Physics-Guided Surrogate Modeling for Machine Learning-Driven DLD Design Optimization

Abstract: Sorting cells based on their mechanical properties is essential for applications in disease diagnostics, cell therapy, and biomedical research. Deterministic Lateral Displacement (DLD) devices provide a label-free method for achieving such sorting, but their performance is highly sensitive to cell size and deformability. Designing effective DLD geometries often demands extensive trial-and-error experimentation, as even small variations in cellular mechanical traits can cause significant changes in migration behavior. To address this challenge, we propose a simulation-driven ML framework that predicts suitable DLD design candidates for a given cell type. Our approach integrates high-fidelity particle-based simulations to model cell deformation and migration through microfluidic pillar arrays with supervised ML models trained to estimate optimal geometries. By mapping mechanical parameters such as bending rigidity and shear modulus to deformation index and migration angle, the framework enables rapid, data-informed design of DLD systems. We also demonstrate a deployable web interface to make this tool accessible for real-world device prototyping.