Open AccessOptimizing Piezoelectric Nanocomposites by High‐Throughput Phase‐Field Simulation and Machine Learning (Adv. Sci. 13/2022)
Author(s) -
Li Weixiong,
Yang Tiannan,
Liu Changshu,
Huang Yuhui,
Chen Chunxu,
Pan Hong,
Xie Guangzhong,
Tai Huiling,
Jiang Yadong,
Wu Yongjun,
Kang Zhao,
Chen LongQing,
Su Yuanjie,
Hong Zijian
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202270084
Subject(s) - piezoelectricity , nanocomposite , materials science , throughput , ferroelectricity , phase (matter) , field (mathematics) , nanotechnology , composite material , computer science , optoelectronics , physics , telecommunications , mathematics , quantum mechanics , dielectric , pure mathematics , wireless
Optimizing Piezoelectric Nanocomposites In article number 2105550, Tiannan Yang, Zhao Kang, Long‐Qing Chen, Yuanjie Su, Zijian Hong, and co‐workers conduct an integrated study with high‐throughput phase‐field simulations and machine learning to systematically reveal the influence of morphology and spatial orientation of an oxide filler on the effective piezoelectric properties of the polymer/ferroelectric oxide nanocomposites.