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. 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.

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.

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.

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. 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.

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.

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. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

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.

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.

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.

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.

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. 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.

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. 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.

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. 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.

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. 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.

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 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.

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.

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. 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.

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.