Assigning the vibrational spectra of cold clusters and molecules that have well-resolved features often requires the explicit consideration of anharmonic effects. The effects that have the most dramatic impact on the experimental spectra are often due to Fermi resonance. These resonances complicate the assignment of high-frequency XH-stretch spectra (X = C, N, O, in particular). For large systems such as molecular clusters, the brute force assignment of experimental spectra by computationally iterating over all candidate structures is prohibitive.<br/> <br/>In this work, we describe our efforts to automate spectroscopic assignments through a data-driven approach. Our method first assumes a local mode form of the Hamiltonian and then performs an active search through the space of physically reasonable anharmonicities that could be present in the system. By finding the Hamiltonian(s) that can best fit the spectra, the assignment can be automatically performed. This approach employs a Bayesian-optimization-derived algorithm as its driver and does not require a large volume of initial training data. In this talk, we focus on applying the method to model problems, higher-level calculations of benchmark systems, and real experimental spectra found in the literature. We then show our efforts to model uncertainty quantification and the model's performance for a family of isotopomers, a common technique used in the experimental literature.

Assigning the vibrational spectra of clusters directly from the spectrum

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