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

Haitao Yang, Jiali Li, Kai Zhuo Lim, Chuanji Pan, Tien Van Truong, Qian Wang, Kerui Li, Shuo Li, Xiao Xiao, Meng Ding, Tianle Chen, Xiaoli Liu, Qian Xie, Pablo Valdivia y. Alvarado, Xiaonan Wang*, Po Yen Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)84-94
Number of pages11
JournalNature Machine Intelligence
Volume4
Issue number1
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

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