Data- driven inverse design of flexible pressure sensors

• 成果类型: Article
• 署名作者: Liu, Zhiguang; Cai, Minkun; Hong, Shenda; Shi, Junli; Xie, Sai; Liu, Chang; Du, Huifeng; Morin, James D.; Li, Gang; Wang, Liu; Wang, Hong; Tang, Ke; Fang, Nicholas X.; Guo, Chuan Fei
• 署名单位: Southern University of Science & Technology; Massachusetts Institute of Technology (MIT); Chinese Academy of Sciences; University of Science & Technology of China, CAS; Peking University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Southern University of Science & Technology; University of Hong Kong
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13249
• DOI: 10.1073/pnas.2320222121
• 发表日期: 2024-07-09
• 关键词: electronic skin; linear-response

摘要

Artificial skins or flexible pressure sensors that mimic human cutaneous mechanoreceptors transduce tactile stimuli to quantitative electrical signals. Conventional trial- and- error designs for such devices follow a forward structure- to- property routine, which is usually time- consuming and determines one possible solution in one run. Data- driven inverse design can precisely target desired functions while showing far higher productivity, however, it is still absent for flexible pressure sensors because of the difficulties in acquiring a large amount of data. Here, we report a property- to- structure inverse design of flexible pressure sensors, exhibiting a significantly greater efficiency than the conventional routine. We use a reduced- order model that analytically constrains the design scope and an iterative jumping- selection method together with a surrogate model that enhances data screening. As an exemplary scenario, hundreds of solutions that overcome the intrinsic signal saturation have been predicted by the inverse method, validating for a variety of material systems. The success in property design on multiple indicators demonstrates that the proposed inverse design is an efficient and powerful tool to target multifarious applications of flexible pressure sensors, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces.