Simulation of infrared spectra using artificial neural networks based on semiempirical and empirical data

Weigel, Udo M.; Herges, Rainer

doi:10.1016/0003-2670(96)00203-6

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…An analogous conclusion was also drawn by Weigel and Herges 144 who attempted to simulate IR spectra using an ANN trained on a set of spectra obtained from experiments and semiempirical quantum-chemical calculations.…”

Section: Prediction Of Ir Spectrasupporting

confidence: 60%

Expert systems for structure elucidation of organic molecules by spectral methods

Elyashberg¹

1999

Russ. Chem. Rev.

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Section: Prediction Of Ir Spectrasupporting

confidence: 60%

Expert systems for structure elucidation of organic molecules by spectral methods

Elyashberg¹

1999

Russ. Chem. Rev.

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“…Absolute intensities are typically even more difficult to simulate accurately for than peak frequencies (Gussoni et al, 2006). Computational models that predict vibrational motion of molecules in isolation using quantum mechanical models (Barone et al, 2012) or by harmonic approximation for larger molecules (Weymuth et al, 2012) suffer from two shortcomings: poor treatment of anharmonicity and lack of solvent effects in liquid solutions (Thomas et al, 2013). Quantum mechanical simulations can parameterize interactions with an implicitly modeled solvent through a polarizable continuum model framework (Cappelli and Biczysko, 2011) but do not adequately represent specific interactions such as hydrogen bonding (Barone et al, 2014).…”

Section: Limits Of Conventional Approaches To Calibrationmentioning

confidence: 99%

Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

et al. 2019

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Abstract. Atmospheric particulate matter (PM) is a complex mixture of many different substances and requires a suite of instruments for chemical characterization. Fourier transform infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR spectroscopy has enjoyed a long history in monitoring gas-phase constituents in the atmosphere and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predict their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid-IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal–optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR spectroscopy to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: seven sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the year 2011 and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at six of the same sites as in 2011 and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR spectroscopy can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations.

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“…We present an efficient, data-driven approach to the prediction of the IR spectra of PAHs that combines a NN and inputs extracted from the NASA Ames PAH IR spectroscopic database. The potential of NNs to predict IR spectra of organic molecules was explored more than 20 years ago [28,29]. However, the discriminatory power of these pioneering attempts was not particularly convincing.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning prediction of infrared spectra of interstellar polycyclic aromatic hydrocarbons

Kovacs,

Zhu,

Carrete

et al. 2020

Preprint

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We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many orders of magnitude lower than what a first-principles calculation would demand. The input to the NN is based on the Morgan fingerprints extracted from the skeletal formulas of the molecules and does not require precise geometrical information such as interatomic distances. The model shows excellent predictive skill for out-of-sample inputs, making it suitable for improving the mixture models currently used for understanding the chemical composition and evolution of the interstellar medium. We also identify the constraints to its applicability caused by the limited diversity of the training data and estimate the prediction errors using a ensemble of NNs trained on subsets of the data. With help from other machine-learning methods like random forests, we dissect the role of different chemical features in this prediction. The power of these topological descriptors is demonstrated by the limited effect of including detailed geometrical information in the form of Coulomb matrix eigenvalues.

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Simulation of infrared spectra using artificial neural networks based on semiempirical and empirical data

Cited by 15 publications

References 28 publications

Expert systems for structure elucidation of organic molecules by spectral methods

Expert systems for structure elucidation of organic molecules by spectral methods

Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

Machine-learning prediction of infrared spectra of interstellar polycyclic aromatic hydrocarbons

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