Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics

<p>In material science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric...

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Xehetasun bibliografikoak
Egile Nagusiak:	Jingjin He, Changxin Wang, Junjie Li, Chuanbao Liu, Dezhen Xue, Jiangli Cao, Yanjing Su, Lijie Qiao, Turab Lookman, Yang Bai
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Tsinghua University Press 2023-09-01
Saila:	Journal of Advanced Ceramics
Gaiak:	machine learning (ml) dielectric temperature spectrum ferroelectrics phase transition information
Sarrera elektronikoa:	https://www.sciopen.com/article/10.26599/JAC.2023.9220788
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Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics

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