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 d...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.展开更多
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 pract...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.展开更多
基金The authors would like to acknowledge the support from the National Natural Science Foundation of China(61871369)M.M.acknowledges the Youth Innovation Promotion Association of CAS and Shanghai Rising-Star Program(20QA1410200).
文摘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.
基金The authors would like to acknowledge the supports from the Key-Area Research and Development Program of Guangdong Province(2020B010176001)the National Natural Science Foundation of China(61871369)M.S.Ma acknowledges the Youth Innovation Promotion Association of CAS and Shanghai Rising-Star Program(20QA1410200).
文摘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.
