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.

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.

Structural defects govern various physical, chemical, and optoelectronic properties of two-dimensional transition-metal dichalcogenides (TMDs). A fundamental understanding of the spatial distribution and dynamics of defects in these low-dimensional systems is critical for advances in nanotechnology. However, such understanding has remained elusive primarily due to the inaccessibility of (a) necessary time scales via standard atomistic simulations and (b) required spatiotemporal resolution in experiments. Here, we take advantage of supervised machine learning, in situ high-resolution transmission electron microscopy (HRTEM) and molecular dynamics (MD) simulations to overcome these limitations. We combine genetic algorithms (GA) with MD to investigate the extended structure of point defects, their dynamical evolution, and their role in inducing the phase transition between the semiconducting (2H) and metallic (1T) phase in monolayer MoS. GA-based structural optimization is used to identify the long-range structure of randomly distributed point defects (sulfur vacancies) for various defect densities. Regardless of the density, we find that organization of sulfur vacancies into extended lines is the most energetically favorable. HRTEM validates these findings and suggests a phase transformation from the 2H-to-1T phase that is localized near these extended defects when exposed to high electron beam doses. MD simulations elucidate the molecular mechanism driving the onset of the 2H to 1T transformation and indicate that finite amounts of 1T phase can be retained by increasing the defect concentration and temperature. This work significantly advances the current understanding of defect structure/evolution and structural transitions in 2D TMDs, which is crucial for designing nanoscale devices with desired functionality.

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.

Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecularscale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.

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

No abstract

In this work, we study the influence of polymer chain length (m), based on Lennard-Jones potential, and nanoparticle (NP)-polymer interaction strength (ɛnp) on aggregation and dispersion of soft repulsive spherically structured NPs in polymer melt using coarse-grain molecular dynamics simulations. A phase diagram is proposed where transitions between different structures in the NP-polymer system are shown to depend on m and ɛnp. At a very weak interaction strength ɛnp = 0.1, a transition from dispersed state to collapsed state of NPs is found with increasing m, due to the polymer's excluded volume effect. NPs are well dispersed at intermediate interaction strengths (0.5 ⩽ ɛnp ⩽ 2.0), independent of m. A transition from dispersion to agglomeration of NPs, at a moderately high NP-polymer interaction strength ɛnp = 5.0, for m = 1-30, is identified by a significant decrease in the second virial coefficient, excess entropy, and potential energy, and a sharp increase in the Kirkwood-Buff integral. We also find that NPs undergo the following transitions with increasing m at ɛnp ⩾ 5.0: string-like → branch-like → sphere-like → dispersed state.