Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification.

Identifying the state of the colloidal self-assembly process is critical to monitoring and controlling the system into desired configurations. Recent application of convolutional neural networks with unsupervised clustering has shown a comparable performance to conventional approaches, in representing and classifying the states of a simulated 2D colloidal batch assembly system. Despite the early success, capturing the subtle differences among similar configurations still presents a challenge. To address this issue, we leverage a Siamese neural network to improve the accuracy of the state classification. Results from a Brownian dynamics-simulated electric field-mediated colloidal self-assembly system and a magnetic field-mediated colloidal self-assembly system demonstrate significant improvement from the original convolutional neural network-based approach. We anticipate the proposed improvement to further pave the way for automated monitoring and control of colloidal self-assembly processes in real time and real space.