TritonDFT：基于多智能体框架的DFT自动化
TritonDFT: Automating DFT with a Multi-Agent Framework
AI总结：
TritonDFT通过多智能体框架实现DFT自动化，提供可扩展的工作流设计、帕累托感知参数推断和多源知识增强，提升DFT执行的效率和准确性。
摘要：
密度泛函理论（DFT）是材料科学的基石，但实际执行DFT需要协调复杂的多步骤工作流。现有工具和基于大语言模型的解决方案自动化了部分步骤，但缺乏对完整工作流自动化、多样化任务适应以及DFT配置中的精度-成本权衡优化的支持。为此，我们提出了TritonDFT，一种多智能体框架，通过专家整理、可扩展的工作流设计、帕累托感知的参数推断和多源知识增强，实现了高效且准确的DFT执行。我们进一步引入DFTBench，一个用于评估智能体多维能力的基准，涵盖科学专业知识、权衡优化、HPC知识和成本效率。TritonDFT提供了一个开放的用户界面用于实际应用。我们的网站位于https://www.tritondft.com。我们的源代码和基准套件可在https://github.com/Leo9660/TritonDFT.git获取。
英文摘要：
Density Functional Theory (DFT) is a cornerstone of materials science, yet executing DFT in practice requires coordinating a complex, multi-step workflow. Existing tools and LLM-based solutions automate parts of the steps, but lack support for full workflow automation, diverse task adaptation, and accuracy-cost trade-off optimization in DFT configuration. To this end, we present TritonDFT, a multi-agent framework that enables efficient and accurate DFT execution through an expert-curated, extensible workflow design, Pareto-aware parameter inference, and multi-source knowledge augmentation. We further introduce DFTBench, a benchmark for evaluating the agent's multi-dimensional capabilities, spanning science expertise, trade0off optimization, HPC knowledge, and cost efficiency. TritonDFT provides an open user interface for real-world usage. Our website is at https://www.tritondft.com. Our source code and benchmark suite are available at https://github.com/Leo9660/TritonDFT.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
