Curie Temperature Prediction of BiFeO3-PbTiO3-BaTiO3 Solid Solution Based on Machine Learning

Perovskite（ABO3） piezoceramics have been developed for several decades, and there are a lot of data available. It is of great significance to find relationships between structure and properties of materials from these data. In this work, experimental data of Curie temperature（Tc） of BiFeO3-PbTiO3-BaTiO3 solid solution of perovskite piezoelectric ceramics was collected to build the model to predict the Tc. From the perspective of thermodynamics, the quadratic polynomial relationship between Tc and reduced mass was introduced but the deviation was relatively large. More descriptors（including element information, physical quantities, space groups number） and SISSO（Sure Independence Screening and Sparsifying Operator） were used for machine learning to find the correlation between Tc and components. Comparing the root mean square error（RMSE） of different descriptors and dimensions, it's found that more descriptors, more fundamental the descriptors are, and larger dimension will result in smaller RMSE to be used. M