3 papers
cond-mat.mtrl-sci2025
Atomistic Insights into Cu/amorphous-TaN Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
Jeong Min Choi, Jaehoon Kim, Ji-Hwan Lee +2
Accurate understanding and control of interfacial adhesion between Cu and TaN diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu inter…
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-sci2024
Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
Jaesun Kim, Jisu Kim, Jaehoon Kim +4
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced…