Theoretical study on hydroformylation catalyzed by cationic cobalt(II) complexes.

Hydroformylation is one of the most important homogeneous reactions in industrial production. Herein, a density functional theory (DFT) method was employed to investigate two proposed reaction mechanisms of hydroformylation catalyzed by cationic cobalt(II) complexes, the carbonyl dissociative mechanism and the associative mechanism. The calculated results showed that the heterolytic H2 activation is the rate-determining step for both the dissociative mechanism and the associative mechanism, with energy barriers of 26.8 kcal mol-1 and 40.5 kcal mol-1, respectively. Meanwhile, the regioselectivity, the spin multiplicity of the catalyst and the substituent effects on the reaction were also investigated. The most stable cobalt(II) catalyst has a doublet state and the linear aldehyde is the dominant product. In addition, it was found that the energy barrier of the reaction decreased when the electron density of the Co center of the catalyst was increased by changing the ligand. The catalytic activity of the catalyst was proposed to be the best when the PEt2 group of the ligand is replaced by the P(tBu)2 group. This study might not only provide new insights for hydroformylation catalyzed by cobalt but also facilitate theory-guided design of novel transition metal catalysts for hydroformylation.

The theoretical design of manganese catalysts with a Si-N-Si-C-Si-C six-membered ring core-based bowl-shaped quadridentate ligand for the hydrogenation of CO/CN bonds.

Herein, a new series of bowl-shaped quadridentate ligands with a Si-N-Si-C-Si-C six-membered ring core and their manganese catalysts were designed using the density functional theory (DFT) method for the hydrogenation of unsaturated CX (XN, O) bonds. The frameworks of these ligands named by LYG (LYG = P(R1)2CH2Si(CH2)(CH3)NSi(CH3)(CH2Si(CH3)CH2P(R3)2)CH2P(R2)2) have a Si-N-Si-C-Si-C six-membered ring core at the bottom of the bowl structure and each Si atom links with one phosphorus arm (-CH2PR2). The Mn catalyst Mn(CO)-LYG was constructed to catalyze the hydrogenation of CO/CN bonds. The calculated results indicate that due to the bowl-shaped structure of LYG quadridentate ligands, these Mn catalysts could be advantageous not only in the tuneup of catalytic activity and stereoselectivity by modifying three phosphorus arms but also in the homogeneous catalyst immobilization by linking with the Si-N-Si-C-Si-C six-membered ring core using different supports. This work might provide theoretical insights to design new framework transition-metal catalysts for the hydrogenation of CX bonds.

The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning.

In this work, high-performance two-dimensional (2D) graphene-based single-atom electrocatalysts (ZZ/ZA-MNxCy) for the oxygen reduction reaction (ORR) were screened out using machine learning (ML). A model was built for the fast prediction of electrocatalysts and two descriptors valence electron correction (VEc) and degree of construction differences (DC) were proposed to improve the accuracy of the model prediction. Two evaluation criteria, high-performance catalyst retention rate rR and high-performance catalyst occupancy rate rO, were proposed to evaluate the accuracy of ML models in high-performance catalyst screening. The addition of VEc and DC in the model could change the mean absolute error (MAEtest) of the test set, the coefficient of determination (R2test) of the test set, rO, and rR from 0.334 V, 0.683, 0.222, and 0.360 to 0.271 V, 0.774, 0.421, and 0.671, respectively. The partially screened potential high-performance ORR electrocatalysts such as ZZ-CoN4 and ZZ-CoN3C1 were also further investigated using a Density Functional Theory (DFT) method, which confirmed the accuracy of the ML model (MAE = 0.157 V, R2 = 0.821).

Improved Elastic Image Pair Method for Finding Transition States.

The elastic image pair (EIP) method is a robust method for finding approximate transition states between two local minima. However, the original implementation of the method had some limitations. In this work, we present an improved EIP method, in which the moving procedure of the image pair and the convergence strategy are modified. In addition, this method is combined with the rational function optimization method to give exact transition states. Tests on a set of 45 different reactions show the reliability and efficiency in finding transition states.

Elastic Image Pair Method for Finding Transition States on Potential Energy Surfaces Using Only First Derivatives.

Herein, an elastic image pair (EIP) method is proposed to search transition states between two potential-energy minima using only first derivatives. In this method, two images are generated, and the spring forces are added to the images to control the distance between the two images. Transition states are reached when the force and the distance of the image pair are both converged. A set of test molecules is optimized using the EIP method, which shows its efficiency in transition state searching compared to other methods. This new method is more stable and reliable in finding transition states with much less computations.