Image-based CNN-DPD model for critical diameter prediction in DLD devices

Deterministic Lateral Displacement (DLD) is a microfluidic technology that separates particles based on size and deformability, with separation efficiency closely tied to the critical diameter. In recent years, machine learning (ML) has emerged as a powerful tool for predicting the critical diameter, offering a promising alternative to time-consuming simulations and experiments. However, traditional regression ML models relying on control parameters often struggle to achieve high prediction accuracy, particularly when dealing with asymmetric shapes, due to their limited ability to capture geometric intricacies. To overcome these constraints, an image-based method for predicting the critical diameter is proposed in present study, integrating Convolutional Neural Networks (CNN) with Dissipative Particle Dynamics (DPD). This CNN-DPD approach demonstrates superior prediction performance compared to conventional regression models and remains effective even when trained on small datasets. Specifically, it achieves the same prediction accuracy comparable to that of traditional models trained on 3000 samples, while requiring only 300 samples. Building upon this, a CNN-based framework for optimizing the DLD pillar shape is introduced. Using this framework, it is found that an asymmetric shape along the x-axis outperforms other configurations, and the mechanism by which it reduces the critical diameter – by modulating the peak shift of inter-pillar flow velocity – is elucidated through DPD simulations.