Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

Low-Dispersity Polymers via Free Radical Alternating Copolymerization: Effects of Charge-Transfer-Complexes

Alternating copolymers are crucial for diverse applications. While dispersity (Ð, also known as molecular weight distribution, MWD) influences the properties of polymers, achieving low dispersities in alternating copolymers poses a notable challenge via free radical polymerizations (FRPs). In this work, we demonstrated an unexpected discovery that dispersities are affected by the participation of charge transfer complexes (CTCs) formed between monomer pairs during free radical alternating copolymerization, which have inspired the successful synthesis of various alternating copolymers with low dispersities (>30 examples, Ð=1.13-1.39) under visible-light irradiation. The synthetic method is compatible with binary, ternary and quaternary alternating copolymerizations and is expandable for both fluorinated and non-fluorinated monomer pairs. DFT calculations combined with model experiments indicated that CTC-absent reaction exhibits higher propagation rates and affords fewer radical terminations, which could contribute to low dispersities. Based on the integration of Monte Carlo simulation and Bayesian optimization, we established the relationship map between FRP parameter space and dispersity, further suggested the correlation between low dispersities and higher propagation rates. Our research sheds light on dispersity control via FRPs and creates a novel platform to investigate polymer dispersity through machine learning.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Stimuli-Responsive Polymers for Advanced 19F Magnetic Resonance Imaging: From Chemical Design to Biomedical Applications

Fluorine magnetic resonance imaging (19F MRI) is a rapidly evolving research area with a high potential to advance the field of clinical diagnostics. In this review, we provide an overview of the recent progress in the field of fluorinated stimuli-responsive polymers applied as 19F MRI tracers. These polymers respond to internal or external stimuli (e.g., temperature, pH, oxidative stress, and specific molecules) by altering their physicochemical properties, such as self-assembly, drug release, and polymer degradation. Incorporating noninvasive 19F labels enables us to track the biodistribution of such polymers. Furthermore, by triggering polymer transformation, we can induce changes in 19F MRI signals, including attenuation, amplification, and chemical shift changes, to monitor alterations in the environment of the tracer. Ultimately, this review highlights the emerging potential of stimuli-responsive fluoropolymer 19F MRI tracers in the current context of polymer diagnostics research.

Controlled Radical Copolymerization toward Tailored F/N Hybrid Polymers by Using Light-Driven Organocatalysis

Controlled radical copolymerizations present attractive avenues to obtain polymers with complicated compositions and sequences. In this work, we report the development of a visible-light-driven organocatalyzed controlled copolymerization of fluoroalkenes and acyclic N-vinylamides for the first time. The approach enables the on-demand synthesis of a broad scope of amide-functionalized main-chain fluoropolymers via novel fluorinated thiocarbamates, facilitating regulations over chemical compositions and alternating fractions by rationally selecting comonomer pairs and ratios. This method allows temporally controlled chain-growth by external light, and maintains high chain-end fidelity that promotes facile preparation of block sequences. Notably, the obtained F/N hybrid polymers, upon hydrolysis, afford free amino-substituted fluoropolymers versatile for post modifications toward various functionalities (e.g., amide, sulfonamide, carbamide, thiocarbamide). We further demonstrate the in situ formation of polymer networks with desirable properties as protective layers on lithium metal anodes, presenting a promising avenue for advancing lithium metal batteries.

Machine learning assisted sorting of active microswimmers

Active matter systems, being in a non-equilibrium state, exhibit complex behaviors, such as self-organization, giving rise to emergent phenomena. There are many examples of active particles with biological origins, including bacteria and spermatozoa, or with artificial origins, such as self-propelled swimmers and Janus particles. The ability to manipulate active particles is vital for their effective application, e.g., separating motile spermatozoa from nonmotile and dead ones, to increase fertilization chance. In this study, we proposed a mechanism-an apparatus-to sort and demix active particles based on their motility values (Péclet number). Initially, using Brownian simulations, we demonstrated the feasibility of sorting self-propelled particles. Following this, we employed machine learning methods, supplemented with data from comprehensive simulations that we conducted for this study, to model the complex behavior of active particles. This enabled us to sort them based on their Péclet number. Finally, we evaluated the performance of the developed models and showed their effectiveness in demixing and sorting the active particles. Our findings can find applications in various fields, including physics, biology, and biomedical science, where the sorting and manipulation of active particles play a pivotal role.

Machine Learning-Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers

Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications.

Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning-Regulated Solvent-Precursor Interactions for Superior Zinc Storage

Carbon superstructures with exquisite morphologies and functionalities show appealing prospects in energy realms, but the systematic tailoring of their microstructures remains a perplexing topic. Here, hydrangea-shaped heterodiatomic carbon superstructures (CHS) are designed using a solution phase manufacturing route, wherein machine learning workflow is applied to screen precursor-matched solvent for optimizing solvent-precursor interaction. Based on the established solubility parameter model and molecular growth kinetics simulation, ethanol as the optimal solvent stimulates thermodynamic solubilization and growth of polymeric intermediates to evoke CHS. Featured with surface-active motifs and consecutive charge transfer paths, CHS allows high accessibility of zincophilic sites and fast ion migration with low energy barriers. A anion-cation hybrid charge storage mechanism of CHS cathode is disclosed, which entails physical alternate uptake of Zn2+/CF3SO3 - ions at electroactive sites and chemical bipedal redox of Zn2+ ions with carbonyl/pyridine motifs. Such a beneficial electrochemistry contributes to all-round improvement in Zn-ion storage, involving excellent capacities (231 mAh g-1 at 0.5 A g-1; 132 mAh g-1 at 50 A g-1), high energy density (152 Wh kg-1), and long-lasting cyclability (100 000 cycles). This work expands the design versatilities of superstructure materials and will accelerate experimental procedures during carbon manufacturing through machine learning in the future.

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data

Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes a machine learning approach that leverages transfer learning and ensemble modeling to efficiently predict the glass transition temperature (Tg) of fluorinated polymers and guide the design of high Tg copolymers. Initially, the quantum machine 9 (QM9) dataset is employed for model pretraining, thus providing robust molecular representations for the subsequent fine-tuning of a specialized copolymer dataset. Ensemble modeling is used to further enhance prediction robustness and reliability, effectively addressing the problems owing to the limited and unevenly distributed nature of the copolymer dataset. Finally, a fine-tuned ensemble model is used to navigate a vast chemical space comprising 61 monomers and identify promising candidates for high Tg fluorinated polymers. The model predicts 247 entries capable of achieving a Tg over 390 K, of which 14 are experimentally validated. This study demonstrates the potential of machine learning in material design and discovery, highlighting the effectiveness of transfer learning and ensemble modeling strategies for overcoming the challenges posed by small datasets in complex copolymer systems.

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion

Synthetic polymers are of paramount importance in modern life - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol-1) using reversible addition-fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures.

Combinatorial High-Throughput Screening of Complex Polymeric Enzyme Immobilization Supports

Recent advances have demonstrated the promise of complex multicomponent polymeric supports to enable supra-biological enzyme performance. However, the discovery of such supports has been limited by time-consuming, low-throughput synthesis and screening. Here, we describe a novel combinatorial and high-throughput platform that enables rapid screening of complex and heterogeneous copolymer brushes as enzyme immobilization supports, named combinatorial high-throughput enzyme support screening (CHESS). Using a 384-well plate format, we synthesized arrays of three-component polymer brushes in the microwells using photoactivated surface-initiated polymerization and immobilized enzymes in situ. The utility of CHESS to identify optimal immobilization supports under thermally and chemically denaturing conditions was demonstrated usingBacillus subtilisLipase A (LipA). The identification of supports with optimal compositions was validated by immobilizing LipA on polymer-brush-modified biocatalyst particles. We further demonstrated that CHESS could be used to predict the optimal composition of polymer brushes a priori for the previously unexplored enzyme, alkaline phosphatase (AlkP). Our findings demonstrate that CHESS represents a predictable and reliable platform for dramatically accelerating the search of chemical compositions for immobilization supports and further facilitates the discovery of biocompatible and stabilizing materials.

High-Throughput Synthesis, Purification, and Application of Alkyne-Functionalized Discrete Oligomers

The development of synthetic oligomers as discrete single molecular entities with accurate control over the number and nature of functional groups along the backbone has enabled a variety of new research opportunities. From fundamental studies of self-assembly in materials science to understanding efficacy and safety profiles in biology and pharmaceuticals, future directions are significantly impacted by the availability of discrete, multifunctional oligomers. However, the preparation of diverse libraries of discrete and stereospecific oligomers remains a significant challenge. We report a novel strategy for accelerating the synthesis and isolation of discrete oligomers in a high-throughput manner based on click chemistry and simplified bead-based purification. The resulting synthetic platform allows libraries of discrete polyether oligomers to be prepared and the impact of variables such as chain length, number, and nature of side chain functionalities and molecular dispersity on antibacterial behavior examined. Significantly, discrete oligomers were shown to exhibit enhanced activity with lower toxicity compared with traditional disperse samples. This work provides a practical and scalable methodology for nonexperts to prepare libraries of multifunctional discrete oligomers and demonstrates the advantages of discrete materials in biological applications.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Multivalent Fluorinated Nanorings for On-Cell 19F NMR

