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

Li, Weixiong; Yang, Tiannan; Liu, Changshu; Huang, Yuhui; Chen, Chunxu; Pan, Hong; Xie, Guangzhong; Tai, Huiling; Jiang, Yadong; Wu, Yongjun; Kang, Zhao; Chen, Long‐Qing; Su, Yuanjie; Hong, Zijian

doi:10.1002/advs.202105550

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

Journal Article · Fri Mar 11 00:00:00 EST 2022 · Advanced Science

DOI:https://doi.org/10.1002/advs.202105550· OSTI ID:1854615

Li, Weixiong ^[1]; Yang, Tiannan ^[2]; Liu, Changshu ^[3]; Huang, Yuhui ^[4]; Chen, Chunxu ^[1]; Pan, Hong ^[1]; Xie, Guangzhong ^[1]; Tai, Huiling ^[1]; Jiang, Yadong ^[1]; Wu, Yongjun ^[4]; Kang, Zhao ^[3]; Chen, Long‐Qing ^[2]; Su, Yuanjie ^[1];

^[4]

School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 P. R. China
School of Materials Science and Engineering The Pennsylvania State University University Park PA 16802 USA
School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 P. R. China
Lab of Dielectric Materials School of Materials Science and Engineering Zhejiang University Hangzhou 310027 P. R. China

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.

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Research Organization:: Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Natural Science Foundation of China (NSFC); Zhejiang Provincial Natural Science Foundation

Grant/Contract Number:: SC0020145

OSTI ID:: 1854615

Alternate ID(s):: OSTI ID: 1854618; OSTI ID: 1880783

Journal Information:: Advanced Science, Journal Name: Advanced Science Vol. 9 Journal Issue: 13; ISSN 2198-3844

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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