Machine-Learning-Based Inverse Design for Electrochemically Controlled Microscopic Gradients of O₂ and H₂O₂

A fundamental understanding of the extracellular microenvironments of O₂ and reactive oxygen species (ROS) such as H₂O₂, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O₂ and H₂O₂ at microscopic scale with high spatiotemporal precision. However, there is a paucity for a high-throughput strategy of microenvironment design and it remains challenging to achieve O₂ and H₂O₂ heterogeneities with the microbiologically desirable spatiotemporal resolutions. Here we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O₂ and H₂O₂ profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O₂ and H₂O₂ profiles with spatial resolution of ~10¹ μm and temporal resolution of ~10⁰ sec. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O₂ and H₂O₂ microenvironments while being order-of-magnitude faster. Interfacing ML-based inverse design with electrochemically controlled concentration heterogeneity creates a viable fast-response platform towards better understanding the extracellular space with desirable spatiotemporal control.