Method for the Systematic Exploration of Elementary Reactions at the Solid-Liquid Interface

We present a combined simulation method of single-component artificial force induced reaction (SC-AFIR) and effective screening medium combined with the reference interaction site model (ESM-RISM), termed SC-AFIR+ESM-RISM. SC-AFIR automatically and systematically explores the chemical reaction pathway, and ESM-RISM directly simulates the precise electronic structure at the solid-liquid interface. Hence, SC-AFIR+ESM-RISM enables us to explore reliable reaction pathways at the solid-liquid interface. We applied it to explore the dissociation pathway of an H2O molecule at the Cu(111)/water interface. The reaction path networks of the whole reaction and the minimum energy paths from H2O to H2 + O depend on the interfacial environment. The qualitative difference in the energy diagrams and the resulting change in the kinematically favored dissociation pathway upon changing the solvation environments are discussed. We believe that SC-AFIR+ESM-RISM will be a powerful tool to reveal the details of chemical reactions in surface catalysis and electrochemistry.

Insights into the Luminescence Quantum Yields of Cyclometalated Iridium(III) Complexes: A Density Functional Theory and Machine Learning Approach

Cyclometalated iridium(III) complexes have been used in various optical materials, including organic light-emitting diodes (OLEDs) and photocatalysts, and a deeper understanding and prediction of their luminescence quantum yields (LQYs) greatly aid in accelerating material design. In this study, we integrated density functional theory (DFT) calculations with machine learning (ML) techniques to extract factors controlling LQY. Although a substantial data set of Ir(III) complexes and their LQYs is indispensable for constructing accurate ML models to predict LQYs, generating this type of data set is challenging due to the complexities associated with ab initio calculations of LQYs. To address this issue, we investigated the nonradiative decay process of nine Ir(III) complexes emitting blue to green, each exhibiting varying experimental LQYs, by using DFT calculations. For all nine complexes, the quenching process was induced by the rotation of the single bond in one of the ligands, which converted the six-coordinate structure to the five-coordinate structure. Since the decay mechanism was common for the nine Ir(III) complexes, parameters correlated with LQYs could be used as objective variables instead of LQYs. Based on this idea, we collected a data set featuring Ir(III) complexes and the energy differences between their six- and five-coordinate triplet structures, which correlated with LQYs. We also constructed ML models using the calculated LQYs as the objective variables with the parameters from the ground-state calculations as explanatory variables. The analyses of the constructed model revealed that the LUMO energy of the ligand made the most significant negative contribution to LQY. This suggests that the potential energy surface of the metal-to-ligand charge transfer (MLCT) excited state, which stabilizes the six-coordinate structure, is reduced by decreasing the energy of the unoccupied orbitals.

Combined molecular dynamics and coordinate driving method for automatically searching complicated reaction pathways

The combined molecular dynamics and coordinate driving (MD/CD) method is updated and generalized in this work to broaden its applications in automatically searching reaction pathways for complicated reactions. In this updated version, MD simulations are performed with the GFN's family of methods to systematically sample conformers for almost any systems with atomic numbers Z ≤ 86. The improved CD procedure is greatly accelerated by applying a pre-screening stage at the semiempirical GFN2-xTB level. An automatic module based on the Marcus theory and its improved version (the Wolynes theory) is designed to include single electron transfer (SET) processes into reaction pathways. The capabilities of this method are demonstrated by exploring the most possible reaction pathways of three experimentally reported reactions: the organophosphine-catalyzed trans phosphinoboration, the Fe(II) complex-mediated C(sp2)-H borylation reaction, and the SET-triggered deaminative radical cross-coupling reaction. Comprehensive reaction networks are obtained for all three reactions with reasonable computational costs. Detailed mechanisms for these reactions can account for the reported experimental facts.

