Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Advanced science, 2022-05, Vol.9 (13), p.e2105550-n/a

Ort / Verlag

Germany: John Wiley & Sons, Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Wiley HSS Collection

Beschreibungen/Notizen

• 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. A high‐throughput phase‐field model is built to systematically investigate the influence of oxide filler morphology on the piezoelectric properties of polymer/oxide nanocomposites. It is discovered that with a constant filler volume fraction, the vertical pillars exhibit the best piezoelectric performances. A linear regression‐based machine learning model is further employed to predict the performance of the polymer/ceramic nanocomposites.