講演情報
[11a-B21-5]Surface reaction analysis of thermal ALE of transition metal oxides with acetylacetonate ligands
〇(P)Lucas Fabian Spiske1, Satoshi Hamaguchi1 (1.Osaka University)
キーワード：
DFT、Molecular Dynamics、Machine Learning Potential
In this work, we investigate the atomic layer etching (ALE) process of the zirconium oxide surface using reactants such as hydrogen fluoride (HF) and acetylacetonate (acac) by performing first-principle density functional theory (DFT) calculations as well as machine learning (ML) potential- based molecular dynamics (MD) simulations. The work helps to unravel the reaction mechanisms of these reactions for etching the surface, as well as modeling the dynamics movement and chemistry of ligands on the surface on a longer timescale through the MD simulations. Both methods can then be combined in a workflow for efficient and automatized further training of ML potentials.