Deep-Learning to Analyse Self-Assembly Processes

Abstract

This report presents the design process of a project aimed at the automatic recognition of 3D structures formed by magnetic spheres in a turbulent water-filled cylinder. This field of research holds promise for future technologies, as macroscopic self-assembly might be the key to three-dimensional storage on chips. With the macroscopic setup, the microscopic self-assembly process is imitated. To automatically recognise a 3D structure, this report is divided into three subgroups that research the optimal test setup, develop an image processing program and create a deep learning model. A labelled result of the 3D formation will be outputted, all while limiting the data size, computation time and inaccuracies. The subgroup responsible for the setup and the underlying physics produces images of the magnetic spheres forming a structure in the test setup. The Image Processing subgroup extracts the properties of the spheres from the image. Finally, the subteam for deep learning, in combination with data management, gives the extracted properties as input to a neural network model, which determines the structure of the spheres. Each submodule has demonstrated successful functionality on its own. However, due to time constraints, a fully integrated system with high accuracy has not been achieved yet. Future work will involve expanding the dataset to enhance the robustness of the recognition algorithms.