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Computing vibrational eigenstates with tree tensor network states (TTNS)

Larsson, Henrik R. (2019) Computing vibrational eigenstates with tree tensor network states (TTNS). Journal of Chemical Physics, 151 (20). Art. No. 204102. ISSN 0021-9606. doi:10.1063/1.5130390.

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We present how to compute vibrational eigenstates with tree tensor network states (TTNSs), the underlying ansatz behind the multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method. The eigenstates are computed with an algorithm that is based on the density matrix renormalization group (DMRG). We apply this to compute the vibrational spectrum of acetonitrile (CH₃CN) to high accuracy and compare TTNSs with matrix product states (MPSs), the ansatz behind the DMRG. The presented optimization scheme converges much faster than ML-MCTDH-based optimization. For this particular system, we found no major advantage of the more general TTNS over MPS. We highlight that for both TTNS and MPS, the usage of an adaptive bond dimension significantly reduces the amount of required parameters. We furthermore propose a procedure to find good trees.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Larsson, Henrik R.0000-0002-9417-1518
Additional Information:© 2019 Published under license by AIP Publishing. Submitted: 3 October 2019; Accepted: 1 November 2019; Published Online: 25 November 2019. The author is thankful to G. K. Chan, K. Gunst, and R. Haghshenas for helpful discussions. He acknowledges support from the German Research Foundation (DFG) via Grant No. LA 4442/1-1.
Funding AgencyGrant Number
Deutsche Forschungsgemeinschaft (DFG)LA 4442/1-1
Issue or Number:20
Record Number:CaltechAUTHORS:20191125-143618654
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Official Citation:Computing vibrational eigenstates with tree tensor network states (TTNS). Henrik R. Larsson. The Journal of Chemical Physics 151:20. doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100044
Deposited By: Tony Diaz
Deposited On:25 Nov 2019 22:54
Last Modified:16 Nov 2021 17:51

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