无自动微分的激发态平均场论
Excited State Mean-Field Theory without Automatic Differentiation
摘要：
我们提出了一个配方的激发态平均场论，其中的衍生物相对于波函数优化所需的波函数参数（不要混淆与核衍生物）的福克矩阵的集合解析表示。通过避免使用自动微分和分组Fock构建在一起，我们发现，我们必须访问内存密集型的双电子积分的次数可以大大减少。此外，新的配方允许理论利用现有的策略，有效的福克矩阵的建设。我们通过壳对筛选策略明确地证明了这一优势，利用该策略，我们实现了立方整体成本缩放。使用这种更有效的实现，我们还研究了理论的能力，预测电荷转移激发过程中的电荷再分配。以耦合团簇为基准，我们发现激发态平均场论通过捕捉轨道弛豫效应和避免自相互作用误差，在预测电荷转移激发的电荷密度变化时，优于其他低成本方法.
英文摘要：
We present a formulation of excited state mean-field theory in which the derivatives with respect to the wave function parameters needed for wave function optimization (not to be confused with nuclear derivatives) are expressed analytically in terms of a collection of Fock-like matrices. By avoiding the use of automatic differentiation and grouping Fock builds together, we find that the number of times we must access the memory-intensive two-electron integrals can be greatly reduced. Furthermore, the new formulation allows the theory to exploit existing strategies for efficient Fock matrix construction. We demonstrate this advantage explicitly via the shell-pair screening strategy, with which we achieve a cubic overall cost scaling. Using this more efficient implementation, we also examine the theory's ability to predict charge redistribution during charge transfer excitations. Using coupled cluster as a benchmark, we find that by capturing orbital relaxation effects and avoiding self-interaction errors, excited state mean field theory out-performs other low-cost methods when predicting the charge density changes of charge transfer excitations.
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