Machine learning–based inverse design for electrochemically controlled microscopic gradients of O(2) and H(2)O(2)

A fundamental understanding of extracellular microenvironments of O(2) and reactive oxygen species (ROS) such as H(2)O(2), ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O(2) and H(2)O(2) at microscopic scale with high spatiotemporal precision. However, there is a paucity of high-throughput strategies of microenvironment design, and it remains challenging to achieve O(2) and H(2)O(2) heterogeneities with microbiologically desirable spatiotemporal resolutions. Here, we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O(2) and H(2)O(2) profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O(2) and H(2)O(2) profiles with spatial resolution of ∼10(1) μm and temporal resolution of ∼10° s. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O(2) and H(2)O(2) microenvironments while being two orders of magnitude faster than experimental explorations. Interfacing ML-based inverse design with electrochemically controlled concentration heterogeneity creates a viable fast-response platform toward better understanding the extracellular space with desirable spatiotemporal control.