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Abstract A Pd‐catalyzed heterocyclization/carbonylation/arylation cascade reaction between β,γ‐unsaturated N−Ts hydrazones and commercially available arylboronic acids as coupling partners is described, producing 2‐pyrazoline‐ketone derivatives in 11–78% yield. A detailed statistical analysis of reactivity patterns of boronic acids provided key information about the limitations of the method, highlighting the challenges of degradation pathways. Our methodology offers a tool for synthesizing diverse 2‐pyrazoline‐ketone derivatives, expanding the toolbox of accessible N−N‐heterocycles. 

Abstract Nitrogen atom‐rich heterocycles and organic azides have found extensive use in many sectors of modern chemistry from drug discovery to energetic materials. The prediction and understanding of their energetic properties are thus key to the safe and effective application of these compounds. In this work, we disclose the use of multivariate linear regression modeling for the prediction of the decomposition temperature and impact sensitivity of structurally diverse tetrazoles and organic azides. We report a data‐driven approach for property prediction featuring a collection of quantum mechanical parameters and computational workflows. The statistical models reported herein carry predictive accuracy as well as chemical interpretability. Model validation was successfully accomplished via tetrazole test sets with parameters generated exclusively in silico. Mechanistic analysis of the statistical models indicated distinct divergent pathways of thermal and impact‐initiated decomposition. 

Chiral polyoxygenated cyclohexanes are valuable constituents of biologically relevant products. Herein, we report a protocol for the direct access to these scaffolds via site‐ and enantioselective non‐directed oxidation of cyclohexyl‐3,5‐mesodiethers using aqueous H₂O₂. Structural shaping of a highly reactive chiral Mn‐oxo species, achieved through the combination of a sterically encumbered ligand and a bulky carboxylic acid, promotes a precise fit of the substrate within the catalyst pocket, which translates into exceptional enantioselectivity (up to >99% ee). Computational studies reveal that C─H oxidation proceeds via an initial hydrogen atom transfer, followed by electron transfer, leading to the formation of a chiral cationic intermediate. The resulting desymmetrized 3‐methoxycyclohexanone products serve as valuable intermediates for the synthesis of bioactive cores, as they can undergo orthogonal chemical modifications to enable further structural diversification. 

Steric molecular descriptors designed for machine learning (ML) applications are critical for connecting structure-function relationships to mechanistic insight. However, many of these descriptors are not suitable for application to com-plex systems, such as catalyst reactive site pockets. In this context, we recently disclosed a new set of 3D steric molecular descriptors that were originally designed for dirhodium(II) tetra-carboxylate catalysts. Herein, we expand the Spatial Molding for Rigid Targets (SMART) descriptor toolkit by releasing SMARTpy; an automated, open-source Python API package for computational workflow integration of SMART descriptors. The impact of the structure of the molecular probe for generation of SMART descriptors was analyzed. Resultant SMART descriptors and pocket features were found to be highly dependent upon probe selection, and do not scale linearly. Flexible probes with smaller substituents can explore narrow pocket regions resulting in a higher resolution pocket imprint. Macrocyclic probes with larger substituents are more applicable to larger cavities with smooth boundaries, such as dirhodium paddlewheel complexes. In these cases, SMARTpy provides comparable descriptors to the original calculation method using UCSF Chimera. Finally, we analyzed a series of case studies demonstrating how SMART descriptors can impact other areas of catalysis, such as organocatalysis, biocatalysis, and protein pocket analysis. 

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)–C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2’-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parame-terization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational lig-and library, correlation of observed reaction outcomes with features of the ligands, and in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a fivefold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt. 

Nonaqueous redox flow batteries (NARFBs) offer a promising solution for large-scale storage of renewable energy. However, crossover of redox active molecules between the two sides of the cell is a major factor limiting their development, as most selective separators are designed for deployment in water, rather than organic solvents. This report describes a systematic investigation of the crossover rates of redox active organic molecules through an anion exchange separator under RFB-relevant non-aqueous conditions (in acetonitrile/KPF6) using a combination of experimental and computational methods. A structurally diverse set of neutral and cationic molecules was selected, and their rates of crossover were determined experimentally with the organic solvent-compatible anion exchange separator Fumasep FAP-375-PP. The resulting data were then fit to various descriptors of molecular size, charge, and hydrophobicity (overall charge, solution diffusion coefficient, globularity, dynamic volume, dynamic surface area, clogP). This analysis resulted in multiple statistical models of crossover rates for this separator. These models were then used to predict tether groups that dramatically slow the crossover of small organic molecules in this system.