Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine learning-integrated workflow

Innovations in wearable electronics and soft robotics hinge significantly on the development of stretchable electrodes. However, a persistent challenge lies in balancing high stretchability, functional performance, and strain insensitivity. Conventional approaches, such as design of experiments and trial-and-error methods, often rely on time-consuming and labor-intensive experiments to navigate a vast and complex parameter space. To overcome this, we establish an integrated workflow merging robot-automated experimentation, machine learning predictions, and finite element simulations to enable the predictive design of stretchable electrodes with strain-insensitive performance. Initially, we construct an ensemble of artificial neural networks through a two-stage workflow, including feasible parameter space definition and active learning loops. Leveraging the prediction model and two-scale simulations, a microtextured stretchable nanocomposite is discovered as a strain-stable platform. Conformal deposition of a thin gold layer showcases metal-like conductivity, high resistance-insensitive stretchability, and robust durability. Furthermore, electrodeposition of Zn and MnO2 on gold conductors enables a stretchable Zn||MnO2 battery, exhibiting large elongation and strain-insensitive electrochemical performance. This machine intelligence-driven approach expedites the multi-parameter optimization of stretchable electrodes, achieving strain-invariant functionalities. Stretchable electrodes are essential for wearable electronics and soft robots, yet their performance always degrades under strain. Here, authors use robotics- and machine learning integrated workflow to design microtextured nanocomposites for strain-insensitive gold conductors and zinc batteries.