Nanostructures and the manufacturing of them are being researched for applications in areas such as controlled drug release, bio-sensors, solar cells, and data storage. The nanostructure known as the gyroid is particularly promising for application in these areas because of the continuous, fully connected channels that spiral through it periodically and uniformly. The gyroid can be manufactured through self-assembling block-copolymers or surfactants, but the mechanism for the assembly is not well understood. A better understanding of the parameters that control the formation of this structure will tell us how we can better control the formation of the gyroid structure. Our goal is to use molecular dynamics simulations to find the mechanism behind the formation of the gyroid in a model system. We were able to simulate its spontaneous formation and tested the efficiency of a number of order parameters, something that can distinguish between the gyroid and the surrounding mixture. As the gyroid forms, it passes through a transition state: the point where it has a 50% probability of forming or melting. The complexity of the gyroid structure makes it difficult to find an order parameter that can capture the structure at the transition state. However, by using committor analysis methods we were able to identify the transition state. We developed several order parameters that can distinguish gyroid from the isotropic mixture and, using Aimless Shooting and Maximum Likelihood Optimization, we ranked these parameters according to their effectiveness.
