Chun research group

Computational Materials Intelligence Lab

We are a computational research group working at the interface of artificial intelligence and atomistic simulations, with a focus on the predictive design of functional materials. We develop AI-accelerated computational frameworks that enable efficient exploration of complex materials design spaces.

인공지능과 계산과학 기반 모델링 및 시뮬레이션을 활용하여 기능성 소재의 예측 설계를 목표로 합니다. 복잡한 소재 설계 및 탐색 과정을 가속화 하기 위한 인공지능 모델 및 플랫폼을 개발하고 있습니다.

Explore our research

Featured Publications

Harnessing AtomisticSkills for Agentic Atomistic Research
Harnessing AtomisticSkills for Agentic Atomistic Research
Bowen Deng, Bohan Li, Matthew Cox, Hoje Chun, Juno Nam, …, Abhirup Patra, Detlef Hohl, Connor W. Coley, Ju Li, Rafael Gómez-Bombarelli
arXiv  ·  01 Jan 2026  ·  doi:10.48550/arXiv.2605.24002
machine-learning methods
Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-Informed Neural Network
Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-Informed Neural Network
Hoje Chun, Minjoon Hong, Seung Hyo Noh, Byungchan Han
Journal of Chemical Theory and Computation 21 (8), 4030-4039  ·  14 Apr 2025  ·  doi:10.1021/acs.jctc.5c00090
machine-learning methods
Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis
Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis
Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, Byungchan Han
npj Computational Materials 10 (1)  ·  19 Oct 2024  ·  doi:10.1038/s41524-024-01432-1
machine-learning energy-materials catalysis
Deep Learning of Mean First Passage Time Scape: Chemical Short-Range Order and Kinetics of Diffusive Relaxation
Deep Learning of Mean First Passage Time Scape: Chemical Short-Range Order and Kinetics of Diffusive Relaxation
Hoje Chun, Hao Tang, Bin Xing, Rafael Gomez-Bombarelli, Ju Li
arXiv  ·  01 Jan 2024  ·  doi:10.48550/arXiv.2411.17839
machine-learning methods
First-principle-data-integrated machine-learning approach for high-throughput searching of ternary electrocatalyst toward oxygen reduction reaction
First-principle-data-integrated machine-learning approach for high-throughput searching of ternary electrocatalyst toward oxygen reduction reaction
Hoje Chun, Eunjik Lee, Kyungju Nam, Ji-Hoon Jang, Woomin Kyoung, Seung Hyo Noh, Byungchan Han
Chem Catalysis 1 (4), 855-869  ·  01 Sep 2021  ·  doi:10.1016/j.checat.2021.06.001
machine-learning energy-materials catalysis
See all publications

Latest Updates

Honors & achievements

Research project
Research project
April 2026
achievements

Our group has been selected for the research project by the National Research Foundation (NRF) of Korea.

Research project
Research project
April 2026
achievements

Our group has started a research project funded by the Kookmin University.

Lab life & activities

Welcome Dong Hyeon and Yena
Welcome Dong Hyeon and Yena!
May 2026
lab-life

Dong Hyeon and Yena from Kookmin University joined our group as an undergraduate intern!

Welcome Jaeyoung
Welcome Jaeyoung!
May 2026
lab-life

Jaeyoung from Yonsei University (Prof. Aloysius Soon group) joined our group as a co-advised graduate student!

Welcome Jun Hyeok
Welcome Jun Hyeok!
April 2026
lab-life

Jun Hyeok from Kookmin University joined our group as an undergraduate intern!

More news & posts

Join Our Team!

We welcome applications from motivated people with diverse backgrounds in Chemistry, Physics, Materials Science, Chemical Engineering, Computer Science, and energy engineering who are interested in computational materials chemistry.

인공지능과 계산과학 연구에 관심있는 화학, 물리, 신소재공학, 화학공학, 컴퓨터공학 등 다양한 전공의 학부생, 대학원생, 박사후연구원들의 지원을 환영합니다!

Recruitment details