Abstract:
The development of advanced materials is extremely slow considering the pressing needs presented by societal challenges. A major reason for this pace is the vastness of possible compositions/processing conditions that must be navigated as part of the discovery/design/development process, which motivates the development of new approaches to accelerate the materials development pipeline. Our work focuses on the use of self-driving labs (SDL), or the combination of automation to perform experiments without human intervention and machine learning to select experiments that best progress toward a user-defined goal. In this talk, we overview our progress applying self-driving labs towards the development of materials that absorb mechanical energy. In particular, materials and architectures that are tough are ubiquitous as protective equipment and structural elements. If such materials could be engineered to absorb more energy per unit weight or volume while remaining easy to produce out of sustainable materials, they could find fruitful application in a number of fields. To explore this, we develop a SDL that combines additive manufacturing and mechanical testing and explore the toughness of 3D printed components. First, we use this platform to benchmark the acceleration provided through the use of SDL and find that this process allows us to identify high performing structures ~60 times faster than grid-based searching, which comprised the first experimental benchmarking of SDLs. Subsequently, we incorporated finite element analysis (FEA) into this SDL to search in a physics-aware fashion and observe further acceleration. Finally, we describe an extensive experimental campaign in which we find structures composed of biodegradable polymers that obtain superlative energy absorbing efficiency. Taking the lessons learned during this campaign, we discuss a pedagogical framework developed to help others use SDLs. Finally, we discuss ongoing efforts to transition this SDL into a community resource by sharing data and enabling external users to explore new systems such as the viscous thread instability.

Bio:
Keith A. Brown is an Associate Professor of Mechanical Engineering, Materials Science & Engineering, and Physics at Boston University. He is also the Associate Chair for Graduate Programs in Mechanical Engineering. He earned an S.B. in Physics from MIT, a Ph.D. in Applied Physics at Harvard University with Robert M. Westervelt, and was an International Institute for Nanotechnology postdoctoral fellow with Chad A. Mirkin at Northwestern University. The KABlab studies approaches to accelerate the development of advanced materials and structures with a focus on polymers. The group employs self-driving labs, additive manufacturing, miniaturization of experiments using scanning probe techniques, and novel platforms for parallel materials development to achieve these goals. Keith has co-authored over 100 peer-reviewed publications, six issued patents, and his work has been recognized through awards including the Frontiers of Materials Award from The Minerals, Metals, & Materials Society (TMS), a Google Faculty Research Award, 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, co-organized a National Academies of Science, Engineering, and Medicine Workshop on AI for Scientific Discovery, and currently leads the MRS Artificial Intelligence in Materials Development Staging Task Force.

In-Person Location:  Bldg. 440, A105/A106

Virtual Link: https://argonne.zoomgov.com/j/1600052763