An LLM agent for end-to-end computational materials discovery
The coordination of multi-scale tasks is an effective strategy for computational materials discovery, yet the repeated application of diverse algorithms and tools renders it challenging. We report MAESTRO, a large language model (LLM) agent system capable of executing the entire screening pipeline for metal-organic frameworks (MOFs). It processes a large body of MOF literature, links relevant publications to their crystal structures, and curates the results into a computation-ready database, which is then screened through a strategy of progressively increasing computational cost. The promising candidates identified for separation under wet flue gas conditions all originate from unrelated ...
| Authors | Chen Yuntong, Huang Ju, Liu Yu et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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