首页|Physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance

Physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance

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Pb(Mg1/3Nb2/3)O3-PbTiO3(PMN-PT)piezoelectric ceramics have excellent piezoelectric properties and are used in a wide range of applications.Adjusting the solid solution ratios of PMN/PT and different concentrations of elemental doping are the main methods to modulate their piezoelectric coefficients.The combination of these controllable conditions leads to an exponential increase of possible compositions in ceramics,which makes it not easy to extend the sample data by additional experimental or theoretical calculations.In this paper,a physics-embedded machine learning method is proposed to overcome the difficulties in obtaining piezoelectric coefficients and Curie temperatures of Sm-doped PMN-PT ceramics with different components.In contrast to all-data-driven model,physics-embedded machine learning is able to learn nonlinear variation rules based on small datasets through potential correlation between ferroelectric properties.Based on the model outputs,the positions of morphotropic phase boundary(MPB)with different Sm doping amounts are explored.We also find the components with the best piezoelectric property and comprehensive performance.Moreover,we set up a database according to the obtained results,through which we can quickly find the optimal components of Sm-doped PMN-PT ceramics according to our specific needs.

Pb(Mg1/3Nb2/3)O3-PbTiO3(PMN-PT)ceramicphysics-embedded machine learningpiezoelec-tric coefficientCurie temperature

辛睿、王亚祺、房泽、郑凤基、高雯、付大石、史国庆、刘建一、张永成

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College of Physics,Center for Marine Observation and Communications,National Demonstration Center for Experimental Applied Physics Education,Qingdao University,Qingdao 266071,China

Centre for Theoretical and Computational Physics,College of Physics,Qingdao University,Qingdao 266071,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Shandong ProvinceYouth Innovation Team Project of Shandong Provincial Education Department

5227211612002400ZR2021ME0962019KJJ012

2024

10.1088/1674-1056/ad51f3

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(8)