2 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…