Optimizing Piezoelectric Nanocomposites by High‐Throughput Phase‐Field Simulation and Machine Learning

Autor: Weixiong Li, Tiannan Yang, Changshu Liu, Yuhui Huang, Chunxu Chen, Hong Pan, Guangzhong Xie, Huiling Tai, Yadong Jiang, Yongjun Wu, Zhao Kang, Long‐Qing Chen, Yuanjie Su, Zijian Hong
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Advanced Science, Vol 9, Iss 13, Pp n/a-n/a (2022)
Druh dokumentu: article
ISSN: 2198-3844
DOI: 10.1002/advs.202105550
Popis: Abstract Piezoelectric nanocomposites with oxide fillers in a polymer matrix combine the merit of high piezoelectric response of the oxides and flexibility as well as biocompatibility of the polymers. Understanding the role of the choice of materials and the filler‐matrix architecture is critical to achieving desired functionality of a composite towards applications in flexible electronics and energy harvest devices. Herein, a high‐throughput phase‐field simulation is conducted to systematically reveal the influence of morphology and spatial orientation of an oxide filler on the piezoelectric, mechanical, and dielectric properties of the piezoelectric nanocomposites. It is discovered that with a constant filler volume fraction, a composite composed of vertical pillars exhibits superior piezoelectric response and electromechanical coupling coefficient as compared to the other geometric configurations. An analytical regression is established from a linear regression‐based machine learning model, which can be employed to predict the performance of nanocomposites filled with oxides with a given set of piezoelectric coefficient, dielectric permittivity, and stiffness. This work not only sheds light on the fundamental mechanism of piezoelectric nanocomposites, but also offers a promising material design strategy for developing high‐performance polymer/inorganic oxide composite‐based wearable electronics.
Databáze: Directory of Open Access Journals
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