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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. These results suggest that mechanistic-level approach to yield estimation may be a useful alternative to models that are derived from full-reaction data and lack information about yield-lowering side reactions., Competing Interests: Competing interests: The authors declare the following competing interests: S.S., A.W., R.R., and B.A.G. are consultants and/or stakeholders of Allchemy, Inc. Allchemy software and its MECH module is property of Allchemy, Inc. USA. All queries about access options to Allchemy, including academic collaborations, should be sent to saraszymkuc@allchemy.net., (© 2024. The Author(s).)

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. This work demonstrates that computers taught the essential knowledge of reaction mechanisms and rules of physical-organic chemistry can design - completely autonomously and in large numbers - mechanistically distinct MCRs. Moreover, when supplemented by models to approximate kinetic rates, the algorithm can predict reaction yields and identify reactions that have potential for organocatalysis. These predictions are validated by experiments spanning different modes of reactivity and diverse product scaffolds., Competing Interests: Competing interests: The authors declare the following competing interests: R.R., A.W., K.M, B.M.-K., T.K., S.S., M.M. and B.A.G. are consultants and/or stakeholders of Allchemy, Inc. Allchemy software and its MECH module is property of Allchemy Inc., USA. All queries about access options to Allchemy, including academic collaborations, should be sent to saraszymkuc@allchemy.net. L.G., A.M., Y.B., P.G., O.P., S.B., R.F., J.M., O.V. and D.T.G. declare no competing interest., (© 2024. The Author(s).)

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. In effect, oxygen serves as a reagent that dictates size, structure, stability, and functional properties of the growing nanoobjects., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

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. In one modality, the levitating droplets operate as completely wireless aliquoting/titrating systems delivering pg quantities of reagents into the liquid in the rotating vial; in another modality, they print microdroplet arrays onto target surfaces. The "airware", levitated reactors are inexpensive to set up, remarkably stable to external disturbances and, for printing applications, require operating voltages much lower than in electrospray, electrowetting, or ink jet systems., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

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. As we show here for the case of asymmetric magnesium catalysis, this model achieves relatively high accuracy and offers out of-the-box predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

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. These dynamic assemblies include open-lattice structures with individual particles performing intricate translational and/or rotational motions, density-gradient particle arrays, nested architectures of mechanical components (e.g., planetary gears), or swarms of light-actuated microbots controlling assembly of other objects., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

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. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

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. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.

Recent years have seen revived interest in computer-assisted organic synthesis 1,2 . The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field 1,3-7 , including examples leading to advanced natural products 6,7 . 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. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule's carbon skeleton 8-12 . The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types 1-7 but will help rationalize and discover new, mechanistically complex transformations., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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. The model we develop not only reproduces several non-obvious and actively studied experimental trends but also identifies a composition of a perovskite oxide electrocatalyst exhibiting an intrinsic overpotential at 10 mA cm -2 oxide of 391 mV, which is among the lowest known of four-metal perovskite oxides., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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.

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 1 , sphericons 2 , polycons 3 , platonicons 4 and two-circle rollers 5 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 6-9 , and can be useful in the context of efficient mixing 10,11 and robotics, for example, in magnetically actuated, millimetre-sized sphericon-shaped robots 12,13 , or larger sphericon- and oloid-shaped robots translocating by shifting their centre of mass 14,15 . However, the rolling paths of these shapes are all sinusoid-like and their diversity ends there. Accordingly, we were intrigued whether a more general problem is solvable: given an infinite periodic trajectory, find the shape that would trace this trajectory when rolling down a slope. Here, we develop an algorithm to design such bodies-which we call 'trajectoids'-and then validate these designs experimentally by three-dimensionally printing the computed shapes and tracking their rolling paths, including those that close onto themselves such that the body's centre of mass moves intermittently uphill (Supplementary Video 2). Our study is motivated largely by fundamental curiosity, but the existence of trajectoids for most paths has unexpected implications for quantum and classical optics, as the dynamics of qubits, spins and light polarization can be exactly mapped to trajectoids and their paths 16 ., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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. In this arrangement, the extractant molecules are not equilibrated with the feed and, instead, "shuttle" between the feed/extractant and the extractant/acceptor interfaces multiple times, with each such molecule ultimately transferring approximately ten metal ions. This shuttling allows for using extractant concentrations much lower than in previous designs even for extremely concentrated feeds and, simultaneously, ensures unprecedented speed and selectivity of the one-pot processes. These experimental results are accompanied by theoretical considerations of the selectivity versus speed trends as well as discussion of parameters essential for system upscaling., (© 2023 Wiley-VCH GmbH.)

