Organic reaction mechanism classification using machine learning

Bispericyclic Ambimodal Dimerization of Pentafulvene: The Origin of Asynchronicity and Kinetic Selectivity of the Endo Transition State

The propensity of fulvenes to undergo dimerization has long been known, although the in-depth mechanism and electronic behavior during dimerization are still elusive. Herein, we made an attempt to gain insights into the reactivity of pentafulvene for Diels-Alder (DA) and [6 + 4]-cycloadditions via conventional and ambimodal routes. The result emphasizes that pentafulvene dimerization preferentially proceeds through a unique bifurcation mechanism where two DA pathways merge together to produce two degenerate [4 + 2]-cycloadducts from a single TS. Despite the [6 + 4]-cycloadduct being thermodynamically preferred, [4 + 2]-cycloaddition reactions are kinetically driven. Singlet biradicaloid is involved in through-space 6e- delocalization as a secondary orbital interaction that originates asynchronicity and stabilizes the bispericyclic transition state (TS). The transformation of various actively participating intrinsic bonding orbitals (IBOs) unambiguously forecasts the formation of multiple products from a single TS and rationalizes the mechanism of ambimodal reactions that are rather difficult to probe with other analyses. The changes in active IBOs clearly distinguish the conventional reactions from bifurcation reactions and can be employed to characterize and confirm the ambimodal mechanism. This report gains a crucial theoretical insight into the mechanism of bifurcation, the origin of asynchronicity, and electronic behavior in ambimodal TS, which will certainly be of enormous value for future studies.

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8 kJ mol-1, significantly outperforming competitive models.

Mechanistic insights into on-surface reactions from isothermal temperature-programmed X-ray photoelectron spectroscopy

On-surface synthesis often proceeds under kinetic control due to the irreversibility of key reaction steps, rendering kinetic studies pivotal. The accurate quantification of reaction rates also bears potential for unveiling reaction mechanisms. Temperature-Programmed X-ray Photoelectron Spectroscopy (TP-XPS) has emerged as an analytical tool for kinetic studies with splendid chemical and sufficient temporal resolution. Here, we demonstrate that the common linear temperature ramps lead to fitting ambiguities. Moreover, pinpointing the reaction order remains intricate, although this key parameter entails information on atomistic mechanisms. Yet, TP-XPS experiments with a stepped temperature profile comprised of isothermal segments facilitate the direct quantification of rate constants from fitting time courses. Thereby, rate constants are obtained for a series of temperatures, which allows independent extraction of both activation energies and pre-exponentials from Arrhenius plots. By using two analogous doubly versus triply brominated aromatic model compounds, we found that their debromination on Ag(111) is best modeled by second-order kinetics and thus proceeds via the involvement of a second, non-obvious reactant. Accordingly, we propose that debromination is activated by surface supplied Ag adatoms. This hypothesis is supported by Density Functional Theory (DFT) calculations. We foresee auspicious prospects for this TP-XPS variant for further exploring the kinetics and mechanisms of on-surface reactions.

Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis

Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time-consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on-line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron-demand Diels-Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer-readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning.

Iterative design of training data to control intricate enzymatic reaction networks

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions

Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach.

Mechanistic Investigation of the Rhodium-Catalyzed Transfer Hydroarylation Reaction Involving Reversible C-C Bond Activation

Carbon-carbon (C-C) bonds are ubiquitous but are among the least reactive bonds in organic chemistry. Recently, catalytic approaches to activate C-C bonds by transition metals have demonstrated the synthetic potential of directly reorganizing the skeleton of small molecules. However, these approaches are usually restricted to strained molecules or rely on directing groups, limiting their broader impact. We report a detailed mechanistic study of a rare example of catalytic C-C bond cleavage of unstrained alcohols that enables reversible ketone transfer hydroarylation under Rh-catalysis. Combined insight from kinetic analysis, in situ nuclear magnetic resonance (NMR) monitoring, and density functional theory (DFT) calculations supports a symmetric catalytic cycle, including a key reversible β-carbon elimination event. In addition, we provide evidence regarding the turnover-limiting step, the catalyst resting state, and the role of the sterically encumbered NHC ligand. The study further led to an improved catalytic system with the discovery of two air-stable precatalysts that showed higher activity for the transformation in comparison to the original conditions.

Hydration of Propargyl Esters Catalyzed by Gold(I) Complexes with Phosphoramidite Calix[4]pyrrole Cavitands as Ligands

We report the synthesis and characterization of two diastereomeric phosphoramidite calix[4]pyrrole cavitands and their corresponding gold(I) complexes, 2in•Au(I)•Cl and 2out•Au(I)•Cl, featuring the metal center directed inward and outward with respect to their aromatic cavity. We studied the catalytic activity of the complexes in the hydration of a series of propargyl esters as the benchmarking reaction. All substrates were equipped with a six-membered ring substituent either lacking or including a polar group featuring different hydrogen bond acceptor (HBA) capabilities. We designed the substrates with the polar group to form 1:1 inclusion complexes of different stabilities with the catalysts. In the case of 2in•Au(I)•OTf, the 1:1 complex placed the alkynyl group of the bound substrate close to the metal center. We compared the obtained results with those of a model phosphoramidite gold(I) complex lacking a calix[4]pyrrole cavity. We found that for all catalysts, the presence of an increasingly polar HBA group in the substrate provoked a decrease in the hydration rate constants. We attributed this result to the competing coordination of the HBA group of the substrate for the Au(I) metal center of the catalysts.

A general neural network model co-driven by mechanism and data for the reliable design of gas-liquid T-junction microdevices

In recent years, many models have been developed to describe the gas-liquid microdispersion process, which mainly rely on mechanistic analysis and may not be universally applicable. In order to provide a more comprehensive model and, most significantly, to provide a model for design, we have established a general database of microbubble generation in T-junction microdevices, including 854 data points from 12 pieces of literature. A neural network model that combines mechanistic and data modeling is developed. By transfer learning, more accurate results can be obtained. Additionally, we have proposed a design method that enables a relative deviation of less than 5% from the expected bubble size. A new device was designed and prepared to confirm the reliability of the method, which can prepare smaller bubbles than other common T-junction devices. In this way, a general and universal database and model are established and a design method for a gas-liquid T-junction microreactor is developed.

Kinetics of ligand exchange in solution: a quantitative mass spectrometry approach

Complex speciation and exchange kinetics of labile ligands are critical parameters for understanding the reactivity of metal complexes in solution. We present a novel approach to determine ligand exchange parameters based on electrospray ionization mass spectrometry (ESI-MS). The introduction of isotopically labelled ligands to a solution of metal host and unlabelled ligands allows the quantitative investigation of the solution-phase equilibria. Furthermore, ion mobility separation can target individual isomers, such as ligands bound at specific sites. As a proof of concept, we investigate the solution equilibria of labile pyridine ligands coordinated in the cavity of macrocyclic porphyrin cage complexes bearing diamagnetic or paramagnetic metal centres. The effects of solvent, porphyrin coordination sphere, transition metal, and counterion on ligand dissociation are discussed. Rate constants and activation parameters for ligand dissociation in the solution can be derived from our ESI-MS approach, thereby providing mechanistic insights that are not easily obtained from traditional solution-phase techniques.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Reaction mechanism - explored with the unified reaction valley approach

One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 Mpro; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field.