Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification
Document Type
Article
Publication Date
11-28-2024
Abstract
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.
Publication Source (Journal or Book title)
Journal of Chemical Physics
Recommended Citation
Lizano-Villalobos, A., Namikas, B., & Tang, X. (2024). Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification. Journal of Chemical Physics, 161 (20) https://doi.org/10.1063/5.0244337