Machine learning assistedτf value prediction of ABO_(3)-type microwave dielectric ceramicsOA
The temperature coefficient of resonance frequency(τf or TCF)is the key parameter for evaluating temperature stability of microwave dielectric ceramics.In this work,a machine learning framework was proposed to predict theτf values of ABO_(3)-type microwave dielectric ceramics.Leveraging a curated dataset of 104 single-phase ABO_(3)-type compounds,we systematically evaluated models based on five machine learning algorithms using 31 structural descriptors as input features.The eXtreme Gradient Boosting(XGB)algorithm emerged as the optimal predictive model,demonstrating robust performance on the test set(R^(2)=0.7799,RMSE=15.7494×10^(−6)℃^(−1)).Consistent results on the validation set further confirmed its generalization capability.Critical features contributing to the model''s performance include molecular dielectric polarizability(pm),tolerance factor(tt),ionic volume(Vi)and relative molecular mass(m).Structure-property relationship studies revealed that the pm plays an important role in modulating the τf value by affecting the permittivity.Quantitative thresholds for these critical descriptors were also derived for identifying materials with near-zeroτf.This work provides an effective data-driven approach for accelerating the discovery of microwave dielectric ceramics with good temperature stability.
Mingyue Yang;Liangyu Mo;Jincheng Qin;Faqiang Zhang;Mingsheng Ma;Yongxiang Li;Zhifu Liu
The State Key Laboratory of High Performance Ceramics,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,201899,China Center of Materials Sciences and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing,100049,ChinaThe State Key Laboratory of High Performance Ceramics,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,201899,China Center of Materials Sciences and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing,100049,ChinaThe State Key Laboratory of High Performance Ceramics,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,201899,ChinaThe State Key Laboratory of High Performance Ceramics,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,201899,ChinaThe State Key Laboratory of High Performance Ceramics,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,201899,China Center of Materials Sciences and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing,100049,ChinaRMIT University,Melbourne,VIC,3001,AustraliaThe State Key Laboratory of High Performance Ceramics,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,201899,China Center of Materials Sciences and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing,100049,China
化学化工
Microwave dielectric ceramicsTemperature coefficient of resonant frequencyMachine learningStructure-property relationship
《Journal of Materiomics》 2026 (1)
P.202-211,10
supports from the Joint Funds of the National Natural Science Foundation of China(No.U24A2052)the Program of Shanghai Academic Research Leader(No.23XD1404600)the Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation(No.GZC20232825)the Shanghai Eastern Talent Plan(No.QNKJ2024026).
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