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. A combination of experiments and theory allows us to relate the logarithm of the incorporation-into-SAM constant to the "bulk" log  P values, characterizing the incoming substrates. These results are in line with classic, semi-empirical linear free energy relationships between partitioning solvent systems; in this way, they substantiate the view of nanoscopic on-particle SAMs acting akin to a bulk solvent phase.

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. Subsequent particle tracking generates large data sets comprising thousands of lysosome trajectories and hundreds of thousands of data points. Analyzing such data sets requires unbiased, automated methods to handle large data sets while capturing the temporal heterogeneity of lysosome trajectory data. This chapter describes integrated and largely automated workflow from live cell imaging to lysosome trajectories to computing the parameters of lysosome dynamics. We describe an open-source code for implementing the continuous wavelet transform (CWT) to distinguish trajectory segments corresponding to active transport (i.e., "runs" and "flights") versus passive lysosome movements. Complementary cumulative distribution functions (CDFs) of the "runs/flights" are generated, and Akaike weight comparisons with several competing models (lognormal, power law, truncated power law, stretched exponential, exponential) are performed automatically. Such high-throughput analyses yield useful aggregate/ensemble metrics for lysosome active transport., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.

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.

As the chemical industry continues to produce considerable quantities of waste chemicals 1,2 , it is essential to devise 'circular chemistry' 3-8 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 9 , work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas 10-15 . 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. Tracing all such syntheses and selecting those that also meet criteria of process and 'green' chemistries is, arguably, beyond the cognition of human chemists. Here we show how computers equipped with broad synthetic knowledge can help address this challenge. Using the forward-synthesis Allchemy platform 16 , we generate giant synthetic networks emanating from approximately 200 waste chemicals recycled on commercial scales, retrieve from these networks tens of thousands of routes leading to approximately 300 important drugs and agrochemicals, and algorithmically rank these syntheses according to the accepted metrics of sustainable chemistry 17-19 . Several of these routes we validate by experiment, including an industrially realistic demonstration on a 'pharmacy on demand' flow-chemistry platform 20 . Wide adoption of computerized waste-to-valuable algorithms can accelerate productive reuse of chemicals that would otherwise incur storage or disposal costs, or even pose environmental hazards., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

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. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chemical descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjective preferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents, and/or a lack of negative data. These findings highlight the likely importance of systematically generating reliable and standardized data sets for algorithm training.

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. Consideration of Akaike weights for various potential models (lognormal, power law, truncated power law, stretched exponential, and exponential) indicates that the experimental data are best described by the lognormal distribution, which, in turn, can be related to one of the space-search strategies particularly effective when "thorough" search needs to balance search for rare target(s) (organelles). In addition, automated, wavelet-based analysis allows for co-tracking the motions of lysosomes and the cargos they carry-particularly the nanoparticle aggregates known to cause selective lysosome disruption in cancerous cells. The methods we describe here could help study nanoparticle assemblies, viruses, and other objects transported inside various vesicle types, as well as coordinated movements of organelles/particles in the cytoplasm. Custom-written code that includes integrated workflow for our analyses is made available for academic use.

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. The newly discovered chemistries are more complex than decades ago and allow for the rapid construction of complex scaffolds in fewer numbers of steps. We study these and other trends in the function of time, reaction-type popularity and complexity based on the algorithm that extracts generalized reaction class templates. These analyses are useful in the context of computer-assisted synthesis, machine learning (to estimate the numbers of models with sufficient reaction statistics), and identifying erroneous entries in reaction databases., (© 2021 Wiley-VCH GmbH.)

Aqueous droplets covered with amphiphilic Janus Au/Fe 3 O 4 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. When the HER-supporting droplets are combined with salt-containing "resistor" ones, the resulting ensembles can act as AND or OR gates or as inverters.

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 via dipolar or multipolar interactions, enabling the stabilization of non-electroneutral crystals. Furthermore, under an applied bias, the beads become polarized and their complex interactions (due to the series of image charges as well as series of image dipoles) result in open-pore crystals which return to compact forms upon bias removal. These effects are rationalized by analytical calculations, and the crystal structures observed in the experiments are reproduced by molecular dynamics simulations.

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. The batteries demonstrated have excellent capacity retention and stable polarization voltage for over 420 h of cycling at 1 mA cm -2 . The COF films essential for these improvements can be readily deposited over large areas and curvilinear supports, enabling, for example, foldable wire-type batteries., (© 2021 Wiley-VCH GmbH.)

Targeted delivery of molecular cargos to specific organelles is of paramount importance for developing precise and effective therapeutics and imaging probes. This work describes a disulfide-based delivery method in which mixed-charged nanoparticles traveling through the endolysosomal tract deliver noncovalently bound dye molecules selectively into mitochondria. This system comprises three elements: (1) The nanoparticles deliver their payloads by a kiss-and-go mechanism - that is, they drop off their dye cargos proximate to mitochondria but do not localize therein; (2) the dye molecules are by themselves nonspecific to any cellular structures but become so with the help of mixed-charge nanocarriers; and (3) the dye is engineered in such a way as to remain in mitochondria for a long time, up to days, allowing for observing dynamic remodeling of mitochondrial networks and long-term tracking of mitochondria even in dividing cells. The selectivity of delivery and long-lasting staining derive from the ability to engineer charge-imbalanced, mixed [+/-] on-particle monolayers and from the structural features of the cargo. Regarding the former, the balance of [+] and [-] ligands can be adjusted to limit cytotoxicity and control the number of dye molecules adsorbed onto the particles' surfaces. Regarding the latter, comparative studies with multiple dye derivatives we synthesized rationalize the importance of polar groups, long alkyl chains, and disulfide moieties in the assembly of fluorescent nanoconstructs and long-lasting staining of mitochondria. Overall, this strategy could be useful for delivering hydrophilic and/or anionic small-molecule drugs difficult to target to mitochondria by classical approaches.

This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms., (© 2021 Wiley-VCH GmbH.)

Teaching computers to plan multistep syntheses of arbitrary target molecules-including natural products-has been one of the oldest challenges in chemistry, dating back to the 1960s. This Account recapitulates two decades of our group's work on the software platform called Chematica, which very recently achieved this long-sought objective and has been shown capable of planning synthetic routes to complex natural products, several of which were validated in the laboratory.For the machine to plan syntheses at an expert level, it must know the rules describing chemical reactions and use these rules to expand and search the networks of synthetic options. The rules must be of high quality: They must delineate accurately the scope of admissible substituents, capture all relevant stereochemical information, detect potential reactivity conflicts, and protection requirements. They should yield only those synthons that are chemically stable and energetically allowed (e.g., not too strained) and should be able to extrapolate beyond examples already published in the literature. In parallel, the network-search algorithms must be able to assign meaningful scores to the sets of synthons they encounter, make judicious choices which of the network's branches to expand, and when to withdraw from unpromising ones. They must be able to strategize over multiple steps to resolve intermittent reactivity conflicts, exchange functional groups, or overcome local maxima of molecular complexity.Meeting all these requirements makes the problem of computer-driven retrosynthesis very multifaceted, combining expert and AI approaches further supplemented by quantum-mechanical and molecular-mechanics calculations. Development of Chematica has been a very long and gradual process because all these components are needed. Any shortcuts-for example, reliance on only expert or only data-based approaches-yield chemically naïve and often erroneous syntheses, especially for complex targets. On the bright side, once all the requisite algorithms are implemented-as they now are-they not only streamline conventional synthetic planning but also enable completely new modalities that would challenge any human chemist, for example, synthesis with multiple constraints imposed simultaneously or library-wide syntheses in which the machine constructs "global plans" leading to multiple targets and benefiting from the use of common intermediates. These types of analyses will have profound impact on the practice of chemical industry, designing more economical, more green, and less hazardous pathways.

