Baocheng Wang, Depeng Kong, Zhiao He, Jikai Liang, Yuyao Lu, Zikang Deng, Honghe Li, Mengke Wang, M. Jamal Deen, Zhiqiu Ye, Shuyao Zhou, Huayong Yang, Honghao Lyu, jun chen, Kaichen Xu, Geng Yang
Machine Learning-Driven Inverse Design In their Research Article (DOI: 10.1002/advs.202524250), Honghao Lyu, Geng Yang, and co-workers introduce a machine learning-accelerated inverse design methodology to automatically customize high-performance tactile sensors. By synergizing support vector machines with active learning, a data-efficient predictor rapidly navigates complex microstructure-property relationships. This real-time, on-demand framework delivers sensors with exceptional sensitivity, linearity, and range, establishing a powerful new paradigm for next-generation robotic sensing systems.