Wang Yizhen, Lin Zihan, Ouyang Runhai, Jiang Bin, Zhang Igor Ying, Xu Xin
Shanghai Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative Innovation Centre of Chemistry for Energy Materials, MOE Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China.
Materials Genome Institute, Shanghai University, Shanghai 200444, China.
JACS Au. 2024 Aug 12;4(8):3205-3216. doi: 10.1021/jacsau.4c00488. eCollection 2024 Aug 26.
Accurate description of the static correlation poses a persistent challenge in electronic structure theory, particularly when it has to be concurrently considered with the dynamic correlation. We develop here a method in the generalized Kohn-Sham density functional theory (DFT) framework, named R-xDH7-SCC15, which achieves an unprecedented accuracy in capturing the static correlation, while maintaining a good description of the dynamic correlation on par with the state-of-the-art DFT and wave function theory methods, all grounded in the same single-reference black-box methodology. Central to R-xDH7-SCC15 is a general-purpose static correlation correction (SCC) model applied to the renormalized XYG3-type doubly hybrid method (R-xDH7). The SCC model development involves a hybrid machine learning strategy that integrates symbolic regression with nonlinear parameter optimization, aiming to achieve a balance between generalization capability, numerical accuracy, and interpretability. Extensive benchmark studies confirm the robustness and broad applicability of R-xDH7-SCC15 across a diverse array of main-group chemical scenarios. Notably, it displays exceptional aptitude in accurately characterizing intricate reaction kinetics and dynamic processes in regions distant from equilibrium, where the influence of static correlation is most profound. Its capability to consistently and efficiently predict the whole energy profiles, activation barriers, and reaction pathways within a user-friendly "black-box" framework represents an important advance in the field of electronic structure theory.
在电子结构理论中，对静态关联进行准确描述一直是一项具有挑战性的任务，尤其是当它必须与动态关联同时考虑时。我们在此开发了一种广义Kohn-Sham密度泛函理论（DFT）框架下的方法，名为R-xDH7-SCC15，该方法在捕捉静态关联方面实现了前所未有的精度，同时在描述动态关联方面与最先进的DFT和波函数理论方法相当，所有这些都基于相同的单参考黑箱方法。R-xDH7-SCC15的核心是一个应用于重整化XYG3型双杂化方法（R-xDH7）的通用静态关联校正（SCC）模型。SCC模型的开发涉及一种混合机器学习策略，该策略将符号回归与非线性参数优化相结合，旨在在泛化能力、数值精度和可解释性之间取得平衡。广泛的基准研究证实了R-xDH7-SCC15在各种主族化学场景中的稳健性和广泛适用性。值得注意的是，它在准确表征远离平衡区域的复杂反应动力学和动态过程方面表现出卓越的能力，在这些区域中静态关联的影响最为深远。它能够在用户友好的“黑箱”框架内一致且高效地预测整个能量分布、活化能垒和反应途径，这代表了电子结构理论领域的一项重要进展。
