18 citations · 19 across the 3 of their papers we have counts for
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 navigating combinatorial spaces with costly property labels and opaque machine-learning models. We introduce LLM4MOF, a c…
cond-mat.mtrl-sci2025★ 1 cited
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-sci2024★ 18 cited
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…