ニューラルネットワークポテンシャルの材料科学への応用～ガラス構造の部分結晶化とイオン伝導機構の解析～

抄録

This study introduces the application of neural network potentials (NNPs) in materials science, with a focus on their use in analyzing partial crystallization and ion conduction mechanisms in glass structures, while also highlighting recent advancements in NNPs. Specifically, molecular dynamics (MD) simulations employing NNPs were performed to investigate the crystallization process of Li₃PS₄ glass under heat treatment. The simulations revealed the nucleation and growth of crystalline phases within the glass matrix, providing atomic-level insights into the crystallization mechanism. Furthermore, an assessment of the impact of crystallization on lithium transport properties demonstrated that the precipitated crystalline phase corresponded to the high-temperature α-phase. Additionally, the formation of new ion conduction pathways through the interconnection of crystal nuclei was identified as a key factor in enhancing ionic conductivity in glass-ceramic materials.