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MINDS Lab

Materials Intelligence for
Design and Simulations

MINDS Lab at Korea University works on AI-driven materials design and discovery. We combine machine learning, atomistic simulation, and autonomous discovery loops to understand how ions move in solids, polymers, and liquids, and to find new materials for batteries.

고려대학교 MINDS Lab은 AI 기반 소재 설계 및 탐색을 연구합니다. 머신러닝, 원자 단위 시뮬레이션, 자율 탐색을 결합해 고체·고분자·액체 내 이온 이동을 이해하고 새로운 배터리 소재를 찾습니다.

School of Mechanical Engineering · School of Smart Mobility
기계공학부 · 스마트모빌리티학부

ResearchPublicationsRecruiting

Research

All research

AI-driven autonomous materials discovery for batteries

How can machine-learning models, simulations, and experiments be connected so that each round chooses the next material to make and test?

Machine-learning interatomic potentials and accelerated simulation

How can simulations keep first-principles accuracy while reaching nanosecond time scales and nanometer length scales?

Ion transport mechanisms in superionic conductors

Why do some crystal structures conduct Li and Na ions orders of magnitude faster than others?

Mechanistic decomposition of transport in liquid, polymer, and amorphous electrolytes

Which microscopic events carry the charge in an electrolyte, and how much does each contribute to the measured conductivity?

High-throughput and machine-learning-guided materials discovery

Which compositions are fast ion conductors and remain stable under operating conditions?

Recent publications

All publications
Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State ConductorsLuca Binci, KyuJung Jun, Bowen Deng, Gerbrand CederAdvanced Energy Materials (2026)
DOIarXiv
Computational discovery of polymer electrolytes with Bayesian optimization and high-throughput molecular dynamics simulationsAntonia S Kuhn, Jurğis Ruža, KyuJung Jun, Pablo Leon, Rafael Gómez-BombarelliMatter (2026)
DOIarXiv
Universal Framework for Decomposing Ionic Transport into Interpretable MechanismsKyuJung Jun*, Pablo A Leon*, Jurğis Ruža, Juno Nam, Rafael Gómez-BombarelliarXiv (2026)
arXivSelected
The free energy landscape of Li- and Na-ion transport in nanoconfinement with machine learning interatomic potentialsSauradeep Majumdar, Swagata Roy, KyuJung Jun, Miguel Steiner, Rafael Gómez-BombarelliChemistry of Materials, 38 (8) (2026)
DOI
Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculationsZhuohan Li*, KyuJung Jun*, Bowen Deng, Gerbrand CederCell Press Blue (2026)
DOI

News

All news

Junho Lim started his Ph.D. in the College of Chemistry at UC Berkeley (Bingqing Cheng group). He worked with us as a visiting student on machine-learning potentials for ion transport in amorphous oxyhalide electrolytes.

Doheon Yeom (B.S. student, School of Smart Mobility) joined the group as an undergraduate researcher.

Oral presentation at the Korean Society of Industrial and Engineering Chemistry 2026 Spring Meeting (Jeju): “Understanding and designing ion conductors via machine learning”.

Oral presentation at the Korean Institute of Metals and Materials 2026 Spring Meeting (Jeju): “Developing universal algorithms for decomposing ionic transport into interpretable mechanisms”.

Junho Lim joined the group as a visiting student.

People

Members and alumni

Principal Investigator: KyuJung Jun (전규정), Assistant Professor. Ph.D., UC Berkeley; postdoctoral researcher, MIT.

KyuJung JunAssistant ProfessorHyeon-jong LeeExchange Ph.D. student (KAIST DMSE)Doheon YeomUndergraduate researcher

Recruiting

We are recruiting M.S. and Ph.D. students, undergraduate researchers, visiting students, and postdocs. Backgrounds in mechanical engineering, materials science, chemical engineering, physics, chemistry, energy engineering, and related fields are all welcome.

연구실 모집

석·박사과정 대학원생, 학부연구생, 방문연구생, 박사후연구원을 모집합니다. 기계공학, 신소재공학, 화학공학, 물리학, 화학, 에너지공학 등 다양한 전공의 지원을 환영합니다.

Details / 자세히 보기kyujung@korea.ac.kr