Advancing microfluidic design with machine learning: a Bayesian optimization approach

Microfluidic technology, which involves the manipulation of fluids in microchannels, faces challenges in channel design and performance optimization due to its complex, multi-parameter nature. Traditional design and optimization approaches usually rely on time-consuming numerical simulations, or on trial-and-error methods, which entail high costs associated with experimental evaluations. Additionally, commonly used optimization methods require many numerical simulations, and to avoid excessive computation time, they approximate simulation results with faster surrogate models. Alternatively, machine learning (ML) is becoming increasingly significant in microfluidics and technology in general, enabling advancements in data analysis, automation, and system optimization. Among ML methods, Bayesian optimization (BO) stands out by systematically exploring the design space, usually using Gaussian processes (GP) to model the objective function and guide the search for optimal designs. In this paper, we demonstrate the application of BO in the design optimization of the microfluidic systems, by enhancing the mixing performance of a micromixer with parallelogram barriers and a Tesla micromixer modified with parallelogram barriers. Micromixer models were made using Comsol Multiphysics software® and their geometric parameters were optimized using BO. The presented approach minimizes the number of required simulations to reach the optimal design, thus eliminating the need for developing a separate surrogate model for approximation of the simulation results. The results showed the effectiveness of using BO for design optimization, both in terms of the execution speed and reaching the optimum of the objective function. The optimal geometries for efficient mixing were achieved at least an order of magnitude faster compared to state-of-the-art optimization methods for microfluidic design. In addition, the presented approach can be widely applied to other microfluidic devices, such as droplet generators, particle separators, etc.