5 papers
Discovery of oxide Li-conducting electrolytes in uncharted chemical space via topology-constrained crystal structure prediction
Seungwoo Hwang, Jiho Lee, Seungwu Han +2
Oxide Li-conducting solid-state electrolytes (SSEs) offer excellent chemical and thermal stability but typically exhibit lower ionic conductivity than sulfides and chlorides. This…
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…
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh +5
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient altern…
Neural Network-Driven Molecular Insights into Alkaline Wet Etching of GaN: Toward Atomistic Precision in Nanostructure Fabrication
Purun-hanul Kim, Jeong Min Choi, Seungwu Han +1
We present large-scale molecular dynamics (MD) simulations based on a machine-learning interatomic potential to investigate the wet etching behavior of various GaN facets in alkali…
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…