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

利用树张量网络状态对乙腈的5000个精确本征态进行振动光谱基准测试

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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 2 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.

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