Rotational Spectrum of Eugenol As Analyzed with Double Resonance and Grid-Based Autofit

Benchmarking the quadrupolar coupling tensor for chlorine to probe weak-bonding interactions

Rotational spectroscopy relies on quantum chemical calculations to interpret observed spectra. Among the most challenging molecules to assign are those with additional angular momenta coupling to the rotation, contributing to the complexity of the spectrum. This benchmark study of computational methods commonly used by rotational spectroscopists targets the nuclear quadrupole coupling constants of chlorine containing molecules and the geometry of its complexes and clusters. For each method, the quality of both structural and electronic parameter predictions is compared with the experimental values. Ab initio methods are found to perform best overall in predicting both the geometry of the complexes and the coupling constants of chlorine with moderate computational cost. This cost can be reduced by combining these methods with density functional theory structure optimization, which still yields adequate predictions. This work constitutes a first step in expanding Bailey's quadrupole coupling data set to encompass molecular clusters. [W. C. Bailey, Calculation of Nuclear Quadrupole Coupling Constants in Gaseous State Molecule, 2019, https://nqcc.wcbailey.net/].

Testing the Scalability of the HS-AUTOFIT Tool in a High-Performance Computing Environment

In the last years, the development of broadband chirped-pulse Fourier transform microwave spectrometers has revolutionized the field of rotational spectroscopy. Currently, it is possible to experimentally obtain a large quantity of spectra that would be difficult to analyze manually due to two main reasons. First, recent instruments allow obtaining a considerable amount of data in very short times, and second, it is possible to analyze complex mixtures of molecules that all contribute to the density of the spectra. AUTOFIT is a spectral assignment software application that was developed in 2013 to support and facilitate the analysis. Notwithstanding the benefits AUTOFIT brings in terms of automation of the analysis of the accumulated data, it still does not guarantee a good performance in terms of execution time because it leverages the computing power of a single computing machine. To cater to this requirement, we developed a parallel version of AUTOFIT, called HS-AUTOFIT, capable of running on high-performance computing (HPC) clusters to shorten the time to explore and analyze spectral big data. In this paper, we report some tests conducted on a real HPC cluster aimed at providing a quantitative assessment of HS-AUTOFIT’s scaling capabilities in a multi-node computing context. The collected results demonstrate the benefits of the proposed approach in terms of a significant reduction in computing time.

Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning

A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra as a method of recovering chemical and structural information encoded in the rotational spectrum. The eigenspectrum is subsequently used by three deep learning networks to constrain the range of stoichiometries, generate SMILES strings, and predict the most likely functional groups present in the molecule. In each model, we utilize dropout layers as an approximation to Bayesian sampling, which subsequently generates probabilistic predictions from otherwise deterministic models. These models are trained on a modestly sized theoretical dataset comprising ∼83 000 unique organic molecules (between 18 and 180 amu) optimized at the ωB97X-D/6-31+G(d) level of theory, where the theoretical uncertainties of the spectoscopic constants are well-understood and used to further augment training. Since chemical and structural properties depend strongly on molecular composition, we divided the dataset into four groups corresponding to pure hydrocarbons, oxygen-bearing species, nitrogen-bearing species, and both oxygen- and nitrogen-bearing species, training each type of network with one of these categories, thus creating "experts" within each domain of molecules. We demonstrate how these models can then be used for practical inference on four molecules and discuss both the strengths and shortcomings of our approach and the future directions these architectures can take.

Automated, context-free assignment of asymmetric rotor microwave spectra

We present a new algorithm, Robust Automated Assignment of Rigid Rotors (RAARR), for assigning rotational spectra of asymmetric tops. The RAARR algorithm can automatically assign experimental spectra under a broad range of conditions, including spectra comprised of multiple mixture components, in ≲100 s. The RAARR algorithm exploits constraints placed by the conservation of energy to find sets of connected lines in an unassigned spectrum. The highly constrained structure of these sets eliminates all but a handful of plausible assignments for a given set, greatly reducing the number of potential assignments that must be evaluated. We successfully apply our algorithm to automatically assign 15 experimental spectra, including 5 previously unassigned species, without prior estimation of molecular rotational constants. In 9 of the 15 cases, the RAARR algorithm successfully assigns two or more mixture components.