SimMOF: AI agent for Automated MOF Simulations

Computer Science > Artificial Intelligence [Submitted on 31 Mar 2026] SimMOF: AI agent for Automated MOF Simulations Jaewoong Lee, Taeun Bae, Jihan Kim Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties. However, MOF simulations remain difficult to access because reliable analysis require expert decisions for workflow construction, parameter selection, tool interoperability, and the preparation of computational ready structures. Here, we introduce SimMOF, a large language model based multi agent framework that automates end-to-end MOF simulation workflows from natural language queries. SimMOF translates user requests into dependency aware plans, generates runnable inputs, orchestrates multiple agents to execute simulations, and summarizes results with analysis aligned to the user query. Through representative case studies, we show that SimMOF enables adaptive and cognitively autonomous workflows that reflect the iterative and decision driven behavior of human researchers and as such provides a scalable foundation for data driven MOF research. Comments: 33 pages, 6 figures, 2 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.29152 [cs.AI]   (or arXiv:2603.29152v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29152 Focus to learn more Submission history From: JaeWoong Lee [view email] [v1] Tue, 31 Mar 2026 02:08:50 UTC (1,908 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)