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<style> span { font-size: 15px; word-break: keep-all } li{ margin: 5px 0; font-size: 15px; } .spaced-para { margin-bottom: 0.2cm; } </style> Welcome to Computational Materials & Interfaces Research Group in the [Division of Advanced Materials Engineering](https://wz3.jbnu.ac.kr/sites/materialseng/index..do), Jeonbuk National University (JBNU), S. Korea. The "Computational Materials & Interfaces Research Group" at JBNU, led by Professor [Taehun Lee](/people), employs computational simulation approaches to analyze and predict the properties of materials. Our primary focus is on semiconductor materials, particularly on defects, and their applications in various fields such as photocatalysts, batteries, and semiconductor devices. We are committed to investigating the electronic and dynamical properties of semiconducting materials at interfaces, including vacuum/surface and electrolyte/electrode interfaces, and heterojunctions between different materials. Additionally, our research explores 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](/publications).

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안녕하세요. 전북대학교 신소재공학부 [전자재료전공](https://materials.jbnu.ac.kr/materials/index.do) "재료 & 계면 전산 모사 연구실" (지도 교수: [이태훈](/people))
홈페이지 입니다. 저희 연구실은 재료와 재료의 계면 특성을 분석하고 예측하기 위해 전산 모사 기법을 활용하고 있습니다. 특히 (광)촉매, 배터리, 반도체 소자 시스템을 모사하며, 그 소자 내에서의 결함이 소자에 미치는 영향에 대해서 연구하고 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 논문](/publications)을 확인하세요.

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Research topics

• Electrochemical interfaces
• Defects at surfaces & interfaces

Materials & interfaces

• Crystalline/amorphous oxides (e.g., TiO₂, ZrO₂, RuO₂, and BiVO₄) with defects, their surfaces & interfaces with electrolyte
• Battery electrodes & their interfaces with electrolyte
• Realstic semicoductor systems with defects & their surfaces and heterojunctions

Methods

• DFT and hybrid functional calculations
• Ab initio molecular dynamics & molecular dynamics with machine learning interatomic potentials using advanced sampling method
• Data mining & High-throughput screening

Applications: (Photo)electrochemical catalysts, Electronic devices, and Li/Na-ion batteries.

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Open Positions: See this [page](/open).