Automatic strain sensor design via active learning and data augmentation for soft machines

Yang, Haitao1; Li, Jiali1; Lim, Kai Zhuo1; Pan, Chuanji1; Tien Van Truong2; Wang, Qian1; Li, Kerui1; Li, Shuo1; Xiao, Xiao3; Ding, Meng1; Chen, Tianle4; Liu, Xiaoli1; Xie, Qian5; Valdivia y Alvarado, Pablo2; Wang, Xiaonan1,6; Chen, Po-Yen4

2022

10.1038/s42256-021-00434-8

NATURE MACHINE INTELLIGENCE   影响因子和分区

2522-5839

["Emerging soft machines require high-performance strain sensors to achieve closed-loop feedback control. Machine learning is a versatile tool to uncover complex correlations between fabrication recipes and sensor performance at the device level. Here a three-stage machine learning framework was realized for a high-accuracy prediction model capable of automating the design of strain sensors. First, a support-vector machine classifier was trained by using 351 compositions of various nanomaterials. Second, through 12 active learning loops, 125 strain sensors were stagewise fabricated to enrich the multidimensional dataset. Third, to address the challenge of data scarcity, data augmentation was implemented to synthesize >10,000 virtual data points, followed by genetic algorithm-based selection to optimize the model's prediction accuracy. Several data-driven design rules for piezoresistive nanocomposites were generalized and validated by in situ microscopic studies. As final demonstrations, model-suggested strain sensors can be integrated into/onto various soft machines to endow them with real-time strain-sensing capabilities.","Piezoresistors can be used in strain sensors for soft machines, but the traditional design process relies on intuition and human ingenuity alone. Haitao Yang and colleagues present a method built on genetic algorithms and other machine learning methods to design and fabricate strain sensors with improved capabilities."]

SCI ; EI

英语

其他

Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic Grant 'Accelerated Materials Development for Manufacturing' by the Agency for Science, Technology and Research[A1898b0043] ; University of Maryland, College Park[2957431] ; Maryland Industrial Partnerships[6808,4311103] ; Maryland Innovation Initiative (MII) Technology Assessment Award[4308302] ; MOST-AFOSR Taiwan Topological and Nanostructured Materials Grant["FA2386-21-1-4065",5284212]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications

WOS:000749017100001

NATURE PORTFOLIO

20220511561505

Genetic algorithms ; Virtual addresses

Data Storage, Equipment and Techniques:722.1 ; Computer Software, Data Handling and Applications:723

Web of Science

1.Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore, Singapore
2.Singapore Univ Technol & Design, Engn Prod Dev, Singapore, Singapore
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
4.Univ Maryland, Dept Chem & Biomol Engn, College Pk, MD 20742 USA
5.Anhui Univ Technol, Sch Metallurg Engn, Maanshan, Peoples R China
6.Tsinghua Univ, Dept Chem Engn, Beijing, Peoples R China

Yang, Haitao,Li, Jiali,Lim, Kai Zhuo,et al. Automatic strain sensor design via active learning and data augmentation for soft machines[J]. NATURE MACHINE INTELLIGENCE,2022,4(1):84-94.

Yang, Haitao.,Li, Jiali.,Lim, Kai Zhuo.,Pan, Chuanji.,Tien Van Truong.,...&Chen, Po-Yen.(2022).Automatic strain sensor design via active learning and data augmentation for soft machines.NATURE MACHINE INTELLIGENCE,4(1),84-94.

Yang, Haitao,et al."Automatic strain sensor design via active learning and data augmentation for soft machines".NATURE MACHINE INTELLIGENCE 4.1(2022):84-94.