Benchmarking Vibrational Spectra: 5000 Accurate Eigenstates of Acetonitrile Using Tree Tensor Network States

Abstract

Accurate vibrational spectra are essential for understanding how molecules behave, yet their computation remains challenging and benchmark data to reliably compare different methods are sparse. Here, we present high-accuracy eigenstate computations for the six-atom, 12-dimensional acetonitrile molecule, a prototypical, strongly coupled, anharmonic system. Using a density matrix renormalization group (DMRG) algorithm with a tree-tensor-network-state (TTNS) ansatz, a refinement using TTNSs as basis set, and reliable procedures to estimate energy errors, we compute up to 5,000 vibrational states with error estimates below 0.0007 cm-1. Our analysis reveals that previous works underestimated the energy error by up to two orders of magnitude. Our data serve as a benchmark for future vibrational spectroscopy methods and our new method offers a path toward similarly precise computations of large, complex molecular systems.