Computational survey of humin formation from 5-(hydroxymethyl)furfural under basic conditions

A comprehensive reaction-path search for the oligomerization of 5-(hydroxymethyl)furfural (HMF) based on quantum chemical calculations was conducted to clarify the mechanism of humin formation in the oxidation of HMF to furan-2,5-dicarboxylic acid (FDCA), in which humin is a typical macromolecular byproduct. The present procedure repeatedly utilizes the multi-component artificial-force-induced reaction (MC-AFIR) method to investigate multistep oligomerization reactions. Although humin formation has been reported even in reagent-grade HMFs with 97-99% purity during their storage at low temperatures, no direct addition path of two HMFs with <185 kJ mol^-1 barrier has been found, suggesting humin formation is caused by a reaction with impurities. Based on the reaction conditions, we considered the reactions of HMF + H₂O, HMF + OH^-, and HMF + O₂ and identified three reaction paths with <65 kJ mol^-1 barrier for the reaction of HMF + OH^-. Further, the suppression of humin formation by the acetal protection of HMF is computationally confirmed.

Chemical modification of dimethylpolysiloxane for enhancement of CO2 binding enthalpy

The intermittent increase in CO₂ concentration in the atmosphere is a serious problem that contributes to climate change; the combustion of fossil fuels produces the majority of CO₂, and technology is needed to capture it efficiently. Various CO₂ capture materials have been developed so far. Membrane separation, in particular, has an advantage over other capture technologies due to its ease of use. Poly(dimethylsiloxane) (PDMS) has been widely used as a membrane material for CO₂ capture because of its high gas permeability. However, despite their high CO₂ permeance, PDMS membranes are still in their infancy, especially regarding CO₂ selectivity due to the weak interaction between CO₂ and PDMS. Here we evaluated the CO₂ interaction with the PDMS chain at the atomic scale and attempted to improve the CO₂ affinity of the PDMS chain using density functional theory (DFT). Specifically, we substituted elements in the Si-O framework with other elements and substituted the methyl groups with other chemical groups, and incorporated metallic elements such as Mg and Ti. All the chemical modifications by main group elements resulted in physisorption, but chemisorption of CO₂ was observed in PDMS incorporating metallic elements. Since several modes of CO₂ binding were observed in PDMS with incorporated metal elements, the binding enthalpy and binding mode were analyzed. As a result of various chemical modifications, it was found that introducing earth metal elements into PDMS was the most effective way to enhance the interaction between PDMS and CO₂.

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.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis

Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment of reaction mixture components consistent with a reaction coordinate, and is not agnostic to the chemical nature of transformations. Conformer exploration, reactive event identification, and reaction network analysis are the main steps taken for identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring the reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events.

From Cyclo[18]carbon to the Novel Nanostructures-Theoretical Predictions

In this paper, we present a number of novel pure-carbon structures generated from cyclo[18]carbon. Due to the very high reactivity of cyclo[18]carbon, it is possible to link these molecules together to form bigger molecular systems. In our studies, we generated new structures containing 18, 36 and 72 carbon atoms. They are of different shapes including ribbons, sheets and tubes. All these new structures were obtained in virtual reactions driven by external forces. For every reaction, the energy requirement was evaluated exactly when the corresponding transition state was found or it was estimated through our new approach. A small HOMO-LUMO gap in these nanostructures indicates easy excitations and the multiple bonds network indicates their high reactivity. Both of these factors suggest that some potential applications of the new nanostructures are as components of therapeutically active carbon quantum dots, terminal fragments of graphene or carbon nanotubes obtained after fracture or growing in situ in catalytic reactions leading to the formation of carbonaceous materials.

Dynamically Hidden Reaction Paths in the Reaction of CF3+ + CO

Reaction paths on a potential energy surface are widely used in quantum chemical studies of chemical reactions. The recently developed global reaction route mapping (GRRM) strategy automatically constructs a reaction route map, which provides a complete picture of the reaction. Here, we thoroughly investigate the correspondence between the reaction route map and the actual chemical reaction dynamics for the CF₃⁺ + CO reaction studied by guided ion beam tandem mass spectrometry (GIBMS). In our experiments, FCO⁺, CF₂⁺, and CF⁺ product ions were observed, whereas if the collision partner is N₂, only CF₂⁺ is observed. Interestingly, for reaction with CO, GRRM-predicted reaction paths leading to the CF⁺ + F₂CO product channel are found at a barrier height of about 2.5 eV, whereas the experimentally obtained threshold for CF⁺ formation was 7.48 ± 0.15 eV. In other words, the ion was not obviously observed in the GIBMS experiment, unless a much higher collision energy than the requisite energy threshold was provided. On-the-fly molecular dynamics simulations revealed a mechanism that hides these reaction paths, in which a non-statistical energy distribution at the first collisionally reached transition state prevents the reaction from proceeding along some reaction paths. Our results highlight the existence of dynamically hidden reaction paths that may be inaccessible in experiments at specific energies and hence the importance of reaction dynamics in controlling the destinations of chemical reactions.

Elastic Image Pair Method for Finding Transition States on Potential Energy Surfaces Using Only First Derivatives

Herein, an elastic image pair (EIP) method is proposed to search transition states between two potential-energy minima using only first derivatives. In this method, two images are generated, and the spring forces are added to the images to control the distance between the two images. Transition states are reached when the force and the distance of the image pair are both converged. A set of test molecules is optimized using the EIP method, which shows its efficiency in transition state searching compared to other methods. This new method is more stable and reliable in finding transition states with much less computations.

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.

New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited

The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH₃ (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.

Synthesis, Structure, and Dynamics of Chiral Eight-Membered Cyclic Molecules with Thienylene and Cyclopropylene Units Alternately Connected

A rhodium(II)-catalyzed asymmetric cyclooligomerization of bifunctional monomers possessing triazolyl and vinyl groups at 2,3- and 3,4-positions on the thiophene ring is studied. Structurally interesting cyclic dimers in which thienylene and cyclopropylene units are alternately connected are obtained as the major components. The eight-membered rings in the center are non-planar and adopt a tub-shaped conformation. We also observe the phenomenon of racemization caused by a tub-to-tub ring-flipping, the activation energy of which is determined as 108 kJ mol -1 by electronic circular dichroism spectra measurement.

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.

Structural Characterization of Nickel-Doped Aluminum Oxide Cations by Cryogenic Ion Trap Vibrational Spectroscopy

Isolated nickel-doped aluminum oxide cations (NiO_m)(Al₂O₃)_n(AlO)⁺ with m = 1-2 and n = 1-3 are investigated by infrared photodissociation (IRPD) spectroscopy in combination with density functional theory and the single-component artificial force-induced reaction method. IRPD spectra of the corresponding He-tagged cations are reported in the 400-1200 cm^-1 spectral range and assigned based on a comparison to calculated harmonic IR spectra of low-energy isomers. Simulated spectra of the lowest energy structures generally match the experimental spectra, but multiple isomers may contribute to the spectra of the m = 2 series. The identified structures of the oxides (m = 1) correspond to inserting a Ni-O moiety into an Al-O bond of the corresponding (Al₂O₃)_1-3(AlO)⁺ cluster, yielding either a doubly or triply coordinated Ni²⁺ center. The m = 2 clusters prefer similar structures in which the additional O atom either is incorporated into a peroxide unit, leaving the oxidation state of the Ni²⁺ atom unchanged, or forms a biradical comprising a terminal oxygen radical anion Al-O^•- and a Ni³⁺ species. These clusters represent model systems for under-coordinated Ni sites in alumina-supported Ni catalysts and should prove helpful in disentangling the mechanism of selective oxidative dehydrogenation of alkanes by Ni-doped catalysts.

AutoMeKin2021: An open-source program for automated reaction discovery

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin.

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH₂)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks

Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation, but computational cost and inconsistent reaction coverage are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization and transition state convergence algorithms that are common to many prediction methodologies. These components are implemented in the context of yet another reaction program (YARP), our reaction prediction package with which we report reaction discovery benchmarks for organic single-step reactions, thermal degradation of a γ-ketohydroperoxide, and competing ring-closures in a large organic molecule. Compared with recent benchmarks, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization by nearly 100-fold and increasing convergence of transition states. This combination of ultra-low cost and high reaction coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications such as chemical degradation, where computational cost is a bottleneck.