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.

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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.

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

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