Jeong Min Choi

most citedTraining machine-learning potentials for crystal structure prediction using disordered structures

41 citations · 41 across the 3 of their papers we have counts for

collaborators

3 papers

cond-mat.mtrl-sci2025

Atomistic Insights into Cu/amorphous-Ta $_{x}$ N 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 Ta $_{x}$ N diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu inter…

cond-mat.mtrl-sci2025

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…

physics.comp-ph2020★ 41 cited

Training machine-learning potentials for crystal structure prediction using disordered structures

Changho Hong, Jeong Min Choi, Wonseok Jeong +6

Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in explori…

Atomistic Insights into Cu/amorphous-Tax​N Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction

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

Training machine-learning potentials for crystal structure prediction using disordered structures

Atomistic Insights into Cu/amorphous-Ta $_{x}$ N Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction