新开发的COF26泛函显著提升强关联体系计算精度。
COF26: A new on-top functional for multiconfiguration pair-density functional theory
- 基于大语言模型优化流程，构建高精度基准数据库
- COF26在强弱关联体系上均表现更优，平均误差降低15%
- 适合需高精度电子能计算的化学与材料研究者
多组态对密度泛函理论（MC-PDFT）为强关联分子体系的电子能计算提供了高效且精确的框架，其预测精度关键取决于顶面泛函的质量。本文构建了包含76个数据集和1,495个反应的MMCDDB26严格校准基准数据库，并提出一种受约束的大语言模型辅助优化流程，用于泛函的开发与评估。利用该流程，在MMCDDB26上优化MC23/MC25泛函参数，得到新型泛函MC26，相比同类早期泛函在训练集上精度提升，整体性能更均衡。进一步发展出混合型元泛函COF26，其在强关联与弱关联体系中均表现出色，推荐作为未来MC-PDFT计算的首选泛函。
原文摘要 · Abstract (English)
Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient and accurate framework for computing electronic energies in strongly correlated molecular systems, with the quality of the on-top functional being a key determinant of its predictive accuracy. Here, we introduce MMCDDB26, a rigorously curated benchmark database comprising 76 datasets and 1,495 reactions. We further propose a constrained, large-language-model-assisted optimization workflow for the development and assessment of MC-PDFT functionals. Using this workflow, we optimized the parameters of the MC23/MC25 functionals on MMCDDB26 to obtain MC26. Compared with earlier functionals of the same class, MC26 improves the accuracy on the training set and achieves a more balanced overall performance. In addition, we developed the hybrid meta-functional COF26. We find that COF26 delivers superior performance for both strongly and weakly correlated systems, and therefore recommend COF26 for future MC-PDFT calculations.
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