Heinrich Jaeger, Ph.D.Physics, University of Chicago
Design is a process that proceeds from desired overall properties to requirements for the constituent components. For materials science, design is a major challenge, because it requires us to invert the typical modeling approach that starts from microscale components in order to predict macroscale behavior. How can one tackle this inverse problem for granular materials that are inherently disordered and far from equilibrium, and for which the design target is not a thermodynamically favored ‘ground state’? I will discuss how concepts from artificial evolution make it possible to identify with high efficiency particle-scale parameters that produce the targeted macroscale behavior. In particular, I will show how one can find particle shapes that are optimized for specific desired outcomes, such as low aggregate porosity or high stiffness under compression. This approach uses large numbers of parallel molecular dynamics simulations together with optimization techniques based on artificial evolution. Optimized shapes are then validated by physical measurements that test large aggregates of 3D-printed versions of the particles. This approach has general applicability and opens up new opportunities for granular materials design as well as discovery.
Heinrich Jaeger is the William J. Friedman and Alicia Townsend Professor of Physics at the University of Chicago. He received his Ph.D. in physics in 1987 from the University of Minnesota and has been on the faculty at the University of Chicago since 1991, directing the Chicago Materials Research Center from 2001 – 2006, and the James Franck Institute from 2007-2010. Jaeger’s current research focuses on self-assembled nanoparticle-based structures, on the rheology of concentrated particle suspensions, and on studies of the packing and flow properties of dry granular materials.
Host: Dr. Sushant Anand
For more information, please contact Prof. Sushant Anand