Computational prediction of complex cationic rearrangement outcomes.

Recent years have seen revived interest in computer-assisted organic synthesis1,2. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field1,3-7, including examples leading to advanced natural products6,7. 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 skeleton8-12. 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 types1-7 but will help rationalize and discover new, mechanistically complex transformations.

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 oloids1, sphericons2, polycons3, platonicons4 and two-circle rollers5 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 detail6-9, and can be useful in the context of efficient mixing10,11 and robotics, for example, in magnetically actuated, millimetre-sized sphericon-shaped robots12,13, or larger sphericon- and oloid-shaped robots translocating by shifting their centre of mass14,15. 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 paths16.

Computer-designed repurposing of chemical wastes into drugs.

As the chemical industry continues to produce considerable quantities of waste chemicals1,2, 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 chemicals9, work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas10-15. 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 platform16, 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 chemistry17-19. Several of these routes we validate by experiment, including an industrially realistic demonstration on a 'pharmacy on demand' flow-chemistry platform20. 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.

Enhancing crystal growth using polyelectrolyte solutions and shear flow.

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 processes1-3, and is important for obtaining institutional approvals of new drugs for which high-quality crystallographic data are required4-7. 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 quantity8-14). 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.

Concentric liquid reactors for chemical synthesis and separation.

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 reactors1 or on flow-chemistry designs2-4, 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.

Computational planning of the synthesis of complex natural products.

Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years1-7. However, the field has progressed greatly since the development of early programs such as LHASA1,7, for which reaction choices at each step were made by human operators. Multiple software platforms6,8-14 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 necessary15,16 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 relationships17,18, 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.

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. 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-2oxide of 391 mV, which is among the lowest known of four-metal perovskite oxides.

Author Correction: Systems of mechanized and reactive droplets powered by multi-responsive surfactants.

In this Letter, the top structure of the right panel of Fig. 1a should be that of cis-9-octadecenoic acid, not trans-9-octadecenoic acid. Please see the accompanying Author Correction. This error has not been corrected online.

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. 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.

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. In effect, oxygen serves as a reagent that dictates size, structure, stability, and functional properties of the growing nanoobjects.

Systems of mechanized and reactive droplets powered by multi-responsive surfactants.

Although 'active' surfactants, which are responsive to individual external stimuli such as temperature, electric or magnetic fields, light, redox processes or chemical agents, are well known, it would be interesting to combine several of these properties within one surfactant species. Such multi-responsive surfactants could provide ways of manipulating individual droplets and possibly assembling them into larger systems of dynamic reactors. Here we describe surfactants based on functionalized nanoparticle dimers that combine all of these and several other characteristics. These surfactants and therefore the droplets that they cover are simultaneously addressable by magnetic, optical and electric fields. As a result, the surfactant-covered droplets can be assembled into various hierarchical structures, including dynamic ones, in which light powers the rapid rotation of the droplets. Such rotating droplets can transfer mechanical torques to their non-nearest neighbours, thus acting like systems of mechanical gears. Furthermore, droplets of different types can be merged by applying electric fields and, owing to interfacial jamming, can form complex, non-spherical, 'patchy' structures with different surface regions covered with different surfactants. In systems of droplets that carry different chemicals, combinations of multiple stimuli can be used to control the orientations of the droplets, inter-droplet transport, mixing of contents and, ultimately, sequences of chemical reactions. Overall, the multi-responsive active surfactants that we describe provide an unprecedented level of flexibility with which liquid droplets can be manipulated, assembled and reacted.

Aerodynamically Levitated Droplets as Small-scale 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. In one modality, the levitating droplets operate as completely wirelesss 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.

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. 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.

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.

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.

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. 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.

Chemist Ex Machina: Advanced Synthesis Planning by Computers.

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.

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

Aqueous droplets covered with amphiphilic Janus Au/Fe3O4 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.

On-Nanoparticle Gating Units Render an Ordinary Catalyst Substrate- and Site-Selective.

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.

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 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.