Modeling Intermolecular Interactions with Exchange-Hole Dipole Moment Dispersion Corrections to Neural Network Potentials.

Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with an accuracy approaching that of a high-level ab initio calculation, but with a much smaller computational cost. Due to their training to density-functional theory (DFT) data and neglect of long-range interactions, some classes of NNPs require an additional term to include London dispersion physics.

Controlling Reactivity and Selectivity in the Mizoroki-Heck Reaction: High Throughput Evaluation of 1,5-Diaza-3,7-diphosphacyclooctane Ligands.

We report a high throughput evaluation of the Mizoroki-Heck reaction of diverse olefin coupling partners. Comparison of different ligands revealed the 1,5-diaza-3,7-diphosphacyclooctane (PN) scaffold to be more broadly applicable than common "gold standard" ligands, demonstrating that this family of readily accessible diphosphines has unrecognized potential in organic synthesis. In particular, two structurally related PN ligands were identified to enable the regiodivergent arylation of styrenes.