3 papers
cs.LG2026
Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents
Kyungmin Nam, Seunghee Han, Jihan Kim
Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine-learning models reveal little…
cond-mat.mtrl-sci2026
EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture
Seunghee Han, Yeonghun Kang, Taeun Bae +5
Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generativ…
cond-mat.mtrl-sci2026
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks
Seunghee Han, Byeong Gwan Lee, Dae Woon Lim +1
Recently, metal-organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported…