Data- driven inverse design of flexible pressure sensors

Liu, Zhiguang1,2,3; Cai, Minkun1; Hong, Shenda4; Shi, Junli1; Xie, Sai1; Liu, Chang2; Du, Huifeng2; Morin, James D.2; Li, Gang1; Wang, Liu5; Wang, Hong1; Tang, Ke6; Fang, Nicholas X.2,7; Guo, Chuan Fei1

2024-07-09

10.1073/pnas.2320222121

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA   影响因子和分区

0027-8424

1091-6490

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.

inverse design flexible pressure sensor flexible electronics machine learning linear response

SCI

英语

第一 ; 通讯

National Natural Science Foundation of China["T2225017","52073138","62102008"] ; Science Technology and Innovation Committee of Shenzhen Municipality[CYJ20210324120202007] ; Shenzhen Sci-Tech Fund[KYTDPT20181011104007] ; Guangdong Provincial Key Laboratory Program"[2021B1212040001]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001272924400012

NATL ACAD SCIENCES

BIOLOGY & BIOCHEMISTRY ; CLINICAL MEDICINE ; MULTIDISCIPLINARY ; PLANT & ANIMAL SCIENCE ; ENVIRONMENT/ECOLOGY ; SOCIAL SCIENCES, GENERAL ; MICROBIOLOGY ; ECONOMICS BUSINESS ; IMMUNOLOGY ; MATERIALS SCIENCE ; MATHEMATICS ; SPACE SCIENCE ; MOLECULAR BIOLOGY & GENETICS ; PHARMACOLOGY & TOXICOLOGY ; CHEMISTRY ; PSYCHIATRY/PSYCHOLOGY ; NEUROSCIENCE & BEHAVIOR ; PHYSICS ; GEOSCIENCES ; AGRICULTURAL SCIENCES ; ENGINEERING

Web of Science

1.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China
2.MIT, Dept Mech Engn, Cambridge, MA 02139 USA
3.Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei 230027, Peoples R China
4.Peking Univ, Natl Inst Hlth Data Sci, Beijing 100191, Peoples R China
5.Univ Sci & Technol China, Dept Modern Mech, Hefei 230027, Peoples R China
6.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China
7.Univ Hong Kong, Dept Mech Engn, Hong Kong 999077, Peoples R China

Liu, Zhiguang,Cai, Minkun,Hong, Shenda,et al. Data- driven inverse design of flexible pressure sensors[J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,2024,121(28).

Liu, Zhiguang.,Cai, Minkun.,Hong, Shenda.,Shi, Junli.,Xie, Sai.,...&Guo, Chuan Fei.(2024).Data- driven inverse design of flexible pressure sensors.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,121(28).

Liu, Zhiguang,et al."Data- driven inverse design of flexible pressure sensors".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 121.28(2024).