The design of imaging agents with a high fluorine content is necessary for overcoming the challenges of low sensitivity in 19F magnetic resonance imaging (MRI)-based molecular imaging. Chemically self-assembled nanorings (CSANs) provide a strategy to increase the fluorine content through multivalent display. We previously reported an 19F NMR-based imaging tracer, in which case a CSAN-compatible epidermal growth factor receptor (EGFR)-targeting protein E1-dimeric dihydrofolate (E1-DD) was bioconjugated to a highly fluorinated peptide. Despite good 19F NMR performance in aqueous solutions, a limited signal was observed in cell-based 19F NMR using this monomeric construct, motivating further design. Here, we design several new E1-DD proteins bioconjugated to peptides of different fluorine contents. Flow cytometry analysis was used to assess the effect of variable fluorinated peptide sequences on the cellular binding characteristics. Structure-optimized protein, RTC-3, displayed an optimal spectral performance with high affinity and specificity for EGFR-overexpressing cells. To further improve the fluorine content, we next engineered monomeric RTC-3 into CSAN, η-RTC-3. With an approximate eightfold increase in the fluorine content, multivalent η-RTC-3 maintained high cellular specificity and optimal 19F NMR spectral behavior. Importantly, the first cell-based 19F NMR spectra of η-RTC-3 were obtained bound to EGFR-expressing A431 cells, showing a significant amplification in the signal. This new design illustrated the potential of multivalent fluorinated CSANs for future 19F MRI molecular imaging applications.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

New Insights on Designing the Next-Generation Materials for Electrochemical Synthesis of Reactive Oxidative Species Towards Efficient and Scalable Water Treatment: A Review and Perspectives

Electrochemical water remediation technologies offer several advantages and flexibility for water treatment and degradation of contaminants. These technologies generate reactive oxidative species (ROS) that degrade pollutants. For the implementation of these technologies at an industrial scale, efficient, scalable, and cost-effective in-situ ROS synthesis is necessary to degrade complex pollutant mixtures, treat large amount of contaminated water, and clean water in a reasonable amount of time and cost. These targets are directly dependent on the materials used to generate the ROS, such as electrodes and catalysts. Here, we review the key design aspects of electrocatalytic materials for efficient in-situ ROS generation. We present a mechanistic understanding of ROS generation, including their reaction pathways, and integrate this with the key design considerations of the materials and the overall electrochemical reactor/cell. This involves tunning the interfacial interactions between the electrolyte and electrode which can enhance the ROS generation rate up to ~ 40% as discussed in this review. We also summarized the current and emerging materials for water remediation cells and created a structured dataset of about 500 electrodes and 130 catalysts used for ROS generation and water treatment. A perspective on accelerating the discovery and designing of the next generation electrocatalytic materials is discussed through the application of integrated experimental and computational workflows. Overall, this article provides a comprehensive review and perspectives on designing and discovering materials for ROS synthesis, which are critical not only for successful implementation of electrochemical water remediation technologies but also for other electrochemical applications.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity

Solution-processed inorganic solar cells with less toxic and earth-abundant elements are emerging as viable alternatives to high-performance lead-halide perovskite solar cells. However, the wide range of elements and process parameters impede the rapid exploration of vast chemical spaces. Here, we developed an automated robot-embedded measurement system that performs photoabsorption spectroscopy, optical microscopy, and white-light flash time-resolved microwave conductivity (TRMC). We tested 576 films of quaternary element-blended wide-bandgap Cs-Bi-Sb-I semiconductors with various compositions, organic salt additives (MACl, FACl, MAI, and FAI, where MA and FA represent methylammonium and formamidinium, respectively), and thermal annealing temperatures. Among them, we found that the maximum power conversion efficiency (PCE) was 2.36%, which is significantly higher than the PCE of 0.68% for a reference film without an additive. Machine learning (ML) and statistical analyses revealed significant features and their relationships with TRMC transients, thereby demonstrating the advantages of combining ML and automated experiments for the high-throughput exploration of photovoltaic materials.

