Electrochemical Deconstructive Methoxylation of Arylalcohols-A Synthetic and Mechanistic Investigation.

Herein, we report a mechanistic investigation of a recently developed electrochemical method for the deconstructive methoxylation of arylalcohols. A combination of synthetic, electroanalytical, and computational experiments have been performed to gain a deeper understanding of the reaction mechanism and the structural requirements for fragmentation to occur. It was found that 2-arylalcohols undergo anodic oxidation to form the corresponding aromatic radical cations, which fragment to form oxocarbenium ions and benzylic radical intermediates via mesolytic cleavage, with further anodic oxidation and trapping of the benzylic carbocation with methanol to generate the observed methyl ether products.

Reformulating Reactivity Design for Data-Efficient Machine Learning.

Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in rational reactivity design. However, model training requires large data sets of typically thousands or tens of thousands of barriers that are very expensive to obtain computationally or experimentally. Furthermore, bespoke data sets are required for each region of interest in reaction space as models typically struggle to generalize.

Comparing the Performances of Force Fields in Conformational Searching of Hydrogen-Bond-Donating Catalysts.

Here, we compare the relative performances of different force fields for conformational searching of hydrogen-bond-donating catalyst-like molecules. We assess the force fields by their predictions of conformer energies, geometries, low-energy, nonredundant conformers, and the maximum numbers of possible conformers. Overall, MM3, MMFFs, and OPLS3e had consistently strong performances and are recommended for conformationally searching molecules structurally similar to those in this study.