Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach

Deterministic Lateral Displacement (DLD) has been widely utilized for the high-throughput and efficient separation of microspheres, cells, exosomes, and proteins, playing a crucial role in size-based particle separation. The high performance of DLD devices in various tasks relies on optimal design. However, current DLD design lacks clear guidance and heavily depends on expert experience, requiring extensive repetitive microfluidic experiments and numerical simulations. This leads to significant economic and time costs, while the complexity of instruments and algorithms further raises the barrier for DLD design and optimization. To address this challenge, this paper proposes a novel integrated approach that combines Dissipative Particle Dynamics (DPD) simulation with various machine learning (ML) models to rapidly predict the critical diameter of DLD devices with arbitrary pillar shapes considering fluid dynamics. The study uses control parameters of Bézier curves to represent the arbitrary pillar shapes, along with the forces in the x and y directions of the fluid as input parameters. The critical diameter serves as output parameter. Four ML models are trained: Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Artificial Neural Networks (ANN). To address the low interpretability of complex ML models, the Shapley Additive Explanations (SHAP) method is introduced to clarify all input features. The results demonstrate that ML techniques are highly effective in predicting the critical diameter within DLD devices, with the ANN model achieving superior performance, attaining an [Formula: see text] value of 0.949. The SHAP analysis reinforces established fluid dynamics principles. Additionally, it reveals the significant influence of asymmetrical pillars on critical diameter.