When an organometallic catalyst is tethered onto a nanoparticle and is embedded in a monolayer of longer ligands terminated in "gating" end-groups, these groups can control the access and orientation of the incoming substrates. In this way, a nonspecific catalyst can become enzyme-like: it can select only certain substrates from substrate mixtures and, quite remarkably, can also preorganize these substrates such that only some of their otherwise equivalent sites react. For a simple, copper-based click reaction catalyst and for gating ligands terminated in charged groups, both substrate- and site-selectivities are on the order of 100, which is all the more notable given the relative simplicity of the on-particle monolayers compared to the intricacy of enzymes' active sites. The strategy of self-assembling macromolecular, on-nanoparticle environments to enhance selectivities of "ordinary" catalysts presented here is extendable to other types of catalysts and gating based on electrostatics, hydrophobicity, and chirality, or the combinations of these effects. Rational design of such systems should be guided by theoretical models we also describe.

Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years 1-7 . However, the field has progressed greatly since the development of early programs such as LHASA 1,7 , for which reaction choices at each step were made by human operators. Multiple software platforms 6,8-14 are now capable of completely autonomous planning. But these programs 'think' only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary 15,16 and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program's knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships 17,18 , allowing it to 'strategize' over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization.

Recent years have witnessed increased interest in systems that are capable of supporting multistep chemical processes without the need for manual handling of intermediates. These systems have been based either on collections of batch reactors 1 or on flow-chemistry designs 2-4 , both of which require considerable engineering effort to set up and control. Here we develop an out-of-equilibrium system in which different reaction zones self-organize into a geometry that can dictate the progress of an entire process sequence. Multiple (routinely around 10, and in some cases more than 20) immiscible or pairwise-immiscible liquids of different densities are placed into a rotating container, in which they experience a centrifugal force that dominates over surface tension. As a result, the liquids organize into concentric layers, with thicknesses as low as 150 micrometres and theoretically reaching tens of micrometres. The layers are robust, yet can be internally mixed by accelerating or decelerating the rotation, and each layer can be individually addressed, enabling the addition, sampling or even withdrawal of entire layers during rotation. These features are combined in proof-of-concept experiments that demonstrate, for example, multistep syntheses of small molecules of medicinal interest, simultaneous acid-base extractions, and selective separations from complex mixtures mediated by chemical shuttles. We propose that 'wall-less' concentric liquid reactors could become a useful addition to the toolbox of process chemistry at small to medium scales and, in a broader context, illustrate the advantages of transplanting material and/or chemical systems from traditional, static settings into a rotating frame of reference.

