Artificial Intelligence for Bioinspired Nanofluidic Iontronics

Bioinspired nanofluidic iontronics is emerging as a pivotal technology for next-generation biosensing and neuromorphic computing. Although constrained by nanoscale perturbations and fabrication heterogeneity, artificial intelligence (AI) effectively mitigates inherent signal and manufacturing bottlenecks, driving a paradigm shift toward bidirectional empowerment. In this Mini-Review, we summarize recent advances in the convergence of AI and iontronics, focusing on the core scientific conflict between algorithmic robustness and nanoscale physical stochasticity. We analyze how this interaction reshapes three key dimensions of the research workflow: (1) AI-enabled characterization for feature extraction and mechanistic analysis amidst noise; (2) AI-driven fabrication via surrogate models and inverse structural design to bridge the design-to-fabrication gap; and (3) AI-expanded applications in intelligent biosensing and physical neuromorphic computing. Finally, we propose a synergistic roadmap: managing physical imperfections via manufacturing-aware models while shifting AI from merely circumventing errors to architectures that actively exploit intrinsic stochasticity.