Systematic, computational discovery of multicomponent and one-pot reactions.

Discovery of new types of reactions is essential to organic chemistry because it expands the scope of accessible molecular scaffolds and can enable more economical syntheses of existing structures. In this context, the so-called multicomponent reactions, MCRs, are of particular interest because they can build complex scaffolds from multiple starting materials in just one step, without purification of intermediates. However, for over a century of active research, MCRs have been discovered rather than designed, and their number remains limited to only several hundred.

Estimation of multicomponent reactions' yields from networks of mechanistic steps.

This work describes estimation of yields of complex, multicomponent reactions (MCRs) based on the modeled networks of mechanistic steps spanning both the main reaction pathway as well as immediate and downstream side reactions. Because experimental values of the kinetic rate constants for individual mechanistic transforms are extremely sparse, these constants are approximated here using Mayr's nucleophilicity and electrophilicity parameters fine-tuned by correction terms grounded in linear free-energy relationships. With this formalism, the model trained on the mechanistic networks of only 20 - but mechanistically- and yield-diverse MCRs - transfers well to newly discovered MCRs that are based on markedly different mechanisms and types of individual mechanistic transforms.

Stirring-Controlled Synthesis of Ultrastable, Fluorescent Silver Nanoclusters.

This paper describes how macroscopic stirring of a reaction mixture can be used to produce nanostructures exhibiting properties not readily achievable via other protocols. In particular, it is shown that by simply adjusting the stirring rate, a standard glutathione-based method-to date, used to produce only marginally stable fluorescent silver nanoclusters, Ag NCs-can be boosted to yield nanoclusters retaining fluorescence for unprecedented periods of over 2 years. This enhancement derives not simply from increased homogenization of the reaction mixture but mainly from an appropriately timed delivery of oxygen from above the reaction mixture.

Light-Driven, Dynamic Assembly of Micron-To-Centimeter Parts, Micromachines and Microbot Swarms.

This work describes light-driven assembly of dynamic formations and functional particle swarms controlled by appropriately programmed light patterns. The system capitalizes on the use of a fluidic bed whose low thermal conductivity assures that light-generated heat remains "localized" and sets strong convective flows in the immediate vicinity of the particles being irradiated. In this way, even low-power laser light or light from a desktop slide projector can be used to organize dynamic formations of objects spanning four orders of magnitude in size (from microns to centimeters) and over nine orders of magnitude in terms of mass.

Aerodynamically Levitated Droplets as Small-Scale Chemical Reactors and Liquid Microprinters.

A thin liquid film spread over the inner surface of a rapidly rotating vial creates an aerodynamic cushion on which one or multiple droplets of various liquids can levitate stably for days or even weeks. These levitating droplets can serve as wall-less ("airware") chemical reactors that can be merged without touching-by remote impulses-to initiate reactions or sequences of reactions at scales down to hundreds of nanomoles. Moreover, under external electric fields, the droplets can act as the world's smallest chemical printers, shedding regular trains of pL or even fL microdrops.

Machine Learning Algorithm Guides Catalyst Choices for Magnesium-Catalyzed Asymmetric Reactions.

Organic-chemical literature encompasses large numbers of catalysts and reactions they can effect. Many of these examples are published merely to document the catalysts' scope but do not necessarily guarantee that a given catalyst is "optimal"-in terms of yield or enantiomeric excess-for a particular reaction. This paper describes a Machine Learning model that aims to improve such catalyst-reaction assignments based on the carefully curated literature data.

Delocalized, asynchronous, closed-loop discovery of organic laser emitters.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics.

Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge.

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea.

AlphaFold2 Can Predict Single-Mutation Effects.

AlphaFold2 (AF) is a promising tool, but is it accurate enough to predict single mutation effects? Here, we report that the localized structural deformation between protein pairs differing by only 1-3 mutations-as measured by the effective strain-is correlated across 3901 experimental and AF-predicted structures. Furthermore, analysis of ∼11 000 proteins shows that the local structural change correlates with various phenotypic changes. These findings suggest that AF can predict the range and magnitude of single-mutation effects on average, and we propose a method to improve precision of AF predictions and to indicate when predictions are unreliable.

