Variability of Ligand pKa during Homogeneously Catalyzed Aqueous Methanol Dehydrogenation

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.

Computational mechanistic studies of ruthenium catalysed methanol dehydrogenation

Homogeneous ruthenium catalysed methanol dehydrogenation could become a key reaction for hydrogen production in liquid fuel cells. In order to improve existing catalytic systems, mechanistic insight is paramount in directing future studies. Herein, we describe what computational mechanistic research has taught us so far about ruthenium catalysed dehydrogenation reactions. In general, two mechanistic pathways can be operative in these reactions: a metal-centered or a metal-ligand cooperative (Noyori-Morris type) minimum energy reaction pathway (MERP). Discerning between these mechanisms on the basis of computational studies has proven to be highly input dependent, and to circumvent pitfalls it is important to consider several factors, such as solvent effects, metal-ligand cooperativity, alternative geometries, and complex electronic structures of metal centres. This Frontiers article summarizes the reported computational research performed on ruthenium catalyzed dehydrogenation reactions performed in the past decade, and serves as a guide for future research.

Mechanism of iron complexes catalyzed in the N-formylation of amines with CO2 and H2: the superior performance of N-H ligand methylated complexes

CO2 hydrogenation into value-added chemicals not only offer an economically beneficial outlet but also help reduce the emission of greenhouse gases. Herein, the density functional theory (DFT) studies have been carried out on CO2 hydrogenation reaction for formamide production catalyzed by two different N-H ligand types of PNP iron catalysts. The results suggest that the whole mechanistic pathway has three parts: (i) precatalyst activation, (ii) hydrogenation of CO2 to generate formic acid (HCOOH), and (iii) amine thermal condensation to formamide with HCOOH. The lower turnover number (TON) of a bifunctional catalyst system in hydrogenating CO2 may attribute to the facile side-reaction between CO2 and bifunctional catalyst, which inhibits the generation of active species. Regarding the bifunctional catalyst system addressed in this work, we proposed a ligand participated mechanism due to the low pKa of the ligand N-H functional in the associated stage in the catalytic cycle. Remarkably, catalysts without the N-H ligand exhibit the significant transfer hydrogenation through the metal centered mechanism. Due to the excellent catalytic nature of the N-H ligand methylated catalyst, the N-H bond was not necessary for stabilizing the intermediate. Therefore, we confirmed that N-H ligand methylated catalysts allow for an efficient CO2 hydrogenation reaction compared to the bifunctional catalysts. Furthermore, the influence of Lewis acid and strong base on catalytic N-formylation were considered. Both significantly impact the catalytic performance. Moreover, the catalytic activity of PNMeP-based Mn, Fe and Ru complexes for CO2 hydrogenation to formamides was explored as well. The energetic span of Fe and Mn catalysts are much closer to the precious metal Ru, which indicates that such non-precious metal catalysts have potentially valuable applications.

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.

Accurate and rapid prediction of pKa of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach

Data-augmented high-throughput QM approach to compute pK_a of transition metal hydride complexes with hDFT accuracy and low cost.