Theoretical Insights into [NHC]Au(I) Catalyzed Hydroalkoxylation of Allenes: A Unified Reaction Valley Approach Study

Mechanistic Details of the Sharpless Epoxidation of Allylic Alcohols—A Combined URVA and Local Mode Study

In this work, we investigated the catalytic effects of a Sharpless dimeric titanium (IV)–tartrate–diester catalyst on the epoxidation of allylalcohol with methyl–hydroperoxide considering four different orientations of the reacting species coordinated at the titanium atom (reactions R1–R4) as well as a model for the non-catalyzed reaction (reaction R0). As major analysis tools, we applied the URVA (Unified Reaction Valley Approach) and LMA (Local Mode Analysis), both being based on vibrational spectroscopy and complemented by a QTAIM analysis of the electron density calculated at the DFT level of theory. The energetics of each reaction were recalculated at the DLPNO-CCSD(T) level of theory. The URVA curvature profiles identified the important chemical events of all five reactions as peroxide OO bond cleavage taking place before the TS (i.e., accounting for the energy barrier) and epoxide CO bond formation together with rehybridization of the carbon atoms of the targeted CC double bond after the TS. The energy decomposition into reaction phase contribution phases showed that the major effect of the catalyst is the weakening of the OO bond to be broken and replacement of OH bond breakage in the non-catalyzed reaction by an energetically more favorable TiO bond breakage. LMA performed at all stationary points rounded up the investigation (i) quantifying OO bond weakening of the oxidizing peroxide upon coordination at the metal atom, (ii) showing that a more synchronous formation of the new CO epoxide bonds correlates with smaller bond strength differences between these bonds, and (iii) elucidating the different roles of the three TiO bonds formed between catalyst and reactants and their interplay as orchestrated by the Sharpless catalyst. We hope that this article will inspire the computational community to use URVA complemented with LMA in the future as an efficient mechanistic tool for the optimization and fine-tuning of current Sharpless catalysts and for the design new of catalysts for epoxidation reactions.

Bonding in Nitrile Photo-dissociating Ruthenium Drug Candidates --A Local Vibrational Mode Study

In this work, we investigated bonding features 15 ruthenium complexes of the type [Ru(tpy)(L)-(CH3CN)]n+, containing the tridentate tpy ligand (tpy = 2,2':6',2'--terpyridine) and various bidentate ancillary ligands, 12 compounds originally synthesized by Loftus et al. (J. Phys. Chem. C 123, 10291-10299 (2019)) complemented with three additional complexes. The main focus of our work was to relate these local features to the experimental data of Loftus et al. which assess the efficiency of nitrile release in an indirect way via observed quantum yields for ruthenium water association after nitrile release. As a tool to quantitatively assess Ru-NC and Ru-L bonding we utilized the local vibrational mode analysis complemented by the topological analysis of the electron density and the natural bond orbital analysis. Interestingly, the stronger Ru-NC bonds have the greater observed quantum yields, leading to the conclusion that the observed quantum yields are a result of a complex interplay of several processes excluding a direct relationship between QY and Ru-NC or Ru-L bond strengths. We identified the ST splitting as one of the key players and not the Ru-NC bond strength, as one may have thought. In summary, this work has presented a modern computational tool set for the investigation of bonding features applied to nitrile photo-dissociating ruthenium drug candidates forming a valuable basis for future design and fine tuning of nitrile releasing ruthenium compounds, as well as for the understanding of how local properties affect overall experimental outcomes.

BF3–Catalyzed Diels–Alder Reaction between Butadiene and Methyl Acrylate in Aqueous Solution—An URVA and Local Vibrational Mode Study

