Optimizing Piezoelectric Nanocomposites by High‐Throughput Phase‐Field Simulation and Machine Learning (Adv. Sci. 13/2022)

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 s...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Frontispiece
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069355/
http://dx.doi.org/10.1002/advs.202270084
Descripción
Sumario: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. [Image: see text]

Ejemplares similares