Accurate, full-dimensional computations of thousands of complex vibrational eigenstates with tree tensor network states

TL;DR

This paper demonstrates how tree tensor network states combined with DMRG enable accurate, full-dimensional calculations of thousands of vibrational eigenstates in complex molecules, advancing molecular spectroscopy simulations.

Contribution

It introduces a method leveraging TTNS and DMRG for large-scale vibrational eigenstate computations, connecting with ML-MCTDH and addressing practical challenges.

Findings

Successfully computed thousands of vibrational eigenstates in complex molecules.

Established the connection between TTNS-based DMRG and ML-MCTDH methods.

Discussed practical challenges and recent advances in excited state targeting.

Abstract

Tree tensor network states (TTNSs) combined with the density matrix renormalization group (DMRG) are emerging as powerful tools for vibrational and vibronic structure simulations in molecules with strong coupling and fluxionality. In this Perspective, we discuss how TTNS methods enable accurate, full-dimensional computations of thousands of eigenstates for molecular systems ranging from quartic-force-field benchmarks to molecules with strong vibronic coupling and protonated water clusters as large as the 33-dimensional Eigen ion, H$_3$O$^+$$\cdot$(H$_2$O)$_3$. We emphasize the close connection and interoperability between DMRG-based TTNS methods and the multilayer multiconfiguration time-dependent Hartree method (ML-MCTDH), which share the same underlying ansatz. We also highlight practical challenges of predictive simulations, including robust error estimation, convergence of observables such as infrared intensities, and optimization of tensor network tree structures. Finally, we outline recent advances toward direct targeting of excited states and discuss opportunities for broader applications in molecular spectroscopy and quantum dynamics.