A method for the olefination of aryl aldehydes with unactivated alkenes via cross carbonyl-olefin metathesis (XCOM) is described. Reaction of an aldehyde substrate and a cis-1,2-disubstituted or monosubstituted olefin with the HBF4 salt of 2,3-diazabicyclo[2.2.2]octane results in high-yielding olefination with exclusive trans stereoselectivity. The reaction is shown to accommodate a range of substitution patterns on the aryl aldehyde and a diverse set of functional groups, including protic functionality that would complicate traditional olefination methods. We show that product inhibition arising from the aliphatic aldehyde side product limits catalytic turnover, but that distillative removal of this component renders catalysis feasible.

This work reports a computational-aided design driven approach towards chiral chalcogen-bond donor structures able to accomplish the still unprecedented, highly challenging asymmetric chalcogen-bonding catalysis. Optimisation of the chiral central backbone in rather simple bistriazole-bistelluride-based structures to effect a bidentate binding to Lewis basic substrates was performed, leading to the identification of (R,R)-1,3-diamino cyclohexane moiety as appropriate chiral spacer. While no binding was observed for other chiral central backbones, a moderate affinity to chloride anion as model substrate was recorded for the optimised neutral, bidentate system (Ka ~50-60 M-1 in THF-d8), for which key binding features were further revealed upon computational analysis on the corresponding catalyst-chloride complex. Hence, a chiral induction using chalcogen-bond as main activation was achieved for the first time, reaching enantioselectivities of up to 11:89 e.r. (78% ee) in the chosen test anion-binding benchmark Reissert-type reaction of (iso)quinolines.

Nucleophilic catalysis is a powerful and well-developed tool for the activation and manipulation of carbon-centered electrophiles, yet, routes for its utilization have been limited to a handful of molecular moieties. Herein, we report that dimethylaminopyridine efficiently promotes the nucleophilic substitution reactions of β-chlorinated Michael acceptors with various O-, N-, S- and C-nucleophiles in terms of electronic and steric properties. The reactions proceed via the formation of vinylpyridinium electrophilic species, which can be either generated in situ under catalytic conditions or, more beneficially, isolated and used further in a separate synthetic step. The executed reactions include preparation of relevant pharmaceutical precursors and cascade processes. Furthermore, quantum chemical calculation methods quantified a rate increase of the key substitution step of a catalytic reaction by approximately 10^6 times compared to a non-catalyzed reaction and established an impact of intermolecular noncovalent interactions on the reaction course.

Over the past twenty years, main group chemistry has broadened its horizons to encompass reactivities once thought to be exclusive domains of transition metals. Element-ligand cooperation has emerged as a promising approach for enabling bond activation reactions, yet it remains relatively unexplored for heavier group 15 elements. In this study, we present a novel class of ylide ligands featuring additional amino donor sites, specifically designed to promote bismuth-ligand cooperativity. We successfully isolated a series of well-defined bismuth scorpionate complexes with a dianionic N,Cylide,N ligand, bearing different substituents at the ylide moiety. These variations result in differing degrees of negative charge stabilization, which in turn leads to variable donation of electron density to the central bismuth atom. Halide abstraction to form cationic bismuth complexes enhanced ligand-to-bismuth electron donation, resulting in low Lewis acidities and reduced reactivity. Conversely, deprotonation of the ylide ligand bearing an electron-withdrawing ArF substituent enabled the synthesis and NMR characterization of a rare bismuth yldiide, which underwent a [2+2] addition of carbodiimide across the formal Bi=C double bond, demonstrating the ability of bismuth yldiides to activate bonds through bismuth-ligand cooperativity.

