Herein we interrogate a type of heterolytic fragmentation reaction called a 'divergent fragmentation' using density functional theory (DFT), natural bond orbital (NBO) analysis, ab initio molecular dynamics (AIMD), and external electric field (EEF) calculations. We demonstrate that substituents, electrostatic environment and non-statistical dynamic effects all influence product selectivity in reactions that involve divergent fragmentation pathways. Direct dynamics simulations reveal an unexpected post-transition state bifurcation (PTSB), and EEF calculations suggest that some transition states for divergent pathways can, in principle, be selectively stabilized if an electric field of the correct magnitude is oriented appropriately. This journal is © The Royal Society of Chemistry 2020.The catalytic hydrogenation of amides is an atom economical method to synthesize amines. Previously, it was serendipitously discovered that the combination of a secondary amide co-catalyst with (iPrPNP)Fe(H)(CO) (iPrPNP = N[CH2CH2(PiPr2)]2 -), results in a highly active base metal system for deaminative amide hydrogenation. Here, we use DFT to develop an improved co-catalyst for amide hydrogenation. Initially, we computationally evaluated the ability of a series of co-catalysts to accelerate the turnover-limiting proton transfer during C-N bond cleavage and poison the (iPrPNP)Fe(H)(CO) catalyst through a side reaction. TBD (triazabicyclodecene) was identified as the leading co-catalyst. It was experimentally confirmed that when TBD is combined with (iPrPNP)Fe(H)(CO) a remarkably active system for amide hydrogenation is generated. TBD also enhances the activity of other catalysts for amide hydrogenation and our results provide guidelines for the rational design of future co-catalysts. This journal is © The Royal Society of Chemistry 2020.Molecular crystal structure prediction is increasingly being applied to study the solid form landscapes of larger, more flexible pharmaceutical molecules. Despite many successes in crystal structure prediction, van der Waals-inclusive density functional theory (DFT) methods exhibit serious failures predicting the polymorph stabilities for a number of systems exhibiting conformational polymorphism, where changes in intramolecular conformation lead to different intermolecular crystal packings. Here, the stabilities of the conformational polymorphs of o-acetamidobenzamide, ROY, and oxalyl dihydrazide are examined in detail. DFT functionals that have previously been very successful in crystal structure prediction perform poorly in all three systems, due primarily to the poor intramolecular conformational energies, but also due to the intermolecular description in oxalyl dihydrazide. In all three cases, a fragment-based dispersion-corrected second-order Møller-Plesset perturbation theory (MP2D) treatment of the crystals overcomes these difficulties and predicts conformational polymorph stabilities in good agreement with experiment. These results highlight the need for methods which go beyond current-generation DFT functionals to make crystal polymorph stability predictions truly reliable. This journal is © The Royal Society of Chemistry 2020.Numerous field and laboratory studies have shown that amines, especially dimethylamine (DMA), are crucial to atmospheric particulate nucleation. https://www.selleckchem.com/products/rbn-2397.html However, the molecular mechanism by which amines lead to atmospheric particulate formation is still not fully understood. Herein, we show that DMA molecules can also promote the conversion of atmospheric SO2 to sulfate. Based on ab initio simulations, we find that in the presence of DMA, the originally endothermic and kinetically unfavourable hydrolysis reaction between gaseous SO2 and water vapour can become both exothermic and kinetically favourable. The resulting product, bisulfite NH2(CH3)2 +·HSO3 -, can be readily oxidized by ozone under ambient conditions. Kinetic analysis suggests that the hydrolysis rate of SO2 and DMA with water vapour becomes highly competitive with and comparable to the rate of the reaction between SO2 and OH·, especially under the conditions of heavily polluted air and high humidity. We also find that the oxidants NO2 and N2O5 (whose role in sulfate formation is still under debate) appear to play a much less significant role than ozone in the aqueous oxidation reaction of SO2. The newly identified oxidation mechanism of SO2 promoted by both DMA and O3 provides another important new source of sulfate formation in the atmosphere. This journal is © The Royal Society of Chemistry 2020.Interfaces that can change their chemistry on demand have huge potential for applications and are prerequisites for responsive or adaptive materials. We report on the performance of a newly designed n-butyl-arylazopyrazole butyl sulfonate (butyl-AAP-C4S) surfactant that can change its structure at the air-water interface by E/Z photo-isomerization in an unprecedented way. Large and reversible changes in surface tension (Δγ = 27 mN m-1) and surface excess (ΔΓ > 2.9 μmol m-2) demonstrate superior performance of the butyl-AAP-C4S amphiphile to that of existing ionic surfactants. Neutron reflectometry and vibrational sum-frequency generation spectroscopy reveal that these large changes are caused by an unexpected monolayer-to-bilayer transition. This exceptional behavior is further shown to have dramatic consequences at larger length scales as highlighted by applications like the light-triggered collapse of aqueous foam which is tuned from high (>1 h) to low ( less then 10 min) stabilities and light-actuated particle motion via Marangoni flows. This journal is © The Royal Society of Chemistry 2020.Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically that this pathology arises when the Bayesian optimization scheme queries latent space points far away from the data on which the variational autoencoder has been trained. Secondly, by reformulating the search procedure as a constrained Bayesian optimization problem, we show that the effects of this pathology can be mitigated, yielding marked improvements in the validity of the generated molecules. We posit that constrained Bayesian optimization is a good approach for solving this kind of training set mismatch in many generative tasks involving Bayesian optimization over the latent space of a variational autoencoder. This journal is © The Royal Society of Chemistry 2020.