Abstract
The interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical ...inventories. Previous work by Lee et al. demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as to provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a “unicorn” in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star-forming region: the Orion Kleinmann–Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have nonlinear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. to accurately reproduce the column densities from the XCLASS fitting program presented by Crockett et al.
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.
We report the study of three structural isomers of phenylpropiolonitrile (3-phenyl-2-propynenitrile, C
6
H
5
-C
3
N) containing an alkyne function and a cyano group, namely
ortho
-,
meta
-, and
para
...-cyanoethynylbenzene (HCC-C
6
H
4
-CN). The pure rotational spectra of these species have been recorded at room temperature in the millimeter-wave domain using a chirped-pulse spectrometer (75-110 GHz) and a source-frequency modulation spectrometer (140-220 GHz). Assignments of transitions in the vibrational ground state and several vibrationally excited states were supported by quantum chemical calculations using the so-called "Lego brick" approach A. Melli, F. Tonolo, V. Barone and C. Puzzarini, J. Phys. Chem. A, 2021,
125
, 9904-9916. From these assignments, accurate spectroscopic (rotational and centrifugal distortion) constants have been derived: for all species and all observed vibrational states, predicted rotational constants show relative accuracy better than 0.1%, and often of the order of 0.01%, compared to the experimental values. The present work hence further validates the use of the "Lego brick" approach for predicting spectroscopic constants with high precision.
Benchmarking experiments and calculations using the "Lego brick" approach on cyanoethynylbenzene isomers.
With an ever-increasing usage of electronic structure programs by the microwave spectroscopy community, there is a growing need to assess the performance of commonly used, low-cost quantum chemical ...methods, particularly with respect to rotational constants because these quantities are central in guiding experiments. Here, we systematically benchmark the predictive power afforded by several low-level ab initio and density functionals combined with a variety of basis sets that are commonly employed in the rotational spectroscopy literature. The data set in our analysis consists of 6916 optimized geometries of 76 representative species where high-resolution experimental gas-phase rotational constants are available. We adopted a Bayesian approach for analyzing the performance of each method and basis set combination, employing Hamiltonian Monte Carlo sampling to determine the uncertainty in theoretical predictions of rotational constants and dipole moments. Our analysis establishes a hierarchy of accuracy and uncertainty, with commonly used methods in the rotational spectroscopy literature such as B3LYP and MP2 yielding lower accuracy and higher uncertainty than newer-generation functionals such as those from the Minnesota family, and ωB97X-D, which, when paired with a modestly sized 6-31+G(d) basis, provides optimal performance with respect to computational cost. Additionally, we provide statistical scaling factors that can be used to empirically correct for vibration–rotation effects, as a means to further improve the accuracy of rotational constants predicted from these relatively low-cost theoretical methods. As part of this, we demonstrate that the uncertainties can be used in simulations of rotational spectra to cross-correlate with broadband spectra, a methodology that could be used to quickly and efficiently survey experimental spectra for new molecules.
Context.
The recent interstellar detections of –CN containing aromatic species, namely benzonitrile, 1-cyanonaphthalene, and 2-cyanonaphthalene, bring renewed interest in related molecules that could ...participate in similar reaction networks.
Aims.
To enable new interstellar searches for benzonitrile derivatives, the pure rotational spectra of several related species need to be investigated in the laboratory.
Methods.
We have recorded the pure rotational spectra of ortho- and meta-dicyanobenzene in the centimetre and millimetre-wave domains. Assignments were supported by high-level quantum chemical calculations. Using Markov chain Monte Carlo simulations, we also searched for evidence of these molecules towards TMC-1 using the GOTHAM survey.
Results.
Accurate spectroscopic parameters are derived from the analysis of the experimental spectra, allowing for reliable predictions at temperatures of interest (i.e. 10–300 K) for astronomical searches. Our searches in TMC-1 for both ortho- and meta-isomers provide upper limits for the abundances of the species.
Abstract
The characterization of interstellar chemical inventories provides valuable insight into the chemical and physical processes in astrophysical sources. The discovery of new interstellar ...molecules becomes increasingly difficult as the number of viable species grows combinatorially, even when considering only the most thermodynamically stable. In this work, we present a novel approach for understanding and modeling interstellar chemical inventories by combining methodologies from cheminformatics and machine learning. Using multidimensional vector representations of molecules obtained through unsupervised machine learning, we show that identification of candidates for astrochemical study can be achieved through quantitative measures of chemical similarity in this vector space, highlighting molecules that are most similar to those already known in the interstellar medium. Furthermore, we show that simple, supervised learning regressors are capable of reproducing the abundances of entire chemical inventories, and predict the abundance of not-yet-seen molecules. As a proof-of-concept, we have developed and applied this discovery pipeline to the chemical inventory of a well-known dark molecular cloud, the Taurus Molecular Cloud 1, one of the most chemically rich regions of space known to date. In this paper, we discuss the implications and new insights machine learning explorations of chemical space can provide in astrochemistry.
