Presentation Information
[18a-PA1-17]Neural Network-Based Molecular Dynamics Simulations of Silica Etching Processes
〇Yuta Yoshimoto1, Meguru Yamazaki1, Naoki Matsumura1, Yuto Iwasaki1, Kazutaka Nishiguchi1, Yasufumi Sakai1, Hideyuki Jippo1 (1.Fujitsu)
Keywords:
molecular dynamics,neural network potential,etching
We develop a high-fidelity neural network potential (NNP) for the Si-O-H-F system to elucidate the atomic-level reaction mechanisms governing the dry and wet etching of silica. By leveraging knowledge distillation from a large-scale pre-trained model, we efficiently construct a lightweight yet accurate NNP, significantly reducing the required volume of first-principles data. This NNP is subsequently employed in molecular dynamics simulations to analyze the dry etching process by hydrogen fluoride and the wet etching process by hydrofluoric acid.