| Abstract Scope |
The core tenet of statistical mechanics is that the frequency of microstates for a material system can be used to predict its macroscopic properties. What if it were possible to turn this relationship around and use it directly for materials design? That is, instead of predicting macroscopic properties, could we engineer them by exploiting the rich information encoded in micro-states and their fluctuations? In this talk, I present a new approach that can be used to transform a statistical physics model that describes a material into a materials design algorithm that tailors it. Because the resulting algorithm is built with a physical model as its foundation, it inherits the ability to exploit micro-state information in guiding an optimization. I’ll show this extra information leads to benefits over black-box optimization methods in terms runtime, efficiency, and robustness. In particular, I’ll show examples of material optimization with this new approach, including optimal self-assembly, non-equilibrium optimization, and a real-world application on the directed self-assembly of diblock copolymers. |