Department of Chemistry, University of Colorado Denver, Denver, Colorado 80217-3364, USA and Beijing Computational Science Research Center, No. 10 East Xibeiwang Road, Haidian District, Beijing 100193, China.
Theoretische Chemie, Physikalisch-Chemisches Institut, Universität Heidelberg, Im Neuenheimer Feld 229, D-69120 Heidelberg, Germany.
J Chem Phys. 2018 Jul 28;149(4):044119. doi: 10.1063/1.5042776.
In a recent paper [H.-D. Meyer and H. Wang, J. Chem. Phys. 148, 124105 (2018)], we have examined the regularization of the equations of motion (EOMs) of the multiconfiguration time-dependent Hartree (MCTDH) approach. We could show that the standard regularization scheme used by almost all researchers in the field is not optimal. The improved regularization allows for larger values of the regularization parameter ϵ, is less sensitive to the actual choice of ϵ, and performs the rotation of initially unoccupied single-particle functions into the "correct" direction in Hilbert space much faster than the old scheme. The latter point increases both the accuracy and efficiency of time propagation for challenging problems. For simple problems, the new scheme requires some additional numerical work as compared with the old scheme, ranging from negligible to almost doubling the total numerical labor. For demanding problems, on the other hand, the additional numerical work of the new scheme is often overcompensated by less steps taken by the integrator. In the present paper, we generalize the new regularization scheme to the multi-layer (ML) extension of MCTDH. Although the principle idea of the new regularization scheme remains unaltered, it was not obvious how the new scheme should be implemented into ML-MCTDH. The ML-MCTDH EOMs are much more complicated than the MCTDH ones, and for optimal numerical performance it was necessary to derive a recursive algorithm for implementing the new regularization scheme.
在最近的一篇论文[H.-D. Meyer 和 H. Wang, J. Chem. Phys. 148, 124105 (2018)]中,我们研究了多组态含时哈特ree(MCTDH)方法的运动方程(EOM)正则化问题。我们证明了该领域几乎所有研究人员使用的标准正则化方案并不是最优的。改进后的正则化方案允许使用更大的正则化参数ϵ,对实际选择ϵ的不敏感,并且比旧方案更快地将初始未占据的单粒子函数旋转到 Hilbert 空间中的“正确”方向。后一点提高了具有挑战性问题的时间传播的准确性和效率。对于简单的问题,与旧方案相比,新方案需要增加一些额外的数值工作,从可以忽略不计到几乎增加总数值工作量的两倍。另一方面,对于要求苛刻的问题,新方案的额外数值工作通常会被积分器采用的步数减少所补偿。在本文中,我们将新的正则化方案推广到 MCTDH 的多层(ML)扩展。尽管新正则化方案的基本思想保持不变,但将新方案实施到 ML-MCTDH 中并不明显。ML-MCTDH 的 EOM 比 MCTDH 的复杂得多,为了获得最佳的数值性能,有必要为实施新的正则化方案推导出一个递归算法。