Engineering the Phase Behavior of Anisotropic Particles By Inverse Design

Anisotropic particles demonstrate a rich set of phase transitions through variation in temperature, pressure, solvent quality, and particle properties. Recent developments in anisotropic particle synthesis have shown promise for using these particles as building blocks for reconfigurable materials. However, due to the large design space, it can be challenging to find appropriate building blocks for targeted behaviors. Here, instead of mapping out the phase behavior for different types of particles, we use the inverse method of Digital Alchemy to find optimal particle shapes for targeted phase behaviors. We present a theoretical design framework along with computer simulation results that give candidate particles for reconfigurable colloidal materials.