AI RESEARCH
LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Design
arXiv CS.LG
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ArXi:2604.13520v1 Announce Type: new Metal-organic frameworks (MOFs) are highly promising for carbon capture, yet navigating their vast design space remains challenging. Recent deep generative models enable de novo MOF design but primarily act as feed-forward structure generators. By heavily relying on predefined building block libraries and non-differentiable post-optimization, they fundamentally sever the information flow required for continuous structural editing. Here, we propose a target-driven generative framework focused on continuous structural manipulation.