Neural Network-Driven Molecular Insights into Alkaline Wet Etching of GaN: Toward Atomistic Precision in Nanostructure Fabrication

We present a Behler-Parrinello-type neural network potential (NNP) for a complex quinary system consisting of Gallium (Ga), Nitrogen (N), Potassium (K), and Oxygen (O). This highly accurate NNP was trained on a diverse dataset to reliably model interactions and dynamics in this multi-component material system, which is crucial for applications in GaN-KOH chemical wet etching.