In this study we investigate the Diels–Alder reaction between methyl acrylate and butadiene, which is catalyzed by BF3 Lewis acid in explicit water solution, using URVA and Local Mode Analysis as major tools complemented with NBO, electron density and ring puckering analyses. We considered four different starting orientations of methyl acrylate and butadiene, which led to 16 DA reactions in total. In order to isolate the catalytic effects of the BF3 catalyst and those of the water environment and exploring how these effects are synchronized, we systematically compared the non-catalyzed reaction in gas phase and aqueous solution with the catalyzed reaction in gas phase and aqueous solution. Gas phase studies were performed at the B3LYP/6-311+G(2d,p) level of theory and studies in aqueous solution were performed utilizing a QM/MM approach at the B3LYP/6-311+G(2d,p)/AMBER level of theory. The URVA results revealed reaction path curvature profiles with an overall similar pattern for all 16 reactions showing the same sequence of CC single bond formation for all of them. In contrast to the parent DA reaction with symmetric substrates causing a synchronous bond formation process, here, first the new CC single bond on the CH2 side of methyl acrylate is formed followed by the CC bond at the ester side. As for the parent DA reaction, both bond formation events occur after the TS, i.e., they do not contribute to the energy barrier. What determines the barrier is the preparation process for CC bond formation, including the approach diene and dienophile, CC bond length changes and, in particular, rehybridization of the carbon atoms involved in the formation of the cyclohexene ring. This process is modified by both the BF3 catalyst and the water environment, where both work in a hand-in-hand fashion leading to the lowest energy barrier of 9.06 kcal/mol found for the catalyzed reaction R1 in aqueous solution compared to the highest energy barrier of 20.68 kcal/mol found for the non-catalyzed reaction R1 in the gas phase. The major effect of the BF3 catalyst is the increased mutual polarization and the increased charge transfer between methyl acrylate and butadiene, facilitating the approach of diene and dienophile and the pyramidalization of the CC atoms involved in the ring formation, which leads to a lowering of the activation energy. The catalytic effect of water solution is threefold. The polar environment leads also to increased polarization and charge transfer between the reacting species, similar as in the case of the BF3 catalyst, although to a smaller extend. More important is the formation of hydrogen bonds with the reaction complex, which are stronger for the TS than for the reactant, thus stabilizing the TS which leads to a further reduction of the activation energy. As shown by the ring puckering analysis, the third effect of water is space confinement of the reacting partners, conserving the boat form of the six-member ring from the entrance to the exit reaction channel. In summary, URVA combined with LMA has led to a clearer picture on how both BF3 catalyst and aqueous environment in a synchronized effort lower the reaction barrier. These new insights will serve to further fine-tune the DA reaction of methyl acrylate and butadiene and DA reactions in general.

On the formation of CN bonds in Titan's atmosphere-a unified reaction valley approach study

In this work, we investigated the formation of protonated hydrogen cyanide HCNH⁺ and methylene amine cation CH[Formula: see text] (both identified in Titan's upper atmosphere) from three different pathways which stem from the interaction between CH₄ and N⁺(³P). As a mechanistic tool, we used the Unified Reaction Valley Approach (URVA) complemented with the Local Mode Analysis (LMA) assessing the strength of the CN bonds formed in these reactions. Our URVA studies could provide a comprehensive overview on bond formation/cleavage processes relevant to the specific mechanism of eight reactions R1- R8 that occur across the three pathways. In addition, we could explain the formation of CH[Formula: see text] and the appearance of HCNH⁺ and CHNH[Formula: see text] along these paths. Although only smaller molecules are involved in these reactions including isomerization, hydrogen atom abstraction, and hydrogen molecule capture, we found a number of interesting features, such as roaming in reaction R3 or the primary interaction of H₂ with the carbon atom in HCNH⁺ in reaction R8 followed by migration of one of the H₂ hydrogen atoms to the nitrogen which is more cost effective than breaking the HH bond first; a feature often found in catalysis. In all cases, charge transfer between carbon and nitrogen could be identified as a driving force for the CN bond formation. As revealed by LMA, the CN bonds formed in reactions R1-R8 cover a broad bond strength range from very weak to very strong, with the CN bond in protonated hydrogen cyanide HCNH⁺ identified as the strongest of all molecules investigated in this work. Our study demonstrates the large potential of both URVA and LMA to shed new light into these extraterrestrial reactions to help better understand prebiotic processes as well as develop guidelines for future investigations involving areas of complex interstellar chemistry. In particular, the formation of CN bonds as a precursor to the extraterrestrial formation of amino acids will be the focus of future investigations. Formation of CN bonds in Titan's atmosphere visualized via the reaction path curvature.

Generative adversarial networks for transition state geometry prediction

This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN.