Inverse Design of Quasicrystals and Complex Nanoparticle Architectures formed at Interface of Two Mediums through ML Approaches.

Nanoparticle assemblies have attracted considerable interest due to their various industrial applications, which motivate the discovery of untraditional complex nanoparticle architectures. In this work, we utilize an active learning framework for the inverse design of the system parameters which determine nanoparticle assembly at the interface between mutually immiscible fluids, as it is not computationally feasible to check all parameter combinations by a brute force approach. To enable the active learning framework, we propose a new, unsupervised method of diffraction pattern analysis based on signal processing techniques.