Research

We study material properties in functional materials and devices, including ferroelectric, 2D/optical, and hydrogen storage materials, using various imaging techniques combined with machine learning algorithms. 

In particular, we study material properties in the applications of information and energy technologies by advanced atomic force microscopy (AFM, 원자힘현미경) techniques combined with machine learning algorithms, and further pursue AI-driven materials discovery and AI-agentic autonomous experimentation for accelerated materials research.

We study the AI-driven design and optimization of HfO₂-based ferroelectric materials and devices by integrating multi-fidelity data from the literature and experiments with machine learning algorithms.

In particular, our research project aims to predict promising material compositions and process conditions for achieving high remanent polarization and low coercive field, and further establish a closed data feedback loop connecting AI design, thin-film synthesis, structural, electrical, and nanoscale characterization, and device verification for accelerated ferroelectric materials research.

We develop agentic AI and autonomous experiment frameworks for AFM that autonomously perceive, plan, and execute measurements through a modular, layered architecture spanning tool control, perception, and reusable experimental skills. In particular, we implement a continuous AI-driven decision loop (hypothesis → measurement plan → validation → update) combined with structured experimental memory across multiple measurements, enabling closed-loop, self-improving autonomous AFM experimentation. 

We synthesize dielectric and ferroelectric ceramic powders via solid-state reaction, encompassing raw material mixing, calcination, and sintering to achieve high phase purity and density. In particular, we fabricate dielectric and ferroelectric thin films through atomic layer deposition (ALD), followed by post-deposition annealing and device fabrication processes. Rigorous characterization ensures consistent electrical performance, reliable thin-film quality, and excellent reproducibility for functional dielectric and ferroelectric materials. 

We study hydrogen storage, electrochemical energy, and catalytic materials, with a focus on understanding structural and chemical evolution during hydrogenation and electrochemical reactions. We employ AFM and electron microscopy techniques to analyze nanoscale surface degradation and reaction mechanisms in hydrogen storage alloys and electrocatalysts.