The combination of hypervalent iodine(III) oxidants and ammonia sources has been applied in various oxidative aminative transformations of high synthetic value. Central to these reactions is the proposed in situ generation of a four-electron oxidizing intermediate, commonly referred to as iodonitrene. However, this species’ mechanism of formation, nature, and relevance to N-atom transfer remains uncertain. Furthermore, evidence for its direct implica-tion as the key reactive intermediate remains elusive. Herein, we present an extensive mechanistic study of a re-cently published oxidative aminative cleavage of alkenes, which allowed us to obtain key insights into these under-studied aspects of hypervalent iodine-mediated nitrogen atom insertion. Through in situ 19F nuclear magnetic reso-nance (NMR), initial rate kinetics, linear free energy relationships (LFER), H/D and 12C/13C kinetic isotope effect (KIE) determination, electrospray ionization mass spectrometry (ESI-MS) and density functional theory (DFT) stud-ies, we show that the formation of an N-iodonium-iminoiodinane is rate-determining in this reaction. This species is highly electrophilic and capable of concerted, asynchronous transfer of a [PhI–N]+ unit to double bonds. These find-ings point towards the N-iodonium-iminoiodinane, not an iodonitrene, being the active N-atom transfer agent gen-erated from the combination of hypervalent iodine(III) oxidants and ammonia. This ultimately deepens our under-standing of this commonly used reagent combination and will help to inform the development of methods and rea-gents for oxidative amination reactions using this reactive manifold.

Oxygen-atom transfer (OAT) reactions, such as epoxidation, often use stoichiometric high-energy oxygen donors that present safety hazards, especially on large scale. Electrochemistry provides a means to replace these reagents with water as the O-atom donor. Here, we identify an electron-deficient Mn-porphyrin electrocatalyst that enables efficient OAT to alkenes and pyridines to access epoxides and N-oxides. The scope of the reaction includes terminal and electron-deficient alkenes that often prove challenging with other chemical and electrochemical methods. Direct comparisons with two chemical oxidation methods, (i) stoichiometric meta-chloroperoxybenzoic acid and (ii) the same Mn-porphyrin catalyst with PhI(OAc)2 as the oxidant, highlight the merits of the electrochemical reaction. Prospects for scalable application of the method is demonstrated through oxidation of pharmaceutically relevant alkenes in a stirred tank electrochemical reactor.

A thorough mechanistic investigation into the disulfonimide (DSI)-catalyzed atroposelective iodination of 2-amino-6-arylpyridines with N-iodosuccinimide is described. While initially hypothesized to proceed via Brønsted-acid catalysis through activation of NIS, experimental evidence led us to reconsider the possible function of the DSI catalyst. We now propose a mechanism in which the conjugate base of the DSI functions instead as a Brønsted-base, with the rate and stereodetermining step involving an enantioselective deprotonation of a Wheland intermediate. The experimental data was used to initiate a DFT exploration of the reaction mechanism, which after thorough analysis of the transition state structures and reaction coordinate confirmed our revised hypothesis. The unique structural features of the highest performing catalyst were explored further with a chemoinformatics derived workflow, leading to a highly detailed elucidation of those factors contributing to the origin of enantioselection.

Steric repulsion, often regarded as a macroscopic manifestation of the Pauli exclusion principle, which prevents electrons with the same spin from occupying the same space, is experiencing renewed interest across the field of chemistry. While its importance is widely recognized, visualizing where and how it occurs remains challenging. We introduce the Steric Exclusion Localization Function (SELF), a novel tool that reveals steric interactions in three dimensions, requiring only a single quantum calculation. With SELF, chemists can see exactly where steric clashes happen in their molecules, quantify their magnitude, and identify which atoms contribute mostly to them. SELF transforms abstract quantum mechanical concepts into intuitive 3D maps accessible through the user-friendly IGMPlot software. We demonstrate its power across diverse chemical systems: from understanding atropisomerism in pharmaceutical scaffolds to rationalizing selectivity in catalysis. Unlike traditional quantum mechanical methods that yield only numbers, SELF provides visual insights that organic chemists can immediately interpret and apply. This approach bridges the gap between theory and practical chemical understanding, making sophisticated quantum mechanical analysis accessible to the broader chemical community. We hope this tool will prove valuable for chemical design, research applications, and teaching steric effects.

