LEGO-MOF maps MOF linkers to an equivariant latent space for continuous editing and uses test-time optimization to achieve a 147.5% average boost in pure CO2 uptake while preserving structural validity.
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EGMOF uses a two-step modular workflow with a diffusion model (Prop2Desc) and transformer (Desc2MOF) to generate valid MOF structures from target properties, achieving over 94% validity and 91% hit rate even with 1,000 training samples across 29 datasets.
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LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Design
LEGO-MOF maps MOF linkers to an equivariant latent space for continuous editing and uses test-time optimization to achieve a 147.5% average boost in pure CO2 uptake while preserving structural validity.
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EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture
EGMOF uses a two-step modular workflow with a diffusion model (Prop2Desc) and transformer (Desc2MOF) to generate valid MOF structures from target properties, achieving over 94% validity and 91% hit rate even with 1,000 training samples across 29 datasets.