Controlled Radical Copolymerization toward Well-Defined Fluoropolymers

In the past 80 years, fluoropolymers have found broad applications in both industrial and academic settings, owing to their unique physicochemical properties. Copolymerizations of fluoroalkene feedstocks present an important avenue to obtain high-performance materials by merging intrinsic attributes of fluorocarbons and great versatility of comonomers. Recently, while massive investigations have disclosed the great potentials of precisely synthesized polymers, researchers have made considerable efforts to approach well-defined fluorinated copolymers. This minireview discusses challenges in controlled radical copolymerizations (CRCPs) of fluoroalkenes and provides a concise perspective on recent progress in CRCPs of fluoroalkenes (e.g., tetrafluoroethylene, chlorotrifluoroethylene, hexafluoropropene, perfluoroalkyl vinyl ethers) with non-fluorinated vinyl comonomers, which have enabled on-demand preparations of various main-chain fluoropolymers with predefined molar masses, low dispersities, as well as regulable chemical compositions and sequences. The synthetic advantages of CRCPs will promote controlled and facile access to customized fluoropolymers for high-tech applications such as batteries, coatings and so on.

Fluoropolymer Nanoparticles Synthesized via Reversible-Deactivation Radical Polymerizations and Their Applications

Fluorinated polymeric nanoparticles (FPNPs) combine unique properties of fluorocarbon and polymeric nanoparticles, which has stimulated massive interest for decades. However, fluoropolymers are not readily available from nature, resulting in synthetic developments to obtain FPNPs via free radical polymerizations. Recently, while increasing cutting-edge directions demand tailored FPNPs, such materials have been difficult to access via conventional approaches. Reversible-deactivation radical polymerizations (RDRPs) are powerful methods to afford well-defined polymers. Researchers have applied RDRPs to the fabrication of FPNPs, enabling the construction of particles with improved complexity in terms of structure, composition, morphology, and functionality. Related examples can be classified into three categories. First, well-defined fluoropolymers synthesized via RDRPs have been utilized as precursors to form FPNPs through self-folding and solution self-assembly. Second, thermally and photoinitiated RDRPs have been explored to realize in situ preparations of FPNPs with varied morphologies via polymerization-induced self-assembly and cross-linking copolymerization. Third, grafting from inorganic nanoparticles has been investigated based on RDRPs. Importantly, those advancements have promoted studies toward promising applications, including magnetic resonance imaging, biomedical delivery, energy storage, adsorption of perfluorinated alkyl substances, photosensitizers, and so on. This Review should present useful knowledge to researchers in polymer science and nanomaterials and inspire innovative ideas for the synthesis and applications of FPNPs.

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime

Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (Tg) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.

The Power of Automation in Polymer Chemistry: Precision Synthesis of Multiblock Copolymers with Block Sequence Control

Machines can revolutionize the field of chemistry and material science, driving the development of new chemistries, increasing productivity, and facilitating reaction scale up. The incorporation of automated systems in the field of polymer chemistry has however proven challenging owing to the demanding reaction conditions, rendering the automation setup complex and costly. There is an imminent need for an automation platform which uses fast and simple polymerization protocols, while providing a high level of control on the structure of macromolecules via precision synthesis. This work combines an oxygen tolerant, room temperature polymerization method with a simple liquid handling robot to automatically prepare precise and high order multiblock copolymers with unprecedented livingness even after many chain extensions. The highest number of blocks synthesized in such a system is reported, demonstrating the capabilities of this automated platform for the rapid synthesis and complex polymer structure formation.

Design of Highly Fluorinated Peptides for Cell-based 19F NMR

The design of imaging agents with high fluorine content is essential for overcoming the challenges associated with signal detection limits in 19F MRI-based molecular imaging. In addition to perfluorocarbon and fluorinated polymers, fluorinated peptides offer an additional strategy for creating sequence-defined 19F magnetic resonance imaging (MRI) imaging agents with a high fluorine signal. Our previously reported unstructured trifluoroacetyllysine-based peptides possessed good physiochemical properties and could be imaged at high magnetic field strength. However, the low detection limit motivated further improvements in the fluorine content of the peptides as well as removal of nonspecific cellular interactions. This research characterizes several new highly fluorinated synthetic peptides composed of highly fluorinated amino acids. 19F NMR analysis of peptides TB-1 and TB-9 led to highly overlapping, intense fluorine resonances and acceptable aqueous solubility. Flow cytometry analysis and fluorescence microscopy further showed nonspecific binding could be removed in the case of TB-9. As a preliminary experiment toward developing molecular imaging agents, a fluorinated EGFR-targeting peptide (KKKFFKK-βA-YHWYGYTPENVI) and an EGFR-targeting protein complex E1-DD bioconjugated to TB-9 were prepared. Both bioconjugates maintained good 19F NMR performance in aqueous solution. While the E1-DD-based imaging agent will require further engineering, the success of cell-based 19F NMR of the EGFR-targeting peptide in A431 cells supports the potential use of fluorinated peptides for molecular imaging.

