Nature realizes extraordinary material properties through the hierarchical organization of polymers from the molecular to the macroscopic scales. 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. Further, both the material property considerations at the nanoscopic scales and the macroscopic design considerations are problems with massive complexity that present too many potential solutions to evaluate in sufficient depth. In this talk, we describe our recent progress addressing the challenge presented by hierarchically structured polymers. At a high level, we pursue two complementary approaches including (1) the development of experimental systems to accelerate the pace
and value of experiments and (2) explorations of particularly fascinating systems in which the structure and properties are coupled. Initially, we discuss autonomous research systems, or automated experimental platforms in which experiments are chosen by machine learning to provide the most useful information. To explore the merits of such autonomous experimental systems, and study the mechanics of macroscopically structured polymers, we present a Bayesian experimental autonomous researcher that combines additive manufacturing, robotics, and mechanical characterization to rapidly print, test, and study mechanical structures. Using this platform, we study the elastic and plastic mechanics of polymer structures. In addition to 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. Next, we discuss our efforts to understand size effects in nanoscale polymers through an approach that combines finite element analysis and nanoindentation. We find that elastomeric thin films are stiffer than bulk samples in a manner that agrees with a newly proposed surface crosslinking model. While this type of in depth characterization is needed to tease apart
the connections between structure and mechanical properties, there are innumerable potential materials and processing conditions to consider. Thus, we conclude by discussing progress towards combining the study of mesoscale polymers with an autonomous research framework through the use of scanning probes to create and interrogate nanoscale libraries of polymers. Ultimately, understanding and leveraging the hierarchal 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.
Dr. Keith A. Brown is an Assistant Professor of Mechanical Engineering, Materials Science & Engineering, and Physics at Boston University. He earned a Ph.D. in Applied Physics at Harvard University under the guidance of Robert M. Westervelt and an S.B. in physics from MIT. Following his doctoral work, he was an International Institute for Nanotechnology postdoctoral fellow with Chad A. Mirkin at Northwestern University. The Brown group studies polymers and smart fluids to determine how
useful properties emerge from hierarchical structure. A considerable focus is developing approaches that increase the throughput of materials research using scanning probe lithography, machine learning, additive manufacturing, and combinatorial chemistry. Keith has co-authored 71 peer-reviewed publications, five issued patents, and his work has been recognized through awards including the Frontiers of Materials Award from The Minerals, Metals, & Materials Society (TMS), being recognized
as a “Future Star of the AVS,” the Omar Farha Award for Research Leadership from Northwestern University, and the AVS Nanometer-Scale Science and Technology Division Postdoctoral Award. Keith served on the Nano Letters Early Career Advisory Board and has organized symposia at the AVS International Symposium and at the MRS Fall Meeting.