Relationship between components and properties of Sm-PMN-PT piezoelectric ceramics

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, 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 output, we have created a database through which we can quickly find the optimal composition of Sm-doped PMN-PT ceramics according to specific needs.The database includes the following for different components of Sm-PMN-PT ceramics: piezoelectric coefficient d33, curie temperature Tc, coercive electric field Ec, residual polarization strength Pr, dielectric constant e.