Matter
ReviewIntelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine
Progress and potential
The synergistic convergence of microfluidics and machine learning enables ample and varied applications. Microfluidic chips are economical devices capable of extracting large amounts of data with minimal reagent consumption. Meanwhile, machine learning offers computational tools with the ability to learn from data and make accurate predictions to guide and optimize scientific research. Currently, microfluidic devices are mostly operated manually and data are analyzed post experiment. The development and integration of on-chip multimodal instrumentation will give rise to the next generation of autonomous platforms operated via data-driven models. This review summarizes progress on the monitoring and control of microfluidic systems via machine learning, discusses advances on the implementation of microfluidics and machine learning in materials science and biomedicine, and identifies challenges and opportunities for research in the emerging field of intelligent microfluidics.