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<p>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 MnO<sub>2</sub> on gold conductors enables a stretchable Zn||MnO<sub>2</sub> 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月19日

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  • Haochen Yang,
  • Qiongyu Chen,
  • Tianle Chen,
  • Yang Li,
  • Elizabeth A. Norris,
  • Joshua M. Little,
  • Jiayue Sun,
  • Snehi Shrestha,
  • Edison Chen,
  • Shenqiang Ren,
  • Teng Li,
  • Po-Yen Chen

摘要

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.