[24p-E203-1~16] 23.1 Joint Session N "Informatics"

[24p-E203-1] Analysis of dendrite microstructure growth based on extended Gibbs free energy model

Misato Tone¹, Shunsuke Sato¹, 〇Masato Kotsugi¹ (1.Tokyo Univ. Sci.)

Keywords:dendrite, machine learning, persistent homology

We have developed an extended Gibbs free energy model by applying data science to conventional Gibbs free energy to analyze the growth mode of dendrite microstructure. The model utilises persistent homology (PH) to draw a novel energy landscape in information space. As a result, a quantitative and bi-directional linkage between the microstructure and the physical properties is established. we could successfully design the features to express the growth mode of dendrite microstructure.