3 papers
cond-mat.mtrl-sci2026
Atomic-Scale Mechanisms of SiO Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential
Jaehoon Kim, Minseok Moon, Hyunsung Cho +5
Plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide (SiO) is widely used for low-temperature fabrication of dielectric thin films, yet its atomic-scale growth…
cond-mat.mtrl-sci2025
Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
Hyunsung Cho, Minseok Moon, Jaehoon Kim +6
Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulat…
cond-mat.mtrl-sci2025
Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials
Sungmo Kang, Rokyeon Kim, Seungwu Han +1
The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while…