Applied machine learning as a driver for polymeric biomaterials design

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language

Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models impose significant rigidity making it difficult to accommodate a broad array of experiment and data types found in polymer science. This inflexibility presents a significant barrier for researchers to leverage their historical data in ML development. Here we show that a domain specific language, termed Chemical Markdown Language (CMDL), provides flexible, extensible, and consistent representation of disparate experiment types and polymer structures. CMDL enables seamless use of historical experimental data to fine-tune regression transformer (RT) models for generative molecular design tasks. We demonstrate the utility of this approach through the generation and the experimental validation of catalysts and polymers in the context of ring-opening polymerization-although we provide examples of how CMDL can be more broadly applied to other polymer classes. Critically, we show how the CMDL tuned model preserves key functional groups within the polymer structure, allowing for experimental validation. These results reveal the versatility of CMDL and how it facilitates translation of historical data into meaningful predictive and generative models to produce experimentally actionable output.

Emerging Trends in Machine Learning: A Polymer Perspective

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.

A User's Guide to Machine Learning for Polymeric Biomaterials

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

Ring Repeating Unit: An Upgraded Structure Representation of Linear Condensation Polymers for Property Prediction

Unique structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, monomer and repeating unit (RU) approximations are widely used to represent polymer structures for generating feature descriptors in the modeling of quantitative structure-property relationships (QSPR). However, such conventional structure representations may not uniquely approximate heterochain polymers due to the diversity of monomer combinations and the potential multi-RUs. In this study, the so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity is proposed for the first time. As a proof of concept, an RRU-based QSPR model was developed to predict the associated glass transition temperature (T_g) of polyimides (PIs) with deterministic values. Comprehensive model validations including external, internal, and Y-random validations were performed. Also, an RU-based QSPR model developed based on the same large database of 1321 PIs provides nonunique prediction results, which further prove the necessity of RRU-based structure representation. Promising results obtained by the application of the RRU-based model confirm that the as-developed RRU method provides an effective representation that accurately captures the sequence of repeat units and thus realizes reliable polymer property prediction by data-driven approaches.

Enzyme-Polymer Conjugates for Tuning, Enhancing, and Expanding Biocatalytic Activity

Combining polymers with functional proteins is an approach that has brought several successful stories in the field of biomedicine with PEGylated therapeutic proteins. The latest advances in polymer chemistry have facilitated the expansion of protein-polymer hybrids to other research areas such as biocatalysis. Polymers can impart stability and novel functionalities to the enzyme of interest, thereby improving the catalytic performance of a given reaction. In this review, we have revisited the main methodologies currently used for the synthesis of enzyme-polymer hybrids, unveiling the interplay between the configuration and the composition of the assembled structure and the eventual traits of the hybrid. Finally, the latest advances, such as the assembly of polymer-based chemoenzymatic nanoreactors and the use of deep learning methodologies to achieve the most suitable polymer compositions for catalysis, are discussed.

Efficient design of peptide-binding polymers using active learning approaches

Active learning (AL) has become a subject of active recent research both in industry and academia as an efficient approach for rapid design and discovery of novel chemicals, materials, and polymers. Herein, we have assessed the applicability of AL for the discovery of polymeric micelle formulations for poorly soluble drugs. We were motivated by the key advantages of this approach making it a desirable strategy for rational design of drug delivery systems due toto its ability to (i) employ relatively small datasets for model development, (ii) iterate between model development and model assessment using small external datasets that can be either generated in focused experimental studies or formed from subsets of the initial training data, and (iii) progressively evolve models towards increasingly more reliable predictions and the identification of novel chemicals with the desired properties. In this study, we compared various AL protocols for their effectiveness in finding biologically active molecules using synthetic datasets. We have investigated the dependency of AL performance on the size of the initial training set, the relative complexity of the task, and the choice of the initial training dataset. We found that AL techniques as applied to regression modeling offer no benefits over random search, while AL used for classification tasks performs better than models built for randomly selected training sets but still quite far from perfect. Using the best performing AL protocol,. Finally, the best performing AL approach was employed to discover and experimentally validate novel binding polymers for a case study of asialoglycoprotein receptor (ASGPR).

Organocatalyzed Controlled Radical Copolymerization toward Hybrid Functional Fluoropolymers Driven by Light

Photo-controlled polymerizations are attractive to tailor macromolecules of complex compositions with spatiotemporal regulation. In this work, with a convenient synthesis for trifluorovinyl boronic ester (TFVB), we report a light-driven organocatalyzed copolymerization of vinyl monomers and TFVB for the first time, which enabled the controlled synthesis of a variety of hybrid fluorine/boron polymers with low dispersities and good chain-end fidelity. The good behaviors of "ON/OFF" switch, chain-extension polymerizations and post-modifications further highlight the versatility and reliability of this copolymerization. Furthermore, we demonstrate that the combination of fluorine and boron could furnish copolymer electrolytes of high lithium-ion transference number (up to 0.83), bringing new opportunities of engineering high-performance materials for energy storage purposes.

Unified machine learning protocol for copolymer structure-property predictions

Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer types such as alternating, random, block, and gradient copolymers. We detail steps for necessary software installation and construction of datasets. We further describe training and optimization steps for four neural network models and subsequent model visualization and comparison using training and test values. For complete details on the use and execution of this protocol, please refer to Tao et al. (2022).¹.

Advances and opportunities in nanoimaging agents for the diagnosis of inflammatory lung diseases

The development of rapid, noninvasive diagnostics to detect lung diseases is a great need after the COVID-2019 outbreak. The nanotechnology-based approach has improved imaging and facilitates the early diagnosis of inflammatory lung diseases. The multifunctional properties of nanoprobes enable better spatial-temporal resolution and a high signal-to-noise ratio in imaging. Targeted nanoimaging agents have been used to bind specific tissues in inflammatory lungs for early-stage diagnosis. However, nanobased imaging approaches for inflammatory lung diseases are still in their infancy. This review provides a solution-focused approach to exploring medical imaging technologies and nanoprobes for the detection of inflammatory lung diseases. Prospects for the development of contrast agents for lung disease detection are also discussed.

A graph representation of molecular ensembles for polymer property prediction

Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric materials, like chain architecture, monomer stoichiometry, and degree of polymerization, and achieves superior accuracy to off-the-shelf cheminformatics methodologies. While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches. The dataset and machine learning models presented in this work pave the path toward new classes of algorithms for polymer informatics and, more broadly, introduce a framework for the modeling of molecular ensembles.

A dual initiator approach for oxygen tolerant RAFT polymerization

Reversible-deactivation radical polymerizations are privileged approaches for the synthesis of functional and hybrid materials. A bottleneck for conducting these processes is the need to maintain oxygen free conditions. Herein we report a broadly applicable approach to "polymerize through" oxygen using the synergistic combination of two radical initiators having different rates of homolysis. The in situ monitoring of the concentrations of oxygen and monomer simultaneously provided insight into the function of the two initiators and enabled the identification of conditions to effectively remove dissolved oxygen and control polymerization under open-to-air conditions. By understanding how the surface area to volume ratio of reaction vessels influence open-to-air polymerizations, well-defined polymers were produced using acrylate, styrenic, and methacrylate monomers, which each represent an expansion of scope for the "polymerizing through" oxygen approach. Demonstration of this method in tubular reactors using continuous flow chemistry provided a more complete structure-reactivity understanding of how reaction headspace influences PTO RAFT polymerizations.

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Despite the large prevalence of diseases affecting cartilage (e.g. knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissue caused by low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly-beta-amino-esters (PBAEs) to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine; therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged multivariate adaptive regression splines (MARS) returned the best predictive performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; a further round of screening was carried out on PBAEs (n = 150) with small variations of structure of the main candidates from the first round. The refinements of such characteristics enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment; this uptake improvement was also experimentally confirmed. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.

Machine learning strategies for the structure-property relationship of copolymers

Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...

Fully biobased triblock copolymers generated using an unconventional oscillatory plug flow reactor

Producing block polymers in continuous flow offers significant advantages in terms of versatility, efficiency and scalability.