GoodVibes is an open-source Python toolkit for processing the results of quantum chemical calculations. Thermochemical data are not simply parsed, but evaluated by evaluation of translational, rotational, vibrational and electronic partition functions. Changes in concentration, pressure, and temperature can be applied, and deficiencies in the rigid rotor harmonic oscillator treatment can be corrected. Vibrational scaling factors can also be applied by automatic detection of the level of theory and basis set. Absolute and relative thermochemical values are output to text and graphical plots in seconds. GoodVibes provides a transparent and reproducible way to process raw computational data into publication-quality tables and figures without the use of spreadsheets.

Learning to stabilize palladium dimers: Catalyst optimization is often difficult to do rationally. Once something works, it may be unclear which specific features underpin the performance. A case in point is the stabilization of palladium(I) dimers, which has relied on a very small class of phosphine ligands. Hueffel et al . used machine learning to search for patterns in this known class of ligands and thereby guide the discovery of variants that likewise stabilize the dimers. The authors were able to synthesize eight previously unreported dimers. —JSY

Although nanoparticles are widely used as catalysts, little is known about their potential ability to trigger privileged transformations as compared to homogeneous molecular or bulk heterogeneous catalysts. We herein demonstrate (and rationalize) that nanoparticles display orthogonal reactivity to molecular catalysts in the cross‐coupling of aryl halides with aryl germanes. While the aryl germanes are unreactive in L_nPd⁰/L_nPd^II catalysis and allow selective functionalization of established coupling partners in their presence, they display superior reactivity under Pd nanoparticle conditions, outcompeting established coupling partners (such as ArBPin and ArBMIDA) and allowing air‐tolerant, base‐free, and orthogonal access to valuable and challenging biaryl motifs. As opposed to the notoriously unstable polyfluoroaryl‐ and 2‐pyridylboronic acids, the corresponding germanes are highly stable and readily coupled. Our mechanistic and computational studies provide unambiguous support of nanoparticle catalysis and suggest that owing to the electron richness of aryl germanes, they preferentially react by electrophilic aromatic substitution, and in turn are preferentially activated by the more electrophilic nanoparticles.

Oxidative coupling reactions, where two electrons are released from the reactants and trapped by an oxidant, have arisen as a versatile alternative to cross-coupling in chemical synthesis. Despite the large number of experimental reports on the process, a clear mechanistic picture is only starting to emerge. In this perspective, we highlight the contribution from DFT calculations to the computational characterization of this mechanism. Oxidative coupling processes have been reported differing in both the catalyst (radicals, precious metals or earth-abundant metals) and the oxidant. We have found more useful to classify them according to the oxidant used, as metal-based oxidants and metal-free oxidants seem to favor different mechanistic variations. All steps in the full catalytic cycle are analyzed, and issues concerning selectivity and influence of the oxidant are considered.