The rapid advancement of machine learning in computational chemistry has opened new doors for designing molecules, predicting molecular properties, and discovering novel materials. However, building scalable and robust models for molecular property prediction remains a significant challenge due to the vast size and complexity of chemical space. In this paper, we introduce ChemBERTa-3, an open-source training framework designed to train and fine-tune large-scale chemical foundation models. We explore the potential of multiple model architectures by evaluating their performance across various molecular datasets from the MoleculeNet suite. Our experiments demonstrated that pre-training on the expansive ZINC20 dataset yields models capable of performing well on both classification and regression tasks, providing valuable insights into drug discovery and materials science. For scalability, we leveraged both AWS-based Ray deployments and on-premise high-performance computing clusters to support the processing power required to train on billions of molecules. In support of reproducible and extensible science, we have open-sourced all ChemBERTa3 models.

Nuclear Magnetic Resonance (NMR) is one of the most powerful structural characterization techniques in molecular sciences. However, the complexity of NMR spectra can make structural assignments prone to er-rors. Here we introduce a deep learning model – CASCADE-2.0 (ChemicAl Shift CAlculation with DEep learn-ing), a practical tool designed to assist chemists in making fast, reliable, and transparent 13C-NMR chemical shift predictions. Building on our previous model, we make improvements to the model architecture and train-ing data, while striving to enhance the model transparency. Leveraging advances in neural network poten-tials, a fourfold expansion of training data in terms of molecular and elemental coverage is made, resulting in a dataset containing around 170,000 experimental shifts cross-validated by DFT. To address DFT limitations, we developed an intelligent data augmentation strategy combining statistical analysis and machine learning predictions to further expand the dataset to 211,000 experimental values. With the expanded dataset and changes in model architecture, a state-of-the-art accuracy of 0.73 ppm was achieved when compared against experimental 13C-NMR shifts. The model also incorporates prediction confidence metrics using a deep-kernel learning architecture, as well as nearest-neighbor analysis, facilitated by a user-friendly web-server. Finally, we demonstrate the versatility of the final model using several real-world applications.

Laboratory automation is an active field in biology, drug discovery, and more recently in synthetic chemistry and materials science. Local automation has existed in the field for quite some time, but long-range or total laboratory automation is much less developed. In this article, we present a complete, open and decentralized, global automation system called the 2D drone swarm system. It is based on a simple approach of small mobile robots moving autonomously in a dedicated track suspended above the scientific equipment for the long-distance sample and closely connected to localized robotic arms dedicated to short-distance transfers, interaction with scientific equipment and direct sample processing. This approach is inspired by the Kiva/Amazon model, where isolated autonomous mobile robots automatically deliver goods to external operators. It is also inspired by the modern automotive industry, such as Tesla's Gigafactories, to provide an evolutionary and flexible system that can adapt to numerous types of tasks with a minimum of resources and easily adapt to different types of workstations. This global automation system is controlled directly from the Laboratory Scheduler by a Robot Subscheduler, coded in an open-source environment, which takes care of all mobile and local robot operations. The result is an operator and scientific equipment safe, cost and energy-efficient, easily extensible and open-source global laboratory automation system that can be adapted to many different applications and laboratories.

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Self-driving laboratories (SDLs) have the potential to revolutionize chemical discovery and optimization, yet their widespread adoption remains limited by high costs, complex infrastructure, and limited accessibility. Here, we introduce RoboChem-Flex, a low-cost, modular self-driving laboratory platform designed to democratize autonomous chemical experimentation. The system combines customizable, in-house-built hardware with a flexible Python-based software framework that integrates real-time device control and advanced Bayesian optimization strategies, including multi-objective and transfer learning workflows. RoboChem-Flex supports both fully autonomous closed-loop operation and human-in-the-loop configurations, enabling seamless integration with shared analytical equipment and minimizing entry barriers. We validate the versatility of the platform across six diverse case studies, including photocatalysis, biocatalysis, thermal cross-couplings, and enantioselective catalysis, spanning both single and multi-objective optimizations. Through these campaigns, we demonstrate RoboChem-Flex’s ability to navigate large, complex chemical spaces, autonomously identify scalable high-performance reaction conditions, and flexibly adapt to a variety of analytical setups. By providing an affordable, scalable, and open platform, RoboChem-Flex offers a tangible step toward making SDLs accessible to resource-limited laboratories, fostering broader participation in automated chemical research.