[S-IT22] Joint Session of Deep Earth Sciences and Innovation through the Integration with Material Sciences

[SIT22-20] Effects of Point Defects on the Thermoelasticity of Al-bearing Bridgmanite Using Molecular Dynamics Based on Machine Learning Potential

*Seitaro Yazawa¹, Kenji Kawai¹, Kobayashi Ryo², Satoshi Ohmura³ (1. The University of Tokyo, 2. Nagoya Institute of Technology, 3. Ehime University)

Keywords:Bridgmanite, Mineral Physics, Machine learning potential, Point defect