Computational study on the mechanism for the synthesis of active pharmaceutical ingredients nitrofurantoin and dantrolene in both solution and mechanochemical conditions

A combination of density functional theory (DFT) calculations and microkinetic simulations is applied to the study of condensation between N-acyl-hydrazides and aldehydes in acidic media to produce the active pharmaceutical ingredients (API) nitrofurantoin and dantrolene. Previous experimental reports have shown that the use of ball milling conditions leads to a reduction in the reaction time, which is associated with a significant reduction of waste. This result is reproduced by the current calculations, which additionally provide a detailed mechanistic explanation for this behavior.

Bimetallic Single-Atom Catalysts for Water Splitting

Green hydrogen from water splitting has emerged as a critical energy vector with the potential to spearhead the global transition to a fossil fuel-independent society. The field of catalysis has been revolutionized by single-atom catalysts (SACs), which exhibit unique and intricate interactions between atomically dispersed metal atoms and their supports. Recently, bimetallic SACs (bimSACs) have garnered significant attention for leveraging the synergistic functions of two metal ions coordinated on appropriately designed supports. BimSACs offer an avenue for rich metal-metal and metal-support cooperativity, potentially addressing current limitations of SACs in effectively furnishing transformations which involve synchronous proton-electron exchanges, substrate activation with reversible redox cycles, simultaneous multi-electron transfer, regulation of spin states, tuning of electronic properties, and cyclic transition states with low activation energies. This review aims to encapsulate the growing advancements in bimSACs, with an emphasis on their pivotal role in hydrogen generation via water splitting. We subsequently delve into advanced experimental methodologies for the elaborate characterization of SACs, elucidate their electronic properties, and discuss their local coordination environment. Overall, we present comprehensive discussion on the deployment of bimSACs in both hydrogen evolution reaction and oxygen evolution reaction, the two half-reactions of the water electrolysis process.

Pd-Catalyzed C(sp2)-H/C(sp2)-H Coupling of Limonene

Limonene undergoes a regioselective Pd(II)-catalyzed C(sp2)-H/C(sp2)-H coupling with acrylic acid esters and amides, α,β-unsaturated ketones, styrenes, and allyl acetate, affording novel 1,3-dienes. DFT computations gave results in accord with the experimental results and allowed for the formulation of a plausible mechanism. The postfunctionalization of one of the coupled products was achieved via a large-scale Sonogashira reaction conducted under micellar catalysis.

Revealing the Mechanistic Features of an Electrosynthetic Catalytic Reaction and the Role of Redox Mediators through DFT Calculations and Microkinetic Modeling

Organic electrosynthesis is an emerging field that provides original selectivity while adding features of atom economy, sustainability, and selectivity. Electrosynthesis is often enhanced by redox mediators or electroauxiliaries. The mechanistic understanding of organic electrosynthesis is however often limited by the low lifetime of intermediates and its difficult detection. In this work, we report a computational analysis of the mechanism of an appealing reaction previously reported by Mei and co-workers which is catalyzed by copper and employs iodide as redox mediator. Our scheme combines DFT calculations with microkinetic modeling and covers both the reaction in solution and the electrodic steps. A detailed mechanistic scheme is obtained which reproduces well experimental data and opens perspectives for the general treatment of these processes.

The Subtle Mechanism of Nickel-Photocatalyzed C(sp3)-H Cross-Coupling

This computational study revises and reformulates the mechanism for the cross-coupling reaction between chlorobenzene and tetrahydrofuran catalyzed by a Ni complex with the assistance of an Ir photocatalyst. This is a representative process of transition-metal photocatalysis, and variations of it have been reported by different experimental authors. It has been also the subject of previous computational studies, which we revise and extend. Density functional theory (DFT) calculations and microkinetic modeling indicate that the most efficient mechanism takes place through an energy-transfer step and involves a NiIII complex.

Efficiency Conceptualization Model: A Theoretical Method for Predicting the Turnover of Catalysts

In recent times, the theoretical prediction of catalytic efficiency is of utmost urgency. With the advent of density functional theory (DFT), reliable computations can delineate a quantitative aspect of the study. To this state-of-the-art approach, valuable incorporation would be a tool that can acknowledge the efficiency of a catalyst. In the current work, we developed the efficiency conceptualization model (ECM) that utilizes the quantum mechanical tool to achieve efficiency in terms of turnover frequency (TOF). Twenty-six experimentally designed transition metal (TM) water oxidation catalysts were chosen under similar experimental conditions of temperature, pressure, and pH to execute the same. The computations conclude that the Fe-based [Fe(OTf)2(Me2Pytacn)] (MWOC-17) is a highly active catalyst and, therefore, can endure for more time in the catalytic cycle. Our results conclude that the Ir-based catalysts [Cp*Ir(κ2-N,O)X] with MWOC-23: X=Cl; and MWOC-24: X=NO3 report the highest computed turnover numbers (TONs),τ c o m p u t e d T O N 0 ${\tau _{computed\;TON}^0 }$ of 406 and 490 against the highest experimental TONs,τ e x p e r i m e n t a l T O N ${\tau _{experimental\;TON} }$ of 1200 and 2000 respectively, whereas the Co-based [Co(12-TMC)]2+ (MWOC-19) has the lowest TONs (τ c o m p u t e d T O N 0 ${\tau _{computed\;TON}^0 }$ =19, τexperimental TON=16) among the chosen catalysts and thereby successful in corroborating the previous experimental results.

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions

Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots". The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, are highlighted. To facilitate the construction of these plots/maps, we introduce mikimo, a Python program that seamlessly integrates with our previously developed automated volcano builder, volcanic.

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

Modeling the alkylation of amines with alkyl bromides: explaining the low selectivity due to multiple alkylation

CONTEXT

Nucleophilic substitution reactions of aliphatic amines with alkyl halides represent a simple and direct mechanism for obtaining higher-order aliphatic amines. However, it is well known that these reactions suffer from low selectivity due to multiple alkylations, which is attributed to the higher reactivity of the newly formed amine. In order to provide a detailed explanation for this kind of system, we have investigated the reactivity of primary and secondary amines with 1-bromopropane and 2-bromopropane. The free energy profile in acetonitrile solution was obtained and a detailed microkinetic analysis was needed to analyze this complex reaction system. We have found that the product of the first alkylation is an ion pair corresponding to the protonated secondary amine and the bromide ion, which can transfer the proton to the reactant primary amine. Then, the newly formed secondary amine can also react, leading to a second alkylation to produce a tertiary protonated amine. Our modeling points out that both the proton transfer equilibria and the similar reactivity of the primary and secondary amines produce reduced selectivity. The proton transfer equilibria also contribute to slowing down the kinetics of the first alkylation.

METHODS

The exploration of the mechanism was done by geometry optimization using the CPCM/X3LYP/ma-def2-SVP method, followed by harmonic frequency calculation at this same level of theory. A composite approach was used to obtain the free energy profile, using the more accurate ωB97X-D3/ma-def2-TZVPP level of theory for electronic energy and the SMD model for the solvation free energy. These calculations were performed with the ORCA 4 program. The detailed microkinetic analysis was done using the Kintecus program.

Multi-Time-Scale Simulation of Complex Reactive Mixtures: How Do Polyoxometalates Form?

Understanding the dynamics of reactive mixtures still challenges both experiments and theory. A relevant example can be found in the chemistry of molecular metal-oxide nanoclusters, also known as polyoxometalates. The high number of species potentially involved, the interconnectivity of the reaction network, and the precise control of the pH and concentrations needed in the synthesis of such species make the theoretical/computational treatment of such processes cumbersome. This work addresses this issue relying on a unique combination of recently developed computational methods that tackle the construction, kinetic simulation, and analysis of complex chemical reaction networks. By using the Bell-Evans-Polanyi approximation for estimating activation energies, and an accurate and robust linear scaling for correcting the computed pKa values, we report herein multi-time-scale kinetic simulations for the self-assembly processes of polyoxotungstates that comprise 22 orders of magnitude, from tens of femtoseconds to months of reaction time. This very large time span was required to reproduce very fast processes such as the acid/base equilibria (at 10-12 s), relatively slow reactions such as the formation of key clusters such as the metatungstate (at 103 s), and the very slow assembly of the decatungstate (at 106 s). Analysis of the kinetic data and of the reaction network topology shed light onto the details of the main reaction mechanisms, which explains the origin of kinetic and thermodynamic control followed by the reaction. Simulations at alkaline pH fully reproduce experimental evidence since clusters do not form under those conditions.

Broad-Scope Amination of Aryl Sulfamates Catalyzed by a Palladium Phosphine Complex

Among phenol-derived electrophiles, aryl sulfamates are attractive substrates since they can be employed as directing groups for C-H functionalization prior to catalysis. However, their use in C-N coupling is limited only to Ni catalysis. Here, we describe a Pd-based catalyst with a broad scope for the amination of aryl sulfamates. We show that the N-methyl-2-aminobiphenyl palladacycle supported by the PCyp2ArXyl2 ligand (Cyp = cyclopentyl; ArXyl2 = 2,6-bis(2,6-dimethylphenyl)phenyl) efficiently catalyzes the C-N coupling of aryl sulfamates with a variety of nitrogen nucleophiles, including anilines, primary and secondary alkyl amines, heteroaryl amines, N-heterocycles, and primary amides. DFT calculations support that the oxidative addition of the aryl sulfamate is the rate-determining step. The C-N coupling takes place through a cationic pathway in the polar protic medium.

Important Role of NH-Carbazole in Aryl Amination Reactions Catalyzed by 2-Aminobiphenyl Palladacycles

2-Aminobiphenyl palladacycles are among the most successful precatalysts for Pd-catalyzed cross-coupling reactions, including aryl amination. However, the role of NH-carbazole, a byproduct of precatalyst activation, remains poorly understood. Herein, the mechanism of the aryl amination reactions catalyzed by a cationic 2-aminobiphenyl palladacycle supported by a terphenyl phosphine ligand, PCyp₂Ar^Xyl2 (Cyp = cyclopentyl; Ar^Xyl2 = 2,6-bis(2,6-dimethylphenyl)phenyl), P1, has been thoroughly investigated. Combining computational and experimental studies, we found that the Pd(II) oxidative addition intermediate reacts with NH-carbazole in the presence of the base (NaO ^t Bu) to yield a stable aryl carbazolyl Pd(II) complex. This species functions as the catalyst resting state, providing the amount of monoligated LPd(0) species required for catalysis and minimizing Pd decomposition. In the case of a reaction with aniline, an equilibrium between the carbazolyl complex and the on-cycle anilido analogue is established, which allows for a fast reaction at room temperature. In contrast, heating is required in a reaction with alkylamines, whose deprotonation involves coordination to the Pd center. A microkinetic model was built combining computational and experimental data to validate the mechanistic proposals. In conclusion, our study shows that despite the rate reduction observed in some reactions by the formation of the aryl carbazolyl Pd(II) complex, this species reduces catalyst decomposition and could be considered an alternative precatalyst in cross-coupling reactions.

Overreact, an in silico lab: Automative quantum chemical microkinetic simulations for complex chemical reactions

Today's demand for precisely predicting chemical reactions from first principles requires research to go beyond Gibbs' free energy diagrams and consider other effects such as concentrations and quantum tunneling. The present work introduces overreact, a novel Python package for propagating chemical reactions over time using data from computational chemistry only. The overreact code infers all differential equations and parameters from a simple input that consists of a set of chemical equations and quantum chemistry package outputs for each chemical species. We evaluate some applications from the literature: gas-phase eclipsed-staggered isomerization of ethane, gas-phase umbrella inversion of ammonia, gas-phase degradation of methane by chlorine radical, and three solvation-phase reactions. Furthermore, we comment on a simple solvation-phase acid-base equilibrium. We show how it is possible to achieve reaction profiles and information matching experiments.

Site-selective photocatalytic functionalization of peptides and proteins at selenocysteine

The importance of modified peptides and proteins for applications in drug discovery, and for illuminating biological processes at the molecular level, is fueling a demand for efficient methods that facilitate the precise modification of these biomolecules. Herein, we describe the development of a photocatalytic method for the rapid and efficient dimerization and site-specific functionalization of peptide and protein diselenides. This methodology, dubbed the photocatalytic diselenide contraction, involves irradiation at 450 nm in the presence of an iridium photocatalyst and a phosphine and results in rapid and clean conversion of diselenides to reductively stable selenoethers. A mechanism for this photocatalytic transformation is proposed, which is supported by photoluminescence spectroscopy and density functional theory calculations. The utility of the photocatalytic diselenide contraction transformation is highlighted through the dimerization of selenopeptides, and by the generation of two families of protein conjugates via the site-selective modification of calmodulin containing the 21st amino acid selenocysteine, and the C-terminal modification of a ubiquitin diselenide.

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.

Water Oxidation Catalyzed by a Bioinspired Tetranuclear Manganese Complex: Mechanistic Study and Prediction

Density functional theory calculations were utilized to elucidate the water oxidation mechanism catalyzed by polyanionic tetramanganese complex a [Mn^III ₃ Mn^IV O₃ (CH₃ COO)₃ (A-α-SiW₉ O₃₄ )]^6- . Theoretical results indicated that catalytic active species 1 (Mn₄ ^{III,III,IV,IV} ) was formed after O₂ formation in the first turnover. From 1, three sequential proton-coupled electron transfer (PCET) oxidations led to the Mn^IV -oxyl radical 4 (Mn₄ ^IV,IV,IV,IV -O⋅). Importantly, 4 had an unusual butterfly-shaped Mn₂ O₂ core for the two substrate-coordinated Mn sites, which facilitated O-O bond formation via direct coupling of the oxyl radical and the adjacent Mn^IV -coordinated hydroxide to produce the hydroperoxide intermediate Int1 (Mn₄ ^III,IV,IV,IV -OOH). This step had an overall energy barrier of 24.9 kcal mol^-1 . Subsequent PCET oxidation of Int1 to Int2 (Mn₄ ^III,IV,IV,IV -O₂ ⋅) enabled the O₂ release in a facile process. Furthermore, apart from the Si-centered complex, computational study suggested that tetramanganese polyoxometalates with Ge, P, and S could also catalyze the water oxidation process, where those bearing P and S likely present higher activities.

Study of Pd-catalyzed Selective Mono- and Di-C(sp3)-H Bond Activation: A Bi-ligand Model

Controlling the number of C-H bond activation is a long-standing challenge in organic synthesis. Recently, Yu's group demonstrated that in Pd-catalyzed alanine's arylation, pyridine-type ligands favor a mono-C-H bond activation, while quinoline-type ligands favor a di-C-H bond activation. To disclose the underlying principles, a theoretical study (density functional theory (DFT)) has been carried out. Our study indicates that a mono-ligand model, which is generally adopted in the community, does not reproduce the experimentally observed mono-/di-selectivity, while a bi-ligand model can rationalize the experimental observations well, including the observed diastereoselectivity in diarylation. The electron-rich pyridine-type ligands with less steric congestion can promote the C-H bond activation reaction of alanine derivatives. The quinoline-type ligands have a better π back-donation interaction with the metal, which makes a more active C-H bond activation than the pyridine-type ligands for this reaction. This bi-ligand model, which is a necessity, allows the understanding and future design of a dual ligand effect in C-H bond activation.

Successes and challenges in using machine-learned activation energies in kinetic simulations

The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has focused on how the accuracy of ML predictions filter through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions and decomposition of 3-hydroperoxypropanal. We find that the kinetic modelling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable, and when one should take more care in applications to kinetics modelling.

Chemical reaction network knowledge graphs: the OntoRXN ontology

The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows.

Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation

While computational screening with first-principles density functional theory (DFT) is essential for evaluating candidate catalysts, limitations in accuracy typically prevent the prediction of experimentally relevant activities. Exemplary of these challenges are homogeneous water oxidation catalysts (WOCs) where differences in experimental conditions or small changes in ligand structure can alter rate constants by over an order of magnitude. Here, we compute mechanistically relevant electronic and energetic properties for 19 mononuclear Ru transition-metal complexes (TMCs) from three experimental water oxidation catalysis studies. We discover that 15 of these TMCs have experimental activities that correlate with a single property, the ionization potential of the Ru(II)-O₂ catalytic intermediate. This scaling parameter allows the quantitative understanding of activity trends and provides insight into the rate-limiting behavior. We use this approach to rationalize differences in activity with different experimental conditions, and we qualitatively analyze the source of distinct behavior for different electronic states in the other four catalysts. Comparison to closely related single-atom catalysts and modified WOCs enables rationalization of the source of rate enhancement in these WOCs.

Experimental and Computational Studies on the Acetate-Assisted C-H Activation of N-Aryl Imidazolium Salts at Rhodium and Iridium: A Chloride Additive Changes the Selectivity of C-H Activation

Combined experimental and computational mechanistic studies of the reactions of unsymmetrical, para-substituted N-aryl imidazolium salts, L2-R₁,R₂, at [MCl₂Cp*]₂ (M = Rh, Ir) in the presence of NaOAc are reported. These proceed via intermediate N-heterocyclic carbene complexes that then allow an internal competition between two differently substituted aryl rings toward C-H activation to be monitored. At 348 K in dichloroethane C-H activation of the aryl with the more electron-withdrawing substituents is generally favored. DFT calculations show similar barriers for proton transfer and dissociative HOAc/Cl^- ligand substitution, with proton transfer favoring electron-donating substituents, and ligand substitution favoring electron-withdrawing substituents. Microkinetic simulations reproduce the experimental preference implying that the ligand substitution step dominates selectivity. For several substrates, notably L2-F,OMe and L2-F,H, running the C-H activation reactions at 298 K in the presence of added [Et₄N]Cl reverses the selectivity. The greater availability of chloride in solution makes an alternative dissociative interchange ligand substitution mechanism accessible, leaving proton transfer as selectivity determining and so favoring electron-donating substituents. Our results highlight the potential importance of the ligand substitution step in the interpretation of substituent effects and demonstrate how a simple additive, [Et₄N]Cl, can have a dramatic effect on selectivity by changing the mechanism of ligand substitution.

Computational Studies on the Mechanisms for Deaminative Amide Hydrogenation by Homogeneous Bifunctional Catalysts

The deaminative hydrogenation of amides is one of the most convenient pathways for the synthesis of amines and alcohols. The ideal source of reducing equivalents for this reaction is molecular hydrogen, though, in practice, this approach requires high pressures and temperatures, with many catalysts achieving only small turnover numbers and frequencies. Nonetheless, during the last ten years, this field has made major advances towards larger turnovers under milder conditions thanks to the development of bifunctional catalysts. These systems promote the heterolytic cleavage of hydrogen into proton and hydride by combining a basic ligand with an acidic metal centre. The present review focuses on the computational study of the reaction mechanism underlying bifunctional catalysis. This review is structured around the fundamental steps of this mechanism, namely the C=O and C–N hydrogenation of the amide, the C–N protonolysis of the hemiaminal, the C=O hydrogenation of the aldehyde, and the competition between hydrogen activation and catalyst deactivation. In line with the complexity of the mechanism, we also provide a perspective on the use of microkinetic models. Both Noyori- and Milstein-type catalysts are discussed and compared.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Isomerization of Functionalized Olefins Using the Dinuclear Catalyst [PdI(μ-Br)(PtBu3)]2: A Mechanistic Study

In a combined experimental and computational study, the isomerization activity of the dinuclear palladium(I) complex [Pd^I(μ‐Br)(P^tBu₃)]₂ towards allyl arenes, esters, amides, ethers, and alcohols has been investigated. The calculated energy profiles for catalyst activation for two alternative dinuclear and mononuclear catalytic cycles, and for catalyst deactivation are in good agreement with the experimental results. Comparison of experimentally observed E/Z ratios at incomplete conversion with calculated kinetic selectivities revealed that a substantial amount of product must form via the dinuclear pathway, in which the isomerization is promoted cooperatively by two palladium centers. The dissociation barrier towards mononuclear Pd species is relatively high, and once the catalyst enters the energetically more favorable mononuclear pathway, only a low barrier has to be overcome towards irreversible deactivation.

The Impact of Computational Uncertainties on the Enantioselectivity Predictions: A Microkinetic Modeling of Ketone Transfer Hydrogenation with a Noyori-type Mn-diamine Catalyst

Selectivity control is one of the most important functions of a catalyst. In asymmetric catalysis the enantiomeric excess (e.e.) is a property of major interest, with a lot of effort dedicated to developing the most enantioselective catalyst, understanding the origin of selectivity, and predicting stereoselectivity. Herein, we investigate the relationship between predicted selectivity and the uncertainties in the computed energetics of the catalytic reaction mechanism obtained by DFT calculations in a case study of catalytic asymmetric transfer hydrogenation (ATH) of ketones with an Mn-diamine catalyst. Data obtained from our analysis of DFT data by microkinetic modeling is compared to results from experiment. We discuss the limitations of the conventional reductionist approach of e.e. estimation from assessing the enantiodetermining steps only. Our analysis shows that the energetics of other reaction steps in the reaction mechanism have a substantial impact on the predicted reaction selectivity. The uncertainty of DFT calculations within the commonly accepted energy ranges of chemical accuracy may reverse the predicted e.e. with the non-enantiodetermining steps contributing to e.e. deviations of up to 25 %.

Understanding Ball Milling Mechanochemical Processes with DFT Calculations and Microkinetic Modeling

Mechanochemistry is an emerging field with many potential applications in sustainable chemistry. But despite the growing interest in the field, its underlying mechanistic foundations are not fully understood yet. This work presents the application of computational tools, such as DFT calculations in continuum and microkinetic modeling, to the analysis of mechanically activated procedures. Two reactions reported in previous experimental publications were studied: (i) a series of Diels-Alder reactions and (ii) the synthesis of sulfonylguanidines. Calculations succeed in reproducing experimentally reported reaction times. The procedures were mostly standard, coupled with some sensitive choices in terms of starting concentrations and dielectric constant. This means that these particular reactions accelerated by ball milling followed the same mechanism as the equivalent reactions in solution. The implications of this result on the general picture of mechanochemical processes are discussed.

DFT Analysis of Organotin Catalytic Mechanisms in Dehydration Esterification Reactions for Terephthalic Acid and 2,2,4,4-Tetramethyl-1,3-cyclobutanediol

Polyesters synthesized from 2,2,4,4-tetramethyl-1,3-cyclobutanediol (TMCD) and terephthalic acid (TPA) are improved alternatives to toxic polycarbonates based on bisphenol A. In this work, we use ωB97X-D/LANL2DZdp calculations, in the presence of a benzaldehyde polarizable continuum model solvent, to show that esterification of TMCD and TPA will reduce and subsequently dehydrate a dimethyl tin oxide catalyst, becoming ligands on the now four-coordinate complex. This reaction then proceeds most plausibly by an intramolecular acyl-transfer mechanism from the tin complex, aided by a coordinated proton donor such as hydronium. These findings are a key first step in understanding polyester synthesis and avoiding undesirable side reactions during production.

Mechanistic Insight into the Origin of Stereoselectivity in the Ribose-Mediated Strecker Synthesis of Alanine

Enantioenriched amino acids are produced in a hydrolytic kinetic resolution of racemic aminonitriles mediated by chiral pentose sugars. Experimental kinetic and spectroscopic results combined with DFT computational studies and microkinetic modeling help to identify the nature of the intermediate species and provide insight into the stereoselectivity of their hydrolysis in the prebiotically relevant ribose-alanine system. These studies support a synergistic role for sugars and amino acids in the emergence of homochirality in biological molecules.

Mechanistic understanding of the Cu(i)-catalyzed domino reaction constructing 1-aryl-1,2,3-triazole from electron-rich aryl bromide, alkyne, and sodium azide: a DFT study

The mechanism of the Cu(i)-catalyzed domino reaction furnishing 1-aryl-1,2,3-triazole assisted by CuI and 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) is explored with density functional theory (DFT) calculations.

Faster palladium-catalyzed arylation of simple arenes in the presence of a methylketone: beneficial effect of an a priori interfering solvent in C–H activation

A methyl ketone accelerates the Pd-catalyzed C–H activation of simple arenes. A noninnocent solvent, it reacts in a reversible way without affecting the outcome of the reaction.

Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst

For disordered catalysts such as atomically dispersed "single-atom" metals on amorphous silica, the active sites inherit different properties from their quenched-disordered local environments. The observed kinetics are site-averages, typically dominated by a small fraction of highly active sites. Standard sampling methods require expensive ab initio calculations at an intractable number of sites to converge on the site-averaged kinetics. We present a new method that efficiently estimates the site-averaged turnover frequency (TOF). The new estimator uses the same importance learning algorithm [Vandervelden et al., React. Chem. Eng. 5, 77 (2020)] that we previously used to compute the site-averaged activation energy. We demonstrate the method by computing the site-averaged TOF for a simple disordered lattice model of an amorphous catalyst. The results show that with the importance learning algorithm, the site-averaged TOF and activation energy can now be obtained concurrently with orders of magnitude reduction in required ab initio calculations.