Large language model agents accelerate inverse design of metal-organic frameworks for gas separation
Metal-organic frameworks (MOFs) offer a highly modular platform for adsorptive gas separation, yet their vast reticular design space makes inverse design difficult under simultaneous constraints of chemical validity, separation performance, and structural diversity. Here, we present LEMO Agent, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space. LEMO Agent couples language-based candidate generation with MOFid standardization, explicit validity checking, Transformer-based property prediction, structured design memory, and multi-island exploration. Through iterative generate--validate--evaluate--remember cycles, the agent uses feedba...
| Authors | Zhaolin Hu, Hehe Fan, Wangyihan Guo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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