论文标题
加速的多模型牛顿型算法，用于更快地接合和激发状态耦合群集方程
Accelerated multimodel Newton-type algorithms for faster convergence of ground and excited state coupled cluster equations
论文作者
论文摘要
我们介绍了一种多模型方法来求解耦合群集方程，该群集方程采用了准牛顿算法的基态和激发态的OLSEN算法。在这些算法中，这两种算法都可以看作是牛顿算法，在与目标模型相关的牛顿方程中使用了较低级耦合群集模型的雅各布矩阵。因此，收敛的改善意味着为足够大的分子系统节省了节省，因为宏观宏观的计算成本比微量制度的成本更陡峭。当有较低级别的雅各布矩阵时，多模型方法是合适的，它比零阶近似更准确。使用雅各布式的CCSD近似，将方法应用于CC3方程，我们表明，确定地面和价兴奋状态所花费的时间可以显着降低。我们还发现了核心激发态的改善的收敛性，表明将通过明确实施核心分离的CCSD jacobian变换获得类似的节省。
We introduce a multimodel approach to solve coupled cluster equations, employing a quasi Newton algorithm for the ground state and an Olsen algorithm for the excited states. In these algorithms, both of which can be viewed as Newton algorithms, the Jacobian matrix of a lower level coupled cluster model is used in Newton equations associated with the target model. Improvements in convergence then implies savings for sufficiently large molecular systems, since the computational cost of macroiterations scales more steeply with system size than the cost of microiterations. The multimodel approach is suitable when there is a lower level Jacobian matrix that is much more accurate than the zeroth order approximation. Applying the approach to the CC3 equations, using the CCSD approximation of the Jacobian, we show that the time spent to determine the ground and valence excited states can be significantly reduced. We also find improved convergence for core excited states, indicating that similar savings will be obtained with an explicit implementation of the core-valence separated CCSD Jacobian transformation.
