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 H activation is the rate-determining step for both the dissociative mechanism and the associative mechanism, with energy barriers of 26.

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(R)CHSi(CH)(CH)NSi(CH)(CHSi(CH)CHP(R))CHP(R)) 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 (-CHPR). The Mn catalyst Mn(CO)-LYG was constructed to catalyze the hydrogenation of CO/CN 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-MNC) 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 and high-performance catalyst occupancy rate , were proposed to evaluate the accuracy of ML models in high-performance catalyst screening.

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.

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.