Abstract
Conference Title: 2018 12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM) Conference Start Date: 2018, May 20 Conference End Date: 2018, May 24 Conference Location: Xi'an, China As the increasing requirement of compact and light electronic and electrical system, especially for high energy density storage system, the development of high dielectric permittivity materials becomes one of critical solutions. Among all materials, barium titanate materials have been found to be a promising ceramic capacitor dielectric system, like BaTi 1−x Hf x O 3 material and BaTi 1−x Sn x O 3 material. However, to search the optimal composition, mass samples are needed by the traditional method of exhaustion, which increases the cost of work. In order to access the optimal composition of high permittivity efficiently, a machine learning prediction is employed to the searching process in Sn/Ca doped barium titanate ceramics. The machine learning prediction is iteration between theoretical predictions and experiment data. According to our prediction, the searching result shows Ba 0.86 Ca 0.14 Ti 0.86 Sn 0.14 O 3 has the peak values of dielectric permittivity with ε r =2.0×104 at T=13°C. As a result, sample preparation number reduces to 5.5% compared with the traditional method of exhaustion. In addition, good temperature stability has been found in Ba 0.86 Ca 0.14 Ti 0.86 Sn 0.14 O 3 because of the relaxor behavior.