Compatibilizerssurfactant molecules designed to improve the stability of an interfaceare employed to enhance material properties in settings ranging from emulsions to polymer blends. A major compatibilization strategy employs block or random copolymers composed of distinct repeat units with preferential affinity for each of the two phases forming the interface. Here we pose the question of whether improved compatibilization could be achieved by employing new synthetic strategies to realize copolymer compatibilizers with specific monomeric sequence. We employ a novel molecular-dynamics-simulation-based genetic algorithm to design model sequence-specific copolymers that minimize energy of a polymer/polymer interface. Results indicate that sequence-specific copolymers offer the potential to yield larger reductions in interfacial energy than either block or random copolymers, with the preferred sequence being compatibilizer concentration dependent. By employing a simple thermodynamic scaling model for copolymer compatibilization, we pinpoint the origins of this sequence specificity and concentration dependence in the “loop entropy” of compatibilizer segments connecting interfacial bridge points. In addition to pointing toward a new strategy for improved interfacial compatibilization, this approach provides a conceptual basis for the computational design of a new generation of sequence-specific polymers leveraging recent and ongoing synthetic advances in this area.

Near-interface alterations in dynamics and glass formation behavior have been the subject of extensive study for the past two decades, both because of their practical importance and in the hope of revealing underlying correlation lengths underpinning glass transition more generally. Here we employ molecular dynamics simulations of thick films to demonstrate that these effects emerge, for segmental-scale translational dynamics at low temperature, from a temperature-independent rescaling of the local activation barrier. This rescaling manifests as a fractional power law decoupling relationship of local dynamics relative to the bulk, with a transition from a regime of weak decoupling at high temperatures to a regime of strong decoupling at low temperatures. The range of this effect saturates at low temperatures, with 90% of the surface perturbation in the barrier lost over a range of 12 segmental diameters. These findings reduce the phenomenology of T g nanoconfinement effects to two propertiesa position-dependent, temperature independent, barrier rescaling factor and an onset time scalewhile substantially constraining the predictions required from any theoretical explanation of this phenomenon.

Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

Polymeric ionic liquids (PILs) are of considerable interest as next-generation battery materials due to their potential to combine the solid-state stability of polymers with the high ion conductivities of ionic liquids. However, polymerization of ionic liquids to form a polymer generally leads to a suppression in ion transport rates that has proven to be a major barrier to the realization of commercially viable PIL solid electrolytes. Here we employ a combination of all atom and coarse-grained molecular dynamics simulations to identify strategies by which ion conductivity can be maximized by maximizing both PIL segmental relaxation rates and the extent of ion transport decoupling from chain dynamics. Results indicate that combined ion size correlates well with PIL glass transition temperatures and segmental dynamics but that ion/polymer decoupling is controlled primarily by the size of the free ion. We also find that ion aggregation promotes both reduced glass transition temperatures and enhanced ion/polymer decoupling. These results suggest that PIL ion mobility can be improved by combining ultralarge bound ions with very small free ions and with chemistries that promote ion aggregation.

Coronaviruses (CoVs) consist of six strains, and the severe acute respiratory syndrome coronavirus (SARS-CoV), newly found coronavirus (SARS-CoV-2) has rapidly spread leading to a global outbreak. The ferret ( Mustela putorius furo) serves as a useful animal model for studying SARS-CoV/SARS-CoV-2 infection and developing therapeutic strategies. A holistic approach for distinguishing differences in gene signatures during disease progression is lacking. The present study discovered gene expression profiles of short-term (3 days) and long-term (14 days) ferret models after SARS-CoV/SARS-CoV-2 infection using a bioinformatics approach. Through Gene Ontology (GO) and MetaCore analyses, we found that the development of stemness signaling was related to short-term SARS-CoV/SARS-CoV-2 infection. In contrast, pathways involving extracellular matrix and immune responses were associated with long-term SARS-CoV/SARS-CoV-2 infection. Some highly expressed genes in both short- and long-term models played a crucial role in the progression of SARS-CoV/SARS-CoV-2 infection, including DPP4, BMP2, NFIA, AXIN2, DAAM1, ZNF608, ME1, MGLL, LGR4, ABHD6 , and ACADM. Meanwhile, we revealed that metabolic, glucocorticoid, and reactive oxygen species-associated networks were enriched in both short- and long-term infection models. The present study showed alterations in gene expressions from short-term to long-term SARS-CoV/SARS-CoV-2 infection. The current result provides an explanation of the pathophysiology for post-infectious sequelae and potential targets for treatment.

High-throughput simulations reveal a universal onset of particle localization in diverse glass-forming liquids.

It has been known for 50 years that polymers exhibit chain normal mode decoupling upon approach to the glass transition, with chain dynamics exhibiting a weaker temperature dependence than segmental dynamics. Inspired by Sokolov and Schweizer’s suggestion that this thermorheological complexity is a consequence of dynamic heterogeneity in the supercooled state, we generalize the Rouse model to account for a distribution of segmental mobilities. The heterogeneous Rouse model (HRM) predicts chain translational normal mode decoupling as a manifestation of diffusion/relaxation decoupling (Stokes–Einstein breakdown)a consequence of differences in how normal modes average over a distribution of segmental mobilities. Molecular dynamics simulations agree with theoretical predictions, with the HRM found to quantitatively predict deviations from Rouse scaling of the translational friction coefficient based on the observed degree of Stokes–Einstein breakdown.

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