We report a Na:−→B dative bond in the NaBH3− cluster, which was designed on the principle of minimum‐energy rupture, prepared by laser vaporization, and characterized by a synergy of anion photoelectron spectroscopy and electronic structure calculations. The global minimum of NaBH3− features a Na−B bond. Its preferred heterolytic dissociation conforms with the IUPAC definition of dative bond. The lone electron pair revealed on Na and the negative Laplacian of electron density at the bond critical point further confirm the dative nature of the Na−B bond. This study represents the first example of a Lewis adduct with an alkalide as the Lewis base.

Aromaticity serves as a kinetic trap for mechanically interlocked cyclo[18]carbon rings.

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.