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

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...

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Veröffentlicht in:Advanced science 2022-05, Vol.9 (13), p.n/a
Hauptverfasser: 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
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Sprache:eng
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Zusammenfassung: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.
ISSN:2198-3844
2198-3844
DOI:10.1002/advs.202270084