Functional groups prediction from infrared spectra based on computer-assist approaches

Wang, Zhimeng; Feng, Xiaoyu; Liu, Junhong; Lu, M.; Li, Menglong

doi:10.1016/j.microc.2020.105395

Cited by 23 publications

(27 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(They are not the only choices, and the research literature provides other alternatives, such as support vector machines and neural networks, that may be more appropriate vibrational spectral analysis. 12,13 ) The student handout and supporting information provide brief discussions of the assumptions of each of these models. The multiclass classification performed in Part III of this activity is structured as multiple one-vs-all binary 4 classifications, in which the probability of membership in each class is separately determined for each molecule and the class with the highest probability is then taken as the final predicted label.…”

Section: Four Common ML Classification Algorithms Are Implemented In This Exercise: Decisionmentioning

confidence: 99%

“…9 More specifically, ML has been applied to both infrared (IR) absorption and Raman vibrational spectroscopy, 10,11 including functional group identification. [12][13][14][15] Although the chemical education community acknowledges the need for student training in computational methods and ML, 16 there are limited pedagogical materials and no standard way of incorporating this into the curriculum. One approach has been the development of dedicated semester-long courses in scientific computing for chemists 17 or cheminformatics 18 that introduce programming in general and include modules on ML methods.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for functional group identification in vibrational spectroscopy: A pedagogical lab for undergraduate chemistry students

Thrall

Lee

Schrier

et al. 2021

Preprint

View full text Add to dashboard Cite

Here we report a computational activity that introduces undergraduate physical chemistry students to machine learning (ML) in the context of vibrational spectroscopy. In the first part of the activity, students use ML binary classification algorithms to distinguish between carbonyl-containing and non-carbonyl-containing molecules on the basis of their infrared absorption spectra and test modifications to this basic analysis. In a further extension of the activity, students implement a multiclass classification to predict whether carbonyl-containing molecules contain a ketone, a carboxylic acid, or another carbonyl group. This activity is designed to introduce students both to the basic workflow of a ML classification analysis and to some of the ways in which machine learning analyses can fail. We provide a comprehensive handout for the activity, including theoretical background and a detailed protocol, as well as datