DFT Virtual Screening Identifies Rhodium–Amidinate Complexes As Potential Homogeneous Catalysts for Methane-to-Methanol Oxidation

On the Mechanism of Light-Driven O2 Evolution by the Mn(III) Complex [Mn(salpd)(OH2)]+ and Quinone

In this study, we apply TD-DFT and DFT calculations to explore the mechanistic details of O2 evolution in an artificial system that closely resembles Photosystem II (PSII). The reaction involves mononuclear Mn(III) complex [Mn(salpd)(OH2)]+ and p-benzoquinone under light-driven conditions. Our calculations reveal that the Schiff-base ligand salpd plays a crucial role in several key steps of the reaction, including the light-mediated oxidation of [Mn(salpd)(OH2)]+ to [Mn(salpd)(OH)]+ by p-benzoquinone, the subsequent oxidation of [Mn(salpd)(OH)]+ to the key Mn(V) intermediate [Mn(salpd)(O)]+, and the critical O-O bond formation step. This role is primarily due to the high propensity of the salpd ligand to undergo oxidation by one unit. This characteristic allows the salpd ligand to reduce Mn(IV) in the intermediate [Mn(salpd)(OH)]+ to Mn(III), triggering a Jahn-Teller effect that increases the ionic character of the hydroxide ligand. This transformation makes the resulting complex a strong nucleophile, facilitating O-O bond formation through a reaction between [Mn(salpd)(OH)]+ and [Mn(salpd)(O)]+ with a moderate overall activation free energy of 18.6 kcal/mol. The mechanistic insights presented in this study may provide a useful foundation for developing novel systems that catalyze water oxidation under light-driven conditions, mimicking Photosystem II, and could potentially contribute to advancements in sustainable energy generation.

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.

Synthesis, X-ray characterization and catalytic homogenous alcohol oxidation activity of Co(II)–carboxamide complex with green oxidant (H2O2) under mild conditions

In this study, a new air and moisture stable mononuclear cobalt(II)–carboxamide complex, [Co(TCrbx)2(CH3OH)2](ClO4)2, was synthesized and characterized (TCrbx = N-(4-methylpyridin-2-yl)thiophene-2-carboxamide). Complex characterization mainly was done with single crystal X-ray analysis. Ligand characterization was done with several spectroscopic techniques (Elemental Analysis, FT-IR, 1H NMR, 13C NMR). Cobalt(II) complex possesses distorted octahedral geometry coordinated with two carboxamide ligands at equatorial and two methanol ligands at axial positions and two perchlorate anions as counter ions. Synthesized complex was successfully tested as homogenous catalyst for the oxidation of benzyl alcohol with environmental friendly oxidant hydrogen peroxide (H2O2) under mild conditions. Benzaldehyde was selectively obtained with the conversion value of 99.5% in dimethyl formamide after 3-h reaction time at 50 °C with 133 TON value. Solvent and temperature effects were also investigated.

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand-ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol-1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol-1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified.

Computational Studies of Carboxylate-Assisted C-H Activation and Functionalization at Group 8-10 Transition Metal Centers

Computational studies on carboxylate-assisted C-H activation and functionalization at group 8-10 transition metal centers are reviewed. This Review is organized by metal and will cover work published from late 2009 until mid-2016. A brief overview of computational work prior to 2010 is also provided, and this outlines the understanding of carboxylate-assisted C-H activation in terms of the "ambiphilic metal-ligand assistance" (AMLA) and "concerted metalation deprotonation" (CMD) concepts. Computational studies are then surveyed in terms of the nature of the C-H bond being activated (C(sp²)-H or C(sp³)-H), the nature of the process involved (intramolecular with a directing group or intermolecular), and the context (stoichiometric C-H activation or within a variety of catalytic processes). This Review aims to emphasize the connection between computation and experiment and to highlight the contribution of computational chemistry to our understanding of catalytic C-H functionalization based on carboxylate-assisted C-H activation. Some opportunities where the interplay between computation and experiment may contribute further to the areas of catalytic C-H functionalization and applied computational chemistry are identified.

Rational design of model Pd(ii)-catalysts for C–H activation involving ligands with charge-shift bonding characteristics

A palladium(ii) complex with a bis-2-borabicyclo[1.1.0]but-1(3)-ene ligand having charge-shift bonding characteristics contributes to better performance for C–H bond activation.