论文标题
激发状态平均场理论没有自动分化
Excited State Mean-Field Theory without Automatic Differentiation
论文作者
论文摘要
我们提出了激发态平均场理论的表述，其中衍生物相对于波函数优化所需的波函数参数（不要与核衍生物混淆）在分析中以类似Fock样矩阵的收集来表达。通过避免使用自动分化并将Fock构建分组在一起，我们发现我们必须访问内存密集的两电子积分的次数可以大大减少。此外，新的配方允许该理论利用现有策略进行有效的Fock矩阵结构。我们通过Shell-Pair筛选策略明确证明了这一优势，我们通过该策略实现了立方的总成本缩放。使用这种更有效的实施，我们还研究了该理论在电荷转移激发期间预测电荷重新分布的能力。使用耦合群集作为基准，我们发现，通过捕获轨道弛豫效应并避免自我相互作用误差，激发态平均场理论在预测电荷传递激励的电荷密度变化时会超过其他低成本方法。
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.