Abstract Much of the information gleaned from observations of star-forming regions comes from the analysis of their molecular emission spectra, particularly in the radio regime. The time-consuming ...nature of fitting synthetic spectra to observations interactively for such line-rich sources, however, often results in such analysis being limited to data extracted from a single-dish observation or a handful of pixels from an interferometric observation. Yet, star-forming regions display a wide variety of physical conditions that are difficult, if not impossible, to accurately characterize with such a limited number of spectra. We have developed an automated fitting routine that visits every pixel in the field of view of an Atacama Large Millimeter/submillimeter Array (ALMA) data cube and determines the best-fit physical parameters, including excitation temperature and column densities, for a given list of molecules. In this proof-of-concept work, we provide an overview of the fitting routine and apply it to 0.″26, 1.1 km s −1 resolution ALMA observations of two sites of massive star formation in NGC 6334I. Parameters were found for 21 distinct molecules by generating synthetic spectra across 7.48 GHz of spectral bandwidth between 280 and 351 GHz. Spatial images of the derived parameters for each of the >8000 pixels are presented with special attention paid to the C 2 H 4 O 2 isomers and their relative variations. We highlight the greater scientific utility of the column density and velocity images of individual molecules compared to traditional moment maps of single transitions.
The carbon chain ions HC
$_3 $
3
O
$^+ $
+
and HC
$_3 $
3
S
$^+ $
+
- longer variants of the famous 'X-ogen' line carrier HCO
$^+ $
+
- have been observed for the first time using two cryogenic ...22-pole ion trap apparatus (FELion, Coltrap) and two different light sources: the Free Electron Laser for Infrared eXperiments (FELIX), which was operated between 460 and 2500 cm
$^{-1} $
−
1
, and an optical parametric oscillator operating near 3200 cm
$^{-1} $
−
1
; signals from both experiments were detected by infrared predissociation action spectroscopy. The majority of vibrational fundamentals were observed for both ions and their wavenumbers compare very favourably with results from high-level anharmonic force field calculations performed here at the coupled-cluster singles and doubles level augmented by a perturbative treatment of triple excitations, CCSD(T). As the action scheme employed here probes the Ne-tagged weakly bound variants, Ne-HC
$_3 $
3
O
$^+ $
+
and Ne-HC
$_3 $
3
S
$^+ $
+
, corresponding calculations of these systems were also performed. Differences in the structures and molecular force fields between the bare ions and their Ne-tagged complexes are found to be very small.
We report a systematic study of all known methyl carbon chains toward TMC-1 using the second data release of the GOTHAM survey, as well as a search for larger species. Using Markov Chain Monte Carlo ...simulations and spectral line stacking of over 30 rotational transitions, we report statistically significant emission from methylcyanotriacetylene (CH3C7N) at a confidence level of 4.6σ, and use it to derive a column density of ∼1011 cm−2. We also searched for the related species, methyltetraacetylene (CH3C8H), and place upper limits on the column density of this molecule. By carrying out the above statistical analyses for all other previously detected methyl-terminated carbon chains that have emission lines in our survey, we assess the abundances, excitation conditions, and formation chemistry of methylpolyynes (CH3C2nH) and methylcyanopolyynes (CH3C2n-1N) in TMC-1, and compare those with predictions from a chemical model. Based on our observed trends in column density and relative populations of the A and E nuclear spin isomers, we find that the methylpolyyne and methylcyanopolyyne families exhibit stark differences from one another, pointing to separate interstellar formation pathways, which is confirmed through gas–grain chemical modeling with nautilus.
Abstract
We report the detection of the lowest-energy conformer of
E
-1-cyano-1,3-butadiene (
E
-1-
C
4
H
5
CN
), a linear isomer of pyridine, using the fourth data reduction of the GBT Observations ...of TMC-1: Hunting for Aromatic Molecules (GOTHAM) deep spectral survey toward TMC-1 with the 100 m Green Bank Telescope. We perform velocity stacking and matched-filter analyses using Markov chain Monte Carlo simulations and find evidence for the presence of this molecule at the 5.1
σ
level. We derive a total column density of
3.8
−
0.9
+
1.0
×
10
10
cm
−2
, which is predominantly found toward two of the four velocity components we observe toward TMC-1. We use this molecule as a proxy for constraining the gas-phase abundance of the apolar hydrocarbon 1,3-butadiene. Based on the three-phase astrochemical modeling code
NAUTILUS
and an expanded chemical network, our model underestimates the abundance of cyano-1,3-butadiene by a factor of 19, with a peak column density of 2.34 × 10
10
cm
−2
for 1,3-butadiene. Compared to the modeling results obtained in previous GOTHAM analyses, the abundance of 1,3-butadiene is increased by about two orders of magnitude. Despite this increase, the modeled abundances of aromatic species do not appear to change and remain underestimated by one to four orders of magnitude. Meanwhile, the abundances of the five-membered ring molecules increase proportionally with 1,3-butadiene by two orders of magnitude. We discuss the implications for bottom-up formation routes to aromatic and polycyclic aromatic molecules.
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