Towards a systematic convergence of Multi-Layer (ML) multi-configuration time-dependent hartree nuclear wavefunctions: the ML-spawning algorithm
The Multi-Layer (ML) variant of the Multi-Configuration Time-Dependent Hartree (MCTDH) method is a powerful tool for the efficient computation of nuclear quantum dynamics in high-dimensional systems. By providing an optimal choice of layered effective degrees of freedom in form of the so-called ML t...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
12 January 2017
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| In: |
Chemical physics
Year: 2017, Volume: 482, Pages: 113-123 |
| DOI: | 10.1016/j.chemphys.2016.08.031 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1016/j.chemphys.2016.08.031 Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S0301010416305134 
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| Author Notes: | David Mendive-Tapia, Thiago Firmino, Hans-Dieter Meyer, Fabien Gatti |
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| 520 | |a The Multi-Layer (ML) variant of the Multi-Configuration Time-Dependent Hartree (MCTDH) method is a powerful tool for the efficient computation of nuclear quantum dynamics in high-dimensional systems. By providing an optimal choice of layered effective degrees of freedom in form of the so-called ML tree, one is able to reduce the computational cost to an amenable number of configurations. Nevertheless, the fact that one must also make a series of ad hoc decisions often based on intuition or experience at the outset — such as the number of configurations per node and the branching of the ML tree — directly affect the efficiency of the computation and make its use less straightforward than the standard MCTDH method. Therefore, herein we detail a new algorithm for adaptively expanding the size of every node on-the-fly (i.e. spawning) and a derived criterion for the selection of an efficient tree’s branching. | ||
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