[18p-S2_204-1~10] 23.1 Joint Session N "Informatics"

3:45 PM - 4:00 PM JST(6:45 AM - 7:00 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[18p-S2_204-7] Analysis of Compositional and Structural Strain Effects on Ferroelectric Properties of ScAlN using Machine Learning Force Fields

〇(M1)Ryotaro Sahashi¹, Po-Yen Chen¹, Teruyasu Mizoguchi² (1.Univ. of Tokyo Eng, 2.Univ. of Tokyo IIS)

Keywords:

Ferroelectrics,Machine Learning Force Fields,ScAlN

We systematically analyzed the effects of Sc concentration (composition) and in-plane strain and c/a ratio (structure) on ferroelectric properties using large-scale molecular dynamics simulations based on machine learning force fields. By independently varying these two factors, which are difficult to decouple experimentally, we quantitatively clarified their respective contributions to the coercive field and polarization magnitude. In the presentation, we will report detailed analysis results, including the correlation between composition and structural strain.