When we think of materials “by design”, we are envisioning a process that gets us from a design target, namely certain desired overall materials properties, to requirements for the constituent components. This is challenging because it requires us to invert the typical modeling approach in physics and material science, which 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 target is not a thermodynamically favored ‘ground state’? I will discuss how concepts from artificial evolution make it possible to find with high efficiency particle-scale parameters best adapted to given target properties. 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.
|Location: Technical University of Catalonia (UPC), Vertex Building.|
|Date: 28 - 30 September 2015, Barcelona, Spain.|