Computational prediction of complex cationic rearrangement outcomes.

Recent years have seen revived interest in computer-assisted organic synthesis. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field, including examples leading to advanced natural products. Such methods typically operate on full, literature-derived 'substrate(s)-to-product' reaction rules and cannot be easily extended to the analysis of reaction mechanisms.

Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis.

Multi-metal oxides in general and perovskite oxides in particular have attracted considerable attention as oxygen evolution electrocatalysts. Although numerous theoretical studies have been undertaken, the most promising perovskite-based catalysts continue to emerge from human-driven experimental campaigns rather than data-driven machine learning protocols, which are often limited by the scarcity of experimental data on which to train the models. This work promises to break this impasse by demonstrating that active learning on even small datasets-but supplemented by informative structural-characterization data and coupled with closed-loop experimentation-can yield materials of outstanding performance.

Solid-body trajectoids shaped to roll along desired pathways.

In everyday life, rolling motion is typically associated with cylindrical (for example, car wheels) or spherical (for example, billiard balls) bodies tracing linear paths. However, mathematicians have, for decades, been interested in more exotically shaped solids such as the famous oloids, sphericons, polycons, platonicons and two-circle rollers that roll downhill in curvilinear paths (in contrast to cylinders or spheres) yet indefinitely (in contrast to cones, Supplementary Video 1). The trajectories traced by such bodies have been studied in detail, and can be useful in the context of efficient mixing and robotics, for example, in magnetically actuated, millimetre-sized sphericon-shaped robots, or larger sphericon- and oloid-shaped robots translocating by shifting their centre of mass.

One-Pot, Three-Phase Recycling of Metals from Li-Ion Batteries in Rotating, Concentric-Liquid Reactors.

Efficient recycling of spent lithium-ion batteries (LIBs) is essential for making their numerous applications sustainable. Hydrometallurgy-based separation methods are an indispensable part of the recycling process but remain limited by the extraction efficiency and selectivity, and typically require numerous binary liquid-liquid extraction steps in which the capacity of the extracting organic phase or partition coefficient of extracted metals become an overall bottleneck. Herein, rotating reactors are described, in which the aqueous feed, organic extractant, and aqueous acceptor phases are all present in the same rotating vessel and can be vigorously stirred and emulsified without the coalescence of aqueous layers.

On-nanoparticle monolayers as a solute-specific, solvent-like phase.

In addition to modifying surface properties, self-assembled monolayers, SAMs, on nanoparticles can selectively incorporate small molecules from the surrounding solution. This selectivity has been used in the design of substrate-specific catalytic systems but its degree has not been quantified. This work uses catalytic centers embedded in on-nanoparticle hydrophobic SAMs to monitor and quantify the partitioning of molecules between the bulk solvent and these monolayers.

Protocol and Software for Automated Detection of Lysosome Active "Runs" and "Flights" with Wavelet Transform Approach.

Lysosomes are highly dynamic degradation/recycling organelles that harbor sophisticated molecular sensors and signal transduction machinery through which they control cell adaptation to environmental cues and nutrients. The movements of these signaling hubs comprise persistent, directional runs-active, ATP-dependent transport along the microtubule tracks-interspersed by short, passive movements and pauses imposed by cytoplasmic constraints. The trajectories of individual lysosomes are usually obtained by time-lapse imaging of the acidic organelles labeled with LysoTracker dyes or fluorescently-tagged lysosomal-associated membrane proteins LAMP1 and LAMP2.

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling.

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions.

Proving Cooperativity of a Catalytic Reaction by Means of Nanoscale Geometry: The Case of Click Reaction.

