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arXiv 2402.03532physics.chem-ph
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通过耦合轨道与占据数优化增强约化密度矩阵泛函理论计算

Enhancing Reduced Density Matrix Functional Theory Calculations by Coupling Orbital and Occupation Optimizations

Yi-Fan Yao, Neil Qiang Su

AI总结:

本文针对约化密度矩阵泛函理论中轨道与占据数解耦优化收敛慢的问题,提出将酉优化与EBI优化耦合并在每步同时更新两者的新方法,配合预条件子和线搜索,显著提升了收敛速度、结果和稳定性,使RDMFT计算更加实用。

摘要:

约化密度矩阵泛函理论(RDMFT)计算通常以解耦方式实现,其中轨道优化和占据数优化交替重复进行。通常,轨道更新采用酉优化方法,而占据数则通过显式转隐式(EBI)方法进行优化。EBI方法通过引入隐式函数来处理显式约束,从而有效地将约束优化问题转化为无约束最小化问题。尽管酉方法和EBI方法各自分别在优化轨道和占据数方面具有稳健的性能,但解耦优化方法往往收敛缓慢,并且需要在轨道优化和占据数优化之间进行数十次交替。为解决这一问题,本工作提出了一种耦合优化方法,将酉优化和EBI优化相结合,在每一步同时更新轨道和占据数。为了在使用简单的一阶算法时实现耦合优化的良好收敛性,进一步引入了一种有效且高效的预条件子和线搜索。通过对不同分子、随机初始猜测、不同基组和不同泛函的大量测试,证明了新方法的优越性。该方法在收敛速度、收敛结果和收敛稳定性方面均优于所有解耦优化方法。即使是像$\mathrm{C_{60}}$这样的大体系,也能在154次迭代中收敛到$10^{-8}$ au,这表明耦合优化方法可以使RDMFT更加实用,并促进其更广泛的应用和进一步发展。

英文摘要:

Reduced density matrix functional theory (RDMFT) calculations are usually implemented in a decoupled manner, where the orbital and occupation optimizations are repeated alternately. Typically, orbital updates are performed using the unitary optimization method, while occupations are optimized through the explicit-by-implicit (EBI) method. The EBI method addresses explicit constraints by incorporating implicit functions, effectively transforming constrained optimization scenarios into unconstrained minimizations. Although the unitary and EBI methods individually achieve robust performance in optimizing orbitals and occupations, respectively, the decoupled optimization methods often suffer from slow convergence and require dozens of alternations between the orbital and occupation optimizations. To address this issue, this work proposes a coupled optimization method that combines unitary and EBI optimizations to update orbitals and occupations simultaneously at each step. To achieve favorable convergence in coupled optimization using a simple first-order algorithm, an effective and efficient preconditioner and line search are further introduced. The superiority of the new method is demonstrated through numerous tests on different molecules, random initial guesses, different basis sets and different functionals. It outperforms all decoupled optimization methods in terms of convergence speed, convergence results and convergence stability. Even a large system like $\mathrm{C_{60}}$ can converge to $10^{-8}$ au in 154 iterations, which shows that the coupled optimization method can make RDMFT more practical and facilitate its wider application and further development.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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