Discovery of Colloidal Assembly Pathways via Manifold Learning of Energy Landscapes

Engineering micro/nanoscale structures through self-assembly of colloidal building blocks is a powerful approach in material design and processing. However, the complexity of these building blocks makes it hard to predict the resulting self-assembled structures and their transport properties. Therefore, developing predictive tools based on computational and machine learning approaches becomes a necessity for successful implementation of new material design procedures.

In this study, we establish a Manifold Learning-based approach to investigate the self-assembly process of colloidal building blocks by transforming the high-dimensional energy landscapes of colloidal particles into low-dimensional parameter spaces. This illustration of the phase space, termed the ξ space, compresses the full energy profile of the system into a single snapshot that contains important information regarding the assembly process and allows the comparison of the behavior of constructs, with different geometric features, in the same abstract space. This approach also facilitates the discovery of intuitive self-assembly pathways based on the energy footprint of colloidal particles, and may pave the way for developing new tools for material discovery and identification.