高电压技术2017，Vol.43Issue(7)：2229-2233,5.DOI:10.13336/j.1003-6520.hve.20170628018
机器学习在储能陶瓷Ba(Ti1-xHfx)O3介电常数寻优中的应用
Application of Machine Learning in Optimization of High-permittivity Energy-storage Ba(Ti1-xHfx)O3 Ceramic
摘要
Abstract
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=l 1％,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.关键词
储能材料设计/介电常数/机器学习/加速寻优/三临界点/陶瓷介质Key words
energy storage material design/dielectric permittivity/machine learning/accelerated search/tricritical point/ceramics引用本文复制引用
刘泳斌,高景晖,闫文博,王妍,何芷欣,钟力生..机器学习在储能陶瓷Ba(Ti1-xHfx)O3介电常数寻优中的应用[J].高电压技术,2017,43(7):2229-2233,5.基金项目
国家自然科学基金(51207121).Project supported by National Natural Science Foundation of China (51207121). （51207121）
