Undergraduate researcher building reproducible simulation workflows for electrochemical interfaces and materials modeling.
My goal is to become a researcher who learns steadily, works honestly, and contributes meaningfully.
I am an undergraduate student at UNIST, majoring in Energy and Chemical Engineering. I enjoy building reproducible computational workflows - from simulation setup to analysis-ready results. My current interests include electrochemical interfaces, molecular simulations, and materials machine learning.
Electrochemical interfaces, molecular simulations (MD), materials machine learning
Reproducible workflows, automation, validation and analysis
A short snapshot of tools I have used in research.
Section: Energy & Environment
Recipients: Jinki Hong, Subin Lee, Gibeom Kim (UNIST)
Paper: Selective F-Diluent Exclusion by Siloxane Co-diluents for Stable Interphases in Lithium Metal Batteries
February 11, 2026
Coming soon. I'll list peer-reviewed papers and preprints here once they are published.
A configurable generator to build reproducible LAMMPS inputs for simulation workflows.
Outcome: Standardized LAMMPS inputs to reduce manual edits and improve reproducibility.
A YAML-driven workflow to build molecular simulation starting structures using Packmol and ASE.
Outcome: Built a reusable Packmol/ASE workflow for reproducible simulation structure generation.
Trained and evaluated a CGCNN-based model to predict bandgaps of hexanary oxides for photocatalyst discovery.
Outcome: Built a reproducible pipeline from structures to bandgap predictions using CGCNN.
A modular Hartree–Fock implementation in Python focused on clarity, validation, and extensibility.
Outcome: Implemented a modular HF codebase designed for clarity, validation, and extensions.