Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics

Low permittivity microwave dielectric ceramics(MWDCs)are attracting great interest because of their promising applications in the new era of 5G and IoT.Although theoretical rules and computational methods are of practical use for permittivity prediction,unsatisfactory predictability and universality impede rational design of new high-performance materials.In this work,based on a dataset of 254 single-phase microwave dielectric ceramics(MWDCs),machine learning(ML)methods established a high accuracy model for permittivity prediction and gave insights of quantitative chemistry/structureproperty relationships.We employed five commonly-used algorithms,and introduced 32 intrinsic chemical,structural and thermodynamic features which have correlations with permittivity for modeling.Machine learning results help identify the permittivity decisive factors,including polarizability per unit volume,average bond length,and average cell volume per atom.The feature-property relationships were discussed.The optimal model constructed by support vector regression with radial basis function kernel was validated its superior predictability and generalization by verification dataset.Low permittivity material systems were screened from a dataset of~3300 materials without reported microwave permittivity by high-throughput prediction using optimal model.Several predicted low permittivity ceramics were synthesized,and the experimental results agree well with ML prediction,which confirmed the reliability of the prediction model.

Optimizing and extending ion dielectric polarizability database for microwave frequencies using machine learning methods

Permittivity at microwave frequencies determines the practical applications of microwave dielectric ceramics.The accuracy and universality of the permittivity prediction by Clausius–Mossotti equation depends on the dielectric polarizability(αD)database.The most influentialαD database put forward by Shannon is facing three challenges in the 5 G era:(1)Few data,(2)Simplistic relation and(3)Low frequency(kHz–MHz)oriented.Here,we optimized and extended the Shannon’s database for microwave frequencies by the four-stage multiple linear regression and support vector machine model.In comparison with the conventional database,the optimized and extended databases achieved higher accuracy and expanded the amount of data from 60 to more than 900.Besides,we analyzed the relationships betweenαD and ion characteristics,including ionic radius(IR),atomic number(N),valence state(V)and coordination number(CN).We found that the positive cubic law of“αD~IR3”discussed in Shannon’s work was valid for the IR changed by the N,but invalid for the change caused by the CN.

Unusual local electric field concentration in multilayer ceramic capacitors

Local electric-field around multitype pores(dielectric pore,interface pore,electrode pore)in multilayer ceramic capacitors(MLCCs)was investigated using Kelvin probe force microscopy combined with the finite element simulation to understand the effect of pores on the electric reliability of MLCCs.Electricfield is found to be concentrated significantly in the vicinity of these pores and the strength of the local electric-field is 1.5e5.0 times of the nominal strength.Unexpectedly,the concentration degree of the pores in the inner electrode is much higher than that in the dielectrics and dielectric-electrode interfaces.Meanwhile,geometry orientations are found to have a remarkable influence on the local electric field strength.The pores act as an insulation degradation precursor via local electric,thermal center,and oxygen vacancies accumulation center.Such unusual local electric field concentration of multitype pores can provide new insights into the understanding of insulation degradation evolution,processing tailoring and design optimization for MLCCs.

独石结构压电陶瓷扭转驱动器

通过设计由异质单元组成的人工结构并调节其界面耦合方式,经常能够得到具有全新性质的功能器件.对压电器件来说,其变形响应来自压电晶体的伸长、压缩、剪切模态及其组合,同时取决于结构设计的边界条件.然而,由于受到晶体微观层面镜面对称性对扭转模态的限制,长期以来,在单一的集成压电元器件中实现基本扭转驱动是一个很困难的任务.本工作中,我们设计了一种共烧独石驱动器,能够初次解决这个问题.该原型器件由两组工作在反向人工剪切模态的多层驱动单元组成,它们手性的协同应力耦合将在宽频范围内产生合成的扭转输出.有限元模拟表明通过改变其几何尺寸,扭转位移可以在很大范围调节.横向挠度测试证实了其在电场作用下稳定且相当大的线性输出响应(1 Hz和40 V电压条件下约3.7μm).更重要的是,该设计方法实际上提出了在单一压电陶瓷元件中获得任意模态和驱动状态的一种更广泛的途径.