Welcome to Computational Materials & Interfaces Research Group in the Division of Advanced Materials Engineering, Jeonbuk National University (JBNU), S. Korea.
“Computational Materials & Interfaces Research Group” in JBNU, led by Professor Taehun Lee, utilizes computational simulation approaches to analyze and predict materials’ properties. Our particular emphasis lies in semiconductor materials, with a focus on defects, and their applications in diverse fields such as solar cells, photocatalysts, and semiconductor devices. We are dedicated to investigating the electronic and dynamical properties of semiconducting materials at interfaces, including vacuum/surfaces, electrolyte/electrode interfaces, and heterojunctions between different materials. Furthermore, our research extends to exploring unconventional forms of materials, such as amorphous structures, nanostructures, and defect-rich phases.
We employ classical electronic structure calculation methods such as density-functional theory and the Hartree-Fock method calculations to characterize material properties at the electronic level. Moreover, our group performs molecular dynamics simulations using machine-learned interatomic potentials, extending the scope of our simulations to device scales. Recently, our focus has turned toward integrating methodologies like data mining and machine learning with traditional simulation methods to speed up the discovery of highly functional materials.
See our group’s publications.
안녕하세요. 전북대학교 신소재공학부 전자재료전공 “재료 & 계면 전산 모사 연구실” (지도 교수: 이태훈)
홈페이지 입니다.
저희 연구실은 재료와 재료의 계면 특성을 분석하고 예측하기 위해 전산 모사 기법을 활용하고 있습니다. 특히 태양 전지, 광촉매, 반도체 소자 등에 활용되는 결함을 포함하는 반도체 재료 및 반도체/전해질 계면에 초점을 맞추고 있습니다. 전자 수준부터 디바이스 스케일까지 재료 특성을 평가하기 위해, density-functional theory, Hartree-Fock method 같은 고전적인 전자 구조 계산 기법을 활용하며, 또한 최근에 주목 받고 있는 머신 러닝 포텐셜 기반의 molecular dynamics을 수행하고 있습니다. 최근에는 데이터마이닝, 머신 러닝 같은 기술을 전통적 방법 및 소재 관련 데이터와 결합하여 차세대 소재 특성 분석과 발견을 가속화하고 있습니다.
자세한 연구 분야 및 결과는 출간 논문을 확인하세요.
- Electrochemical interfaces
- Defects at surfaces & interfaces
- Crystalline/amorphous oxides (e.g., TiO2, ZrO2, RuO2, and BiVO4) with defects, their surfaces & interfaces with electrolyte
- Battery electrodes & their interfaces with electrolyte
- Hybrid perovskites with defects & their surfaces and heterojunctions
- DFT and hybrid functional calculations
- Ab initio molecular dynamics & molecular dynamics with machine learning interatomic potentials
- Data mining & High-throughput screening
Open Positions: See this page.