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Designing an efficient multi-agent hardware system to solve large-scale computational problems through high-parallelism processing with nonlinear interactions remains a challenge. Here, the authors demonstrate that a multi-agent hardware system deploying distributed Ag nanoclusters as physical agents enables parallel, complex computing.

The potential of 2D materials for biosensing applications is often limited by large device-to-device variation. Here, the authors report a calibration method and a machine learning approach leveraging the redundancy of a sensing platform based on 256 integrated graphene transistors to enhance the system accuracy in real-time ion classification.