AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
AutoDFT is a new multi-agent framework designed to enhance autonomous DFT calculations in materials science. It integrates large language model reasoning throughout the DFT lifecycle, allowing for real-time adjustments and improved reliability. The framework has demonstrated high success rates in various tasks, making it accessible for experimentalists without deep computational expertise.
- ▪AutoDFT automates the entire DFT lifecycle, from planning to execution.
- ▪The framework achieved a 94.1% task-level success rate on the VASPBench benchmark.
- ▪AutoDFT enables reliable first-principles results for users lacking extensive computational knowledge.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26179 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
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Condensed Matter > Materials Science arXiv:2605.26179 (cond-mat) [Submitted on 25 May 2026] Title:AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations Authors:Penghui Yang, Zhonghan Zhang, Yue Li, Xinrun Wag, Yanchen Deng, Yuhao Lu, Bijun Tang, Zheng Liu, Bo An View a PDF of the paper titled AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations, by Penghui Yang and 8 other authors View PDF HTML (experimental) Abstract:Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
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