arXiv cs.AI· Chen Yuntong, Huang Lijiang, Li Yingqi, Liu Yu, Zhao Dan, Sun Mingqi, Ju Chentian, Liu Yanbing, Li Xiao-Yan, Zhao Guobin·· 3 小时前AI 评分44
DREAMS：用于端到端计算材料发现的 LLM 智能体系统
An LLM agent for end-to-end computational materials discovery
AI 导读
研究者推出 DREAMS，一个基于 LLM 的智能体系统，可端到端执行金属有机框架（MOFs）的整套计算筛选流程。它处理大量 MOF 文献、将论文关联到晶体结构并整理成可直接计算的数据库，再用成本递增的模拟逐级筛选。筛出的湿烟气分离候选材料均来自独立研究，非专家也能借此自动化发现高性能材料。
正文 · 原文
Abstract:Coordinating multi-scale tasks is an effective strategy for computational materials discovery, yet the diverse algorithms and tools involved make this process highly challenging. Here we present DREAMS, a large language model (LLM)-based agent system capable of executing the entire screening pipeline for metal-organic frameworks (MOFs). DREAMS processes a large body of MOF literature, links publications to their crystal structures, and curates them into a computation-ready database, which is then screened with progressively more expensive simulations. The promising candidates identified for separation under wet flue gas conditions all originate from independent studies. By bridging the heterogeneous stages from literature mining to high-throughput simulation, DREAMS operates across application domains and enables even non-experts to discover high-performance materials in an end-to-end, automated manner.
| Subjects: | Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.20434 [cond-mat.mtrl-sci] |
| (or arXiv:2608.20434v2 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2608.20434 arXiv-issued DOI via DataCite |
Submission history
From: Guobin Zhao [view email]
[v1]
Thu, 20 Aug 2026 06:23:09 UTC (4,930 KB)
[v2]
Fri, 23 Oct 2026 10:37:02 UTC (11,279 KB)
来源：arXiv cs.AI · arxiv.org