Application of Machine Learning in Optimization of High-permittivity Energy-storage Ba(Ti1-xHfx)O3 Ceramic

被引：0
|
作者：
Liu Y. [1 ]
Gao J. [1 ]
Yan W. [1 ]
Wang Y. [1 ]
He Z. [1 ]
Zhong L. [1 ]
机构：
[1] State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an
来源：
Gaodianya Jishu/High Voltage Engineering | 2017年 / 43卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Accelerated search; Ceramics; Dielectric permittivity; Energy storage material design; Machine learning; Tricritical point;
D O I：
10.13336/j.1003-6520.hve.20170628018
中图分类号：
学科分类号：
摘要：
In order to accelerate the design process of high dielectric permittivity materials, the machine learning optimization iterating with fabrication and experiment characterization method was employed in designing the high dielectric permittivity tricritical point Ba(Ti1-xHfx)O3 ceramic. During the process, the optimization machine learning model was built to accelerate the searching for high-permittivity tricritical point, and several possible algorithms' efficiency and convergence rate have been compared and discussed. The results show that the largest relative permittivity is found to be 4.5×104 at the composition of x=11%, which is much higher than that of normal ceramics (about 1 000); and the efficiency has been improved by 37.5%. This finding may provide a new method for designing high permittivity and energy density ceramics dielectrics. © 2017, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：2229 / 2233
页数：4
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