用于密度泛函开发的智能体AI：重新审视r2SCAN
Agentic AI for Density-Functional Development: Revisiting r2SCAN
- PsiQuantum
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用LLM智能体在物理约束下搜索并优化r2SCAN泛函，提出r2SCAN+，显著降低带隙和分子性质误差，证明智能体搜索可有效辅助密度泛函开发。
AI中文摘要：
我们展示了使用大型语言模型（LLM）辅助搜索和优化面向带隙的r2SCAN修正的物理约束智能体开发元广义梯度近似（meta-GGA）泛函。未拟合任何真实成键体系，从而保留了r2SCAN的非经验哲学。在最终候选集中，带隙和若干分子子集相对于r2SCAN有所改善；最佳候选r2SCAN+将包含24个固体的基准上的带隙平均绝对误差（MAE）从1.26 eV降至0.96 eV，并将329个分子性质上的总体MAE从4.79 kcal/mol降至4.42 kcal/mol。我们首先从r2SCAN的无量纲成分中筛选出78个交换和90个关联候选修正项，涵盖三次多项式、指数、指数衰减乘积及比值。允许每个候选组合来自交换目录、关联目录或两者的1至3个修正项，产生约8×10^5种不同形式，使得穷举高通量筛选不切实际。我们使用r2SCAN的精确约束、物理规范和目标的等轨道导数响应定义了LLM智能体的搜索标准。随后，智能体将这些标准与其预训练知识和累积的搜索反馈相结合，提出并优化稀疏形式，优先考虑与等轨道响应相关的项；第二个LLM评论者在确定性验证前筛选提案。与均匀随机搜索相比，该工作流从先前评估中学习，产生的下游拒绝更少（0.6%对24.6%），并找到了更强的高响应候选：51个智能体候选超过了最佳随机搜索响应1.263，总体最佳达到1.331。这些结果表明，当灵活假设生成与自动物理验证相结合时，智能体搜索可以支持密度泛函开发。
英文摘要：
We demonstrate physics-constrained agentic development of a meta-generalized gradient approximation (meta-GGA) functional using a large language model (LLM) to assist the search and optimization of a band-gap-oriented revision of r2SCAN. No real bonded systems were fitted, preserving r2SCAN's nonempirical philosophy. Across the finalist set, band gaps and several molecular subsets improve relative to r2SCAN; the top finalist, r2SCAN+, reduces the band-gap MAE on a benchmark comprising 24 solids from 1.26 to 0.96 eV and the aggregate MAE on 329 molecular properties from 4.79 to 4.42 kcal/mol. We first curated 78 exchange and 90 correlation candidate correction terms from r2SCAN's dimensionless ingredients, spanning polynomial terms through third degree, exponentials, exponentially damped products, and ratios. Allowing each candidate to combine one to three correction terms from the exchange catalog, the correlation catalog, or both yields about 8 x 10^5 distinct forms, making exhaustive high-throughput screening impractical. We defined the search criteria for the LLM agent using r2SCAN's exact constraints, physical norms, and the targeted iso-orbital derivative response. The agent then combined these criteria with its pretrained knowledge and accumulated search feedback to propose and refine sparse forms, prioritizing terms tied to the iso-orbital response; a second LLM critic screened proposals before deterministic verification. Compared with uniform random search, the workflow learned from prior evaluations, incurred far fewer downstream rejections (0.6% versus 24.6%), and located stronger high-response candidates: 51 agentic candidates exceeded the best random-search response of 1.263, with the overall best reaching 1.331. These results show that agentic search can support density-functional development when flexible hypothesis generation is coupled to automated physical verification.