The challenge of prebiotic chemistry is to trace the syntheses of life's key building blocks from a handful of primordial substrates. Here we report a forward-synthesis algorithm that generates a full network of prebiotic chemical reactions accessible from these substrates under generally accepted conditions. This network contains both reported and previously unidentified routes to biotic targets, as well as plausible syntheses of abiotic molecules. It also exhibits three forms of nontrivial chemical emergence, as the molecules within the network can act as catalysts of downstream reaction types; form functional chemical systems, including self-regenerating cycles; and produce surfactants relevant to primitive forms of biological compartmentalization. To support these claims, computer-predicted, prebiotic syntheses of several biotic molecules as well as a multistep, self-regenerative cycle of iminodiacetic acid were validated by experiment., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

When components of a metal-organic framework (MOF) and a crystal growth modulator diffuse through a gel medium, they can form arrays of regularly-spaced precipitation bands containing MOF crystals of different morphologies. With time, slow variations in the local concentrations of the growth modulator cause the crystals to change their shapes, ultimately resulting in unusual concave microcrystallites not available via solution-based methods. The reaction-diffusion and periodic precipitation phenomena 1) extend to various types of MOFs and also MOPs (metal-organic polyhedra), and 2) can be multiplexed to realize within one gel multiple growth conditions, in effect leading to various crystalline phases or polycrystalline formations., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A computer program for retrosynthetic planning helps develop multiple "synthetic contingency" plans for hydroxychloroquine and also routes leading to remdesivir, both promising but yet unproven medications against COVID-19. These plans are designed to navigate, as much as possible, around known and patented routes and to commence from inexpensive and diverse starting materials, so as to ensure supply in case of anticipated market shortages of commonly used substrates. Looking beyond the current COVID-19 pandemic, development of similar contingency syntheses is advocated for other already-approved medications, in case such medications become urgently needed in mass quantities to face other public-health emergencies., (This journal is © The Royal Society of Chemistry 2020.)

Lysosomes have become an important target for anticancer therapeutics because lysosomal cell death bypasses the classical caspase-dependent apoptosis pathway, enabling the targeting of apoptosis- and drug-resistant cancers. However, only a few small molecules-mostly repurposed drugs-have been tested so far, and these typically exhibit low cancer selectivity, making them suitable only for combination therapies. Here, we show that mixed-charge nanoparticles covered with certain ratios of positively and negatively charged ligands can selectively target lysosomes in cancerous cells while exhibiting only marginal cytotoxicity towards normal cells. This selectivity results from distinct pH-dependent aggregation events, starting from the formation of small, endocytosis-prone clusters at cell surfaces and ending with the formation of large and well-ordered nanoparticle assemblies and crystals inside cancer lysosomes. These assemblies cannot be cleared by exocytosis and cause lysosome swelling, which gradually disrupts the integrity of lysosomal membranes, ultimately impairing lysosomal functions and triggering cell death.

The ability to grow properly sized and good quality crystals is one of the cornerstones of single-crystal diffraction, is advantageous in many industrial-scale chemical processes 1-3 , and is important for obtaining institutional approvals of new drugs for which high-quality crystallographic data are required 4-7 . Typically, single crystals suitable for such processes and analyses are grown for hours to days during which any mechanical disturbances-believed to be detrimental to the process-are carefully avoided. In particular, stirring and shear flows are known to cause secondary nucleation, which decreases the final size of the crystals (though shear can also increase their quantity 8-14 ). Here we demonstrate that in the presence of polymers (preferably, polyionic liquids), crystals of various types grow in common solvents, at constant temperature, much bigger and much faster when stirred, rather than kept still. This conclusion is based on the study of approximately 20 diverse organic molecules, inorganic salts, metal-organic complexes, and even some proteins. On typical timescales of a few to tens of minutes, these molecules grow into regularly faceted crystals that are always larger (with longest linear dimension about 16 times larger) than those obtained in control experiments of the same duration but without stirring or without polymers. We attribute this enhancement to two synergistic effects. First, under shear, the polymers and their aggregates disentangle, compete for solvent molecules and thus effectively 'salt out' (that is, induce precipitation by decreasing solubility of) the crystallizing species. Second, the local shear rate is dependent on particle size, ultimately promoting the growth of larger crystals (but not via surface-energy effects as in classical Ostwald ripening). This closed-system, constant-temperature crystallization driven by shear could be a valuable addition to the repertoire of crystal growth techniques, enabling accelerated growth of crystals required by the materials and pharmaceutical industries.

