用AI自动发现更准的量子化学泛函，性能比顶尖模型高9%。
Agentic Discovery of Exchange-Correlation Density Functionals
- 用大模型生成泛函结构，通过迭代试错优化参数
- 新泛函SAFS26-a在热化学数据集上比ωB97M-V提升约9%
- 需人工约束防止模型走捷径，确保结果物理合理
交换关联（XC）泛函的精确构建是密度泛函理论（DFT）中长期存在的挑战。目前绝大多数泛函由研究人员结合物理直觉、精确约束和经验拟合手工设计。大语言模型的进展为这一人工设计流程提供了系统化、自动化的替代方案。本文提出一种智能体搜索系统，利用大模型基于进化历史提出结构化的泛函改进方案。系统通过计划-执行-总结的迭代循环，以标准热化学数据集优化泛函参数，并在保留子集上评估性能。所发现最强泛函SAFS26-a（Seed Agentic Functional Search 2026）相较黄金标准ωB97M-V基线性能提升约9%。该研究也揭示了人工智能辅助科学的一条警示：足够强大的模型既能发现真实改进，也能利用非物理解法欺骗基准测试；将领域知识转化为显式约束仍是确保结果科学可靠的关键。
原文摘要 · Abstract (English)
The development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT). The vast majority of XC functionals have been hand designed by human researchers combining physical insight, exact constraints, and empirical fitting. Recent advances in large language models enable a systematic, automated alternative to this human-driven design loop. This report presents an agentic search system in which an LLM proposes structured functional-form changes guided by evolutionary history. The system attempts to improve functional performance through an iterative plan-execute-summarize loop, where improvements are measurable by optimizing functional parameters against a standard thermochemistry dataset, then evaluating performance on a held-out subset. The strongest discovered functional, SAFS26-a (Seed Agentic Functional Search 2026), improves upon the gold-standard ωB97M-V baseline by ~9%. These results also surface a cautionary lesson for AI-assisted science: models powerful enough to discover genuine improvements are equally capable of exploiting unphysical shortcuts to game the benchmark; domain expertise translated into explicitly enforced constraints remains essential to keeping results scientifically grounded.
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