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.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
