Hyungmin An

collaborators

3 papers

cond-mat.mtrl-sci2026

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

Sangmin Oh, Jinmu You, Jaesun Kim +4

We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowled…

cond-mat.mtrl-sci2025

Etching-to-deposition transition in SiO $_{2}$ /Si $_{3}$ N $_{4}$ using CH $_{x}$ F $_{y}$ ion-based plasma etching: An atomistic study with neural network potentials

Hyungmin An, Sangmin Oh, Dongheon Lee +4

Plasma etching, a critical process in semiconductor fabrication, utilizes hydrofluorocarbons both as etchants and as precursors for carbon film formation, where precise control ove…

cond-mat.mtrl-sci2025

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Suyeon Ju, Jinmu You, Gijin Kim +3

Achieving higher operational voltages, faster charging, and broader temperature ranges for Li-ion batteries necessitates advancements in electrolyte engineering. However, the compl…

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

Etching-to-deposition transition in SiO2​/Si3​N4​ using CHx​Fy​ ion-based plasma etching: An atomistic study with neural network potentials

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Etching-to-deposition transition in SiO $_{2}$ /Si $_{3}$ N $_{4}$ using CH $_{x}$ F $_{y}$ ion-based plasma etching: An atomistic study with neural network potentials