Composition- and Condition-Dependent Kinetics of Homogeneous Ester Hydrogenation by a Mn-Based Catalyst

HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts

A method is introduced for the automated analysis of reactivity exploration for extended in silico databases of transition-metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical data sets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst data sets opens new opportunities for the design of high-performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration.

Performance of homogeneous catalysts viewed in dynamics

Effective assessment of catalytic performance is the foundation for the rational design and development of new catalysts with superior performance. The ubiquitous screening/optimization studies use reaction yields as the sole performance metric in an approach that often neglects the complexity of the catalytic system and intrinsic reactivities of the catalysts. Using an example of hydrogenation catalysis, we examine the transient behavior of catalysts that are often encountered in activation, deactivation and catalytic turnover processes. Each of these processes and the reaction environment in which they take place are gradually shown to determine the real-time catalyst speciation and the resulting kinetics of the overall catalytic reaction. As a result, the catalyst performance becomes a complex and time-dependent metric defined by multiple descriptors apart from the reaction yield. This behaviour is not limited to hydrogenation catalysis and affects various catalytic transformations. In this feature article, we discuss these catalytically relevant descriptors in an attempt to arrive at a comprehensive depiction of catalytic performance.

A theoretical study of asymmetric ketone hydrogenation catalyzed by Mn complexes: from the catalytic mechanism to the catalyst design

Herein, a density functional theory (DFT) study was performed to investigate asymmetric ketone hydrogenation (AKH) catalyzed by Mn complexes, from the catalytic mechanism to the catalyst design. The calculated results indicated that the Mn(CO)₂-PSiNSiP (A1, PSiNSiP = P(Ph)₂Si(CH₃)₂NSi(CH₃)₂P(Ph)₂) pincer complex has potential high catalytic activity for ketone hydrogenation. The Mn(CO)-LYB (B, LYB = P(Ph)₂Si(CH₃)₂NSi(CH₃)₂P(Me)₂) pincer complex was then designed to catalyze AKH with good stereoselectivity. The hydrogen transfer (HT) step is the chirality-determining step. To avoid the enantiomer of Mn(CO)₂-LYB, which could eliminate the high stereoselectivity during AKH, novel Mn complexes with quadridentate ligands, such as Mn(CO)-LYC (C, LYC = P(CH₃)₂CH₂Si(CH₃)NSi(CH₃)(Si(CH₃)CH₂P(CH₃)₂)CH₂P(Ph)₂) and Mn(CO)-LYD (D, LYD = P(CH₃)₂CH₂Si(CH₃)NSi(CH₃)(Si(CH₃)CH₂P(CH₃)₂)CH₂P(Cy)₂), were designed to drive AKH with medium stereoselectivity. In order to increase the stereoselectivity of AKH, Mn(CO)-LYE (E, LYE = PH₂CH₂Si(CH₃)NSi(CH₃)(Si(CH₃)CH₂P(CH₃)₂)CH₂P(Ph)₂) and Mn(CO)-LYF (F, LYF = PH₂CH₂Si(CH₃)NSi(CH₃)(Si(CH₃)CH₂P(CH₃)₂)CH₂P(Cy)₂) were further designed and showed very good stereoselectivity, which is due to the lower deformation energy and stronger interactions between the ketone substrates and catalysts. This work may shed light on the design of cheap metal catalysts with a new ligand framework for the asymmetric hydrogenation (AH) of CX bonds (X = O, N).

Solvent-Assisted Ketone Reduction by a Homogeneous Mn Catalyst

The choice of a solvent and the reaction conditions often defines the overall behavior of a homogeneous catalytic system by affecting the preferred reaction mechanism and thus the activity and selectivity of the catalytic process. Here, we explore the role of solvation in the mechanism of ketone reduction using a model representative of a bifunctional Mn-diamine catalyst through density functional theory calculations in a microsolvated environment by considering explicit solvent and fully solvated ab initio molecular dynamics simulations for the key elementary steps. Our computational analysis reveals the possibility of a Meerwein–Ponndorf–Verley (MPV) type mechanism in this system, which does not involve the participation of the N–H moiety and the formation of a transition-metal hydride species in ketone conversion. This path was not previously considered for Mn-based metal–ligand cooperative transfer hydrogenation homogeneous catalysis. The MPV mechanism is strongly facilitated by the solvent molecules present in the reaction environment and can potentially contribute to the catalytic performance of other related catalyst systems. Calculations indicate that, despite proceeding effectively in the second coordination sphere of the transition-metal center, the MPV reaction path retains the enantioselectivity preference induced by the presence of the small chiral N,N′-dimethyl-1,2-cyclohexanediamine ligand within the catalytic Mn(I) complex.

Solvent-mediated outer-sphere CO2 electro-reduction mechanism over the Ag111 surface

The electrocatalytic CO2 reduction reaction (CO2RR) is one of the key technologies of the clean energy economy. Molecular-level understanding of the CO2RR process is instrumental for the better design of electrodes operable at low overpotentials with high current density. The catalytic mechanism underlying the turnover and selectivity of the CO2RR is modulated by the nature of the electrocatalyst, as well as the electrolyte liquid, and its ionic components that form the electrical double layer (EDL). Herein we demonstrate the critical non-innocent role of the EDL for the activation and conversion of CO2 at a high cathodic bias for electrocatalytic conversion over a silver surface as a representative low-cost model cathode. By using a multiscale modeling approach we demonstrate that under such conditions a dense EDL is formed, which hinders the diffusion of CO2 towards the Ag111 electrocatalyst surface. By combining DFT calculations and ab initio molecular dynamics simulations we identify favorable pathways for CO2 reduction directly over the EDL without the need for adsorption to the catalyst surface. The dense EDL promotes homogeneous phase reduction of CO2 via electron transfer from the surface to the electrolyte. Such an outer-sphere mechanism favors the formation of formate as the CO2RR product. The formate can undergo dehydration to CO via a transition state stabilized by solvated alkali cations in the EDL.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.