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

TL;DR

This paper presents a simulation-driven machine learning framework that predicts optimal DLD device geometries for cell sorting based on mechanical properties, reducing trial-and-error in device design.

Contribution

It introduces an integrated approach combining high-fidelity simulations with supervised ML models to efficiently design DLD devices tailored to specific cell types.

Findings

Framework accurately predicts DLD geometries for different cell mechanical properties.

The ML model reduces design time compared to traditional trial-and-error methods.

A web interface facilitates practical deployment of the design tool.

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 machine learning (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.