Presentation Information
[18a-PA1-5]Analysis of Electric Field-induced Phase Transition in Tungsten Bronze-type Antiferroelectrics using Machine Learning Interatomic Potential
〇Hodaka Abe1, Sou Yasuhara1, Takuya Hoshina1 (1.Science Tokyo)
Keywords:
Machine-learning Interatomic Potential,Dielectrics,Antiferroelectrics
Antiferroelectrics are expected to be used in energy storage capacitors due to their large polarization response arising from electric field-induced phase transitions. While K2NdNb5O15-type antiferroelectrics with a tungsten bronze-type structure exhibit excellent energy storage properties, the mechanism of their polarization response remains unclear due to the complexity of their crystal structure. In this study, we created a surrogate model based on first-principles calculations using machine learning and applied it to molecular dynamics simulations in order to analyze the atomic level mechanism of the electric field-induced phase transition in this material.