On regularizing the ML-MCTDH equations of motion
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 researcher...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
27 July 2018
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| In: |
The journal of chemical physics
Year: 2018, Volume: 149, Issue: 4 |
| ISSN: | 1089-7690 |
| DOI: | 10.1063/1.5042776 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1063/1.5042776 Verlag, Volltext: https://aip.scitation.org/doi/10.1063/1.5042776 
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| Author Notes: | Haobin Wang, and Hans-Dieter Meyer |
MARC
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| 520 | |a 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. | ||
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