发表机构
Rutgers University-New Brunswick（罗格斯大学新布朗斯维克分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出关联感知智能体框架CAFES，结合关联诊断、自适应方法选择和LLM辅助，解决强关联系统多体模拟中的软件碎片化与方法选择难题，并通过三个研究案例验证其有效性。
AI 中文摘要
我们提出了CAFES，一个用于强关联系统电子结构模拟的关联感知智能体框架。CAFES解决了两个挑战：多体计算软件生态的碎片化，以及在不同关联区域选择合适方法的困难。它结合了关联诊断、自适应方法选择和大型语言模型（LLM）辅助，适用于分子、晶体和模型哈密顿系统。一种研究-任务架构将研究级规划与任务级执行分离，而一个精选的科学知识层为方法选择、工作流设计和结果解释提供可复用的指导。我们通过三项研究级研究展示了CAFES：使用DMRG-CASSCF计算叶黄素的低激发电子态，使用DMET探测扩展蜂窝状Hubbard模型中的相竞争，以及生成带有CCSD标签的量子化学数据集。这些计算展示了智能体工作流在强关联电子结构问题上的潜力，而这一领域在现有的智能体计算框架中受到的关注有限。
英文摘要
We present CAFES, a correlation-aware agentic framework for electronic-structure simulations of strongly correlated systems. CAFES addresses two challenges: the fragmented software landscape for many-body calculations and the difficulty of selecting appropriate methods across diverse correlation regimes. It combines correlation diagnostics, adaptive method selection, and large language model (LLM) assistance for molecular, crystalline, and model-Hamiltonian systems. A study-task architecture separates study-level planning from task-level execution, while a curated scientific knowledge layer provides reusable guidance for method selection, workflow design, and result interpretation. We demonstrate CAFES through three research-level studies: calculating the low-lying electronic states of lutein using DMRG-CASSCF, probing phase competition in the extended honeycomb Hubbard model using DMET, and generating a quantum-chemical dataset with CCSD labels. These calculations demonstrate the potential of agentic workflows for strongly correlated electronic-structure problems, a regime that has received limited attention in existing agentic computational frameworks.