When a thin polymer film supported by a conductive substrate is contacted by and then separated from a micropatterned polymeric stamp, the so-called contact electrification creates electrical charges over the stamped regions. Simultaneously, image charges are induced in the conductive substrate. Together, the surface and image charges establish large fields within the film, in effect polarizing it. Upon consecutive stampings, the magnitudes of polarization add up, enabling imprinting of multilevel polarization patterns. Because the electric field is high only within the film but low across the Gaussian surface surrounding the film/substrate system, the discharge of surface charges is slow and the polarization patterns are relatively long-lived. These findings are significant since multilevel polarization states have, to date, been achieved only in ferroelectrics or some specialized polymers-the current method extends them to common polymers such as poly(methyl methacrylate), poly(vinyl pyrrolidone), or poly(vinyl acetate).

When computers plan multistep syntheses, they can rely either on expert knowledge or information machine-extracted from large reaction repositories. Both approaches suffer from imperfect functions evaluating reaction choices: expert functions are heuristics based on chemical intuition, whereas machine learning (ML) relies on neural networks (NNs) that can make meaningful predictions only about popular reaction types. This paper shows that expert and ML approaches can be synergistic-specifically, when NNs are trained on literature data matched onto high-quality, expert-coded reaction rules, they achieve higher synthetic accuracy than either of the methods alone and, importantly, can also handle rare/specialized reaction types., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Although metal nanoparticles (NPs) stabilized with self-assembled monolayers (SAMs) of various organic ligands have proven useful in applications ranging from chemical sensing, to bionanotechnology, to plasmonics and energy conversion, they have not been widely considered as suitable building blocks of electronic circuitry, largely because metals screen electric fields and prevent electrically tunable conductivity. However, when metal nanoparticles a few nanometers in size are stabilized by charged ligands and placed under bias, the counterions surrounding the NPs can redistribute and establish local electric fields that feed back into the electronic currents passing through the nanoparticles' metallic cores. Herein, the manner in which the interplay between counterion gradients and electron flows can be controlled by using different types of SAMs is discussed. This can give rise to a new class of nanoparticle-based "chemoelectronic" logic circuits capable of sensing, processing, and ultimately reporting various chemical signals., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The ability to estimate the acidity of C-H groups within organic molecules in non-aqueous solvents is important in synthetic planning to correctly predict which protons will be abstracted in reactions such as alkylations, Michael additions, or aldol condensations. This Article describes the use of the so-called graph convolutional neural networks (GCNNs) to perform such predictions on the time scales of milliseconds and with accuracy comparing favorably with state-of-the-art solutions, including commercial ones. The crux of the method is to train GCNNs using descriptors that reflect not only topological but also chemical properties of atomic environments. The model is validated against adversarial controls, supplemented by the discussion of realistic synthetic problems (on which it correctly predicts the most acidic protons in >90% of cases), and accompanied by a Web application intended to aid the community in everyday synthetic planning.

Although computer programs for retrosynthetic planning have shown improved and in some cases quite satisfactory performance in designing routes leading to specific, individual targets, no algorithms capable of planning syntheses of entire target libraries - important in modern drug discovery - have yet been reported. This study describes how network-search routines underlying existing retrosynthetic programs can be adapted and extended to multi-target design operating on one common search graph, benefitting from the use of common intermediates and reducing the overall synthetic cost. Implementation in the Chematica platform illustrates the usefulness of such algorithms in the syntheses of either (i) all members of a user-defined library, or (ii) the most synthetically accessible members of this library. In the latter case, algorithms are also readily adapted to the identification of the most facile syntheses of isotopically labelled targets. These examples are industrially relevant in the context of hit-to-lead optimization and syntheses of isotopomers of various bioactive molecules., (This journal is © The Royal Society of Chemistry 2019.)

Stable, purely fluidic particle traps established by vortex flows induced within a rotating fluid are described. The traps can manipulate various types of small parts, dynamically assembling them into high-symmetry clusters, cages, interlocked architectures, jammed colloidal monoliths, or colloidal formations on gas bubbles. The strength and the shape of the trapping region can be controlled by the strengths of one or both vortices and/or by the system's global angular velocity. The system exhibits a range of interesting dynamical behaviors including a Hopf-bifurcation transition between equilibrium-point trapping and the so-called limit cycle in which the particles are confined to circular orbits. Theoretical considerations indicate that these vortex-vortex traps can be further miniaturized to manipulate objects with sizes down to ≈10 µm., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Dendritic cells (DCs) are potent antigen-presenting cells with high sentinel ability to scan their neighborhood and to initiate an adaptive immune response. Whereas chemotactic migration of mature DCs (mDCs) towards lymph nodes is relatively well documented, the migratory behavior of immature DCs (imDCs) in tumor microenvironments is still poorly understood. Here, microfluidic systems of various geometries, including mazes, are used to investigate how the physical and chemical microenvironment influences the migration pattern of imDCs. Under proper degree of confinement, the imDCs are preferentially recruited towards cancer vs. normal cells, accompanied by increased cell speed and persistence. Furthermore, a systematic screen of cytokines, reveals that Gas6 is a major chemokine responsible for the chemotactic preference. These results and the accompanying theoretical model suggest that imDC migration in complex tissue environments is tuned by a proper balance between the strength of the chemical gradients and the degree of spatial confinement.

Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer's help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible "intermediate" atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and - above all - machine extraction of reaction rules underlying modern synthesis-planning programs.

Machine learning can predict the major regio-, site-, and diastereoselective outcomes of Diels-Alder reactions better than standard quantum-mechanical methods and with accuracies exceeding 90 % provided that i) the diene/dienophile substrates are represented by "physical-organic" descriptors reflecting the electronic and steric characteristics of their substituents and ii) the positions of such substituents relative to the reaction core are encoded ("vectorized") in an informative way., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Akin to electronic systems that can tune to and process signals of select frequencies, systems/networks of chemical reactions also "propagate" time-varying concentration inputs in a frequency-dependent manner. Whereas signals of low frequencies are transmitted, higher frequency inputs are dampened and converted into steady-concentration outputs. Such behavior is observed in both idealized reaction chains as well as realistic signaling cascades, in the latter case explaining the experimentally observed responses of such cascades to input calcium oscillations. These and other results are supported by numerical simulations within the freely available Kinetix web application we developed to study chemical systems of arbitrary architectures, reaction kinetics, and boundary conditions., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

As the programs for computer-aided retrosynthetic design come of age, they are no longer identifying just one or few synthetic routes but a multitude of chemically plausible syntheses, together forming large, directed graphs of solutions. An important problem then emerges: how to select from these graphs and present to the user manageable numbers of top-scoring pathways that are cost-effective, promote convergent vs. linear solutions, and are chemically diverse so that they do not repeat only minor variations in the same chemical theme. This paper describes a family of reaction network algorithms that address this problem by (i) using recursive formulae to assign realistic prices to individual pathways and (ii) applying penalties to chemically similar strategies so that they are not dominating the top-scoring routes. Synthetic examples are provided to illustrate how these algorithms can be implemented - on the timescales of ∼1 s even for large graphs - to rapidly query the space of synthetic solutions under the scenarios of different reaction yields and/or costs associated with performing reaction operations on different scales.

Correction for 'Electrostatics at the nanoscale' by David A. Walker et al., Nanoscale, 2011, 3, 1316-1344.

The magnitudes of the charges developed on contact-electrified polymers depend on not only the properties of these materials but also the nature of distant substrates on which the polymers are supported. In particular, image charges induced in conductive substrates can decrease charges on the polymers by arc discharge through the surrounding gas. This mode of charge dissipation occurs on timescales of milliseconds and can be prevented by insulating the sharp edges of the conductive supports., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)