Physical AI for Transfomative Materials Discovery

Choe group develops algorithm-driven, target-oriented functional materials by integrating synthesis, characterization, and iterative optimization to achieve breakthrough functions. We focus on reticular materials and porous crystalline solids—including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolitic imidazolate frameworks (ZIFs), metal–organic polyhedra (MOPs), and zeolites—where precise structural control enables programmable properties. A central task of our research is to embed algorithms directly into the discovery process. We build closed-loop workflows that connect experimental data to decision-making, enabling the next round of design to be guided by quantitative learning rather than human intuition alone. By transforming trial-and-error into an adaptive and reproducible discovery engine, we aim to accelerate how new materials are conceived, validated, and refined from fundamental design principles to measurable function. In doing so, we seek to bridge imagination and implementation—bringing dream materials to real-world function.