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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., (© 2023. The Author(s).)

Biofouling on the surface of implanted medical devices and biosensors severely hinders device functionality and drastically shortens device lifetime. Poly(ethylene glycol) and zwitterionic polymers are currently considered "gold-standard" device coatings to reduce biofouling. To discover novel anti-biofouling materials, a combinatorial library of polyacrylamide-based copolymer hydrogels is created, and their ability is screened to prevent fouling from serum and platelet-rich plasma in a high-throughput parallel assay. It is found that certain nonintuitive copolymer compositions exhibit superior anti-biofouling properties over current gold-standard materials, and machine learning is used to identify key molecular features underpinning their performance. For validation, the surfaces of electrochemical biosensors are coated with hydrogels and their anti-biofouling performance in vitro and in vivo in rodent models is evaluated. The copolymer hydrogels preserve device function and enable continuous measurements of a small-molecule drug in vivo better than gold-standard coatings. The novel methodology described enables the discovery of anti-biofouling materials that can extend the lifetime of real-time in vivo sensing devices., (© 2022 Wiley-VCH GmbH.)

We report a study combining computational design and experimental evaluation of polyimides with high glass transition temperatures: T g between 220 °C and 500 °C. The computational approach is based on the recently introduced competitive learning algorithm, supervised self-organizing maps (SUSI), which we recast as an ensemble method, e-SUSI. We use e-SUSI to solve both unsupervised and supervised/semisupervised learning tasks capturing structure-property relationships of high- T g polyimides historically studied at Almaden Research Center. Predictors trained on historical data were applied to the combinatorial library of novel polyimides and informed selection of the candidates for synthesis and characterization. In this manner, three new polyimides were prepared with T g values 281 °C, 282 °C, and 331 °C. The measured values closely agree with the predicted values 273 °C, 311 °C, and 335 °C, respectively. We discuss specific reasons that make the proposed computational design strategy attractive in rapid, deliverable-driven efforts with limited, small-batch data sets.

The architectural features of cellular life and its ecologies at larger scales are built upon foundational networks of reactions between molecules that avoid a collapse to equilibrium. The search for life's origins is, in some respects, a search for biotic network attributes in abiotic chemical systems. Radiation chemistry has long been employed to model prebiotic reaction networks, and here we report network-level analyses carried out on a compiled database of radiolysis reactions, acquired by the scientific community over decades of research. The resulting network shows robust connections between abundant geochemical reservoirs and the production of carboxylic acids, amino acids, and ribonucleotide precursors-the chemistry of which is predominantly dependent on radicals. Moreover, the network exhibits the following measurable attributes associated with biological systems: (1) the species connectivity histogram exhibits a heterogeneous (heavy-tailed) distribution, (2) overlapping families of closed-loop cycles, and (3) a hierarchical arrangement of chemical species with a bottom-heavy energy-size spectrum. The latter attribute is implicated with stability and entropy production in complex systems, notably in ecology where it is known as a trophic pyramid. Radiolysis is implicated as a driver of abiotic chemical organization and could provide insights about the complex and perhaps radical-dependent mechanisms associated with life's origins.

Chiral pentose sugars mediate the enantioselective synthesis of amino acid precursors, with the magnitude of the chiral induction dictated by a subtle cooperativity between sugar hydroxyl groups. Ribose and lyxose give opposite chiral preferences, and theoretical calculations reveal the pseudoenantiomeric nature of transition state structures from the two sugars. Prebiotically plausible mixtures of natural d-sugars lead to enantioenrichment of natural l-amino acid precursors. Temporal monitoring and kinetic modeling of the reaction reveal an unusual dynamic kinetic resolution that shifts toward an enantioselective pathway over time, providing an amplification mechanism for the transfer of chiral information. This work adds to growing evidence for synergy in the etiology of the single chirality of the two most important classes of biological molecules, the sugars that make up DNA and RNA and the amino acids that form proteins.

It is generally recognized that a distinguishing feature of life is its peculiar capability to avoid equilibration. The origin of this capability and its evolution along the timeline of abiogenesis is not yet understood. We propose to study an analog of this phenomenon that could emerge in non-biological systems. To this end, we introduce the concept of sustainability of transient kinetic regimes. This concept is illustrated via investigation of cooperative effects in an extended system of compartmentalized chemical oscillators under batch and semi-batch conditions. The computational study of a model system shows robust enhancement of lifetimes of the decaying oscillations which translates into the evolution of the survival function of the transient non-equilibrium regime. This model does not rely on any form of replication. Rather, it explores the role of a structured effective environment as a contributor to the system-bath interactions that define non-equilibrium regimes. We implicate the noise produced by the effective environment of a compartmentalized oscillator as the cause of the lifetime extension.

We consider the hypothesis of the primordial nature of the non-enzymatic reverse tricarboxylic acid (rTCA) cycle and describe a modeling approach to quantify the uncertainty of this hypothesis due to the combinatorial aspect of the constituent chemical transformations. Our results suggest that a) rTCA cycle belongs to a degenerate optimum of auto-catalytic cycles, and b) the set of targets for investigations of the origin of the common metabolic core should be significantly extended.