Establishing whether a reaction is catalyzed by a single-metal catalytic center or cooperatively by a fleeting complex encompassing two such centers may be an arduous pursuit requiring detailed kinetic, isotopic, and other types of studies─as illustrated, for instance, by over a decade-long work on single-copper versus di-copper mechanisms of the popular "click" reaction. This paper describes a method to interrogate such cooperative mechanisms by a nanoparticle-based platform in which the probabilities of catalytic units being proximal can be varied systematically and, more importantly, independently of their volume concentration. The method relies on geometrical considerations rather than a detailed knowledge of kinetic equations, yet the scaling trends it yield can distinguish between cooperative and non-cooperative mechanisms.

Computer-designed repurposing of chemical wastes into drugs.

As the chemical industry continues to produce considerable quantities of waste chemicals, it is essential to devise 'circular chemistry' schemes to productively back-convert at least a portion of these unwanted materials into useful products. Despite substantial progress in the degradation of some classes of harmful chemicals, work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas. Comprehensive analyses of which valuable products are synthesizable from diverse chemical wastes are difficult because even small sets of waste substrates can, within few steps, generate millions of putative products, each synthesizable by multiple routes forming densely connected networks.

Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling.

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks─and a carefully selected database of >10,000 literature examples─we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases.

Large-Scale, Wavelet-Based Analysis of Lysosomal Trajectories and Co-Movements of Lysosomes with Nanoparticle Cargos.

Lysosomes-that is, acidic organelles known for degradation/recycling-move through the cytoplasm alternating between bursts of active transport and short, diffusive motions or even pauses. While their mobility is essential for lysosomes' fusogenic and non-fusogenic interactions with target organelles, their movements have not been characterized in adequate detail. Here, large-scale statistical analysis of lysosomal movement trajectories reveals that lysosome trajectories in all examined cell types-both cancer and noncancerous ones-are superdiffusive and characterized by heavy-tailed distributions of run and flight lengths.

An Electrocatalytic Reaction As a Basis for Chemical Computing in Water Droplets.

Aqueous droplets covered with amphiphilic Janus Au/FeO nanoparticles and suspended in an organic phase serve as building blocks of droplet-based electronic circuitry. The electrocatalytic activity of these nanoparticles in a hydrogen evolution reaction (HER) underlies the droplet's ability to rectify currents with typical rectification ratios of ∼10. In effect, individual droplets act as low-frequency half-wave rectifiers, whereas several appropriately wired droplets enable full-wave rectification.

Is Organic Chemistry Really Growing Exponentially?

In terms of molecules and specific reaction examples, organic chemistry features an impressive, exponential growth. However, new reaction classes/types that fuel this growth are being discovered at a much slower and only linear (or even sublinear) rate. The proportion of newly discovered reaction types to all reactions being performed keeps decreasing, suggesting that synthetic chemistry becomes more reliant on reusing the well-known methods.

Stimuli-responsive granular crystals assembled by dipolar and multipolar interactions.

This work describes granular crystals held together by unusual, multipolar interactions and, under the application of an external bias, undergoing reversible structural transitions between closed and open forms. The system comprises two types of polymeric beads agitated on one or between two conductive plates and gradually acquiring charges by contact electrification. The charges thus developed induce a series of electrostatic images in the conductive supports and, in effect, the beads interact dipolar or multipolar interactions, enabling the stabilization of non-electroneutral crystals.

Self-Assembling Films of Covalent Organic Frameworks Enable Long-Term, Efficient Cycling of Zinc-Ion Batteries.

Despite their safety, nontoxicity, and cost-effectiveness, zinc aqueous batteries still suffer from limited rechargeability and poor cycle life, largely due to spontaneous surface corrosion and formation of large Zn dendrites by irregular and uneven plating and stripping. In this work, these untoward effects are minimized by covering Zn electrodes with ultrathin layers of covalent organic frameworks, COFs. These nanoporous and mechanically flexible films form by self-assembly-via the straightforward and scalable dip-coating technique-and permit efficient mass and charge transport while suppressing surface corrosion and growth of large Zn dendrites.