| Abstract Scope |
Nature realizes extraordinary material properties through the hierarchical organization of polymers from the molecular to the macroscopic scale. Synthetically recapitulating this level of control has been a long-standing challenge as it requires mastery of each scale and an understanding of how to piece these levels together. Practically, however, there are too many distinct material compositions and processing conditions to test using conventional hypothesis-driven research. Thus, new experimental paradigms are needed. Here, we describe our recent progress using advances in machine learning and automated research systems to study hierarchically structured polymers. In particular, we discuss the degree to which experimental research can be accelerated through the combination of automated experimental systems and machine learning to choose experiments. To explore the merits of such autonomous experimental systems, and discover novel mechanical metamaterials, we present a Bayesian experimental autonomous researcher (BEAR) that combines additive manufacturing, robotics, and mechanical characterization to rapidly construct, test, and, study mechanical structures. Using this platform, we study the elastic and plastic mechanics of polymer structures. Critically, we find that the use of a BEAR enables us to discover high performance structures in 60 times fewer experiments than grid-based experimentation. In addition to rapidly developing an understanding of a family of mechanical structures, these experiments provide important lessons regarding how machine learning and automation can accelerate experimental research and mechanical design. Finally, we describe recent efforts to adopt this autonomous research framework at the nanoscopic scale using scanning probes to create and interrogate libraries of polymers. Ultimately, understanding and leveraging the hierarchical arrangements of materials is a grand challenge. Autonomous research systems that span additive manufacturing, machine learning, and advanced characterization have the potential for transformatively advancing the pace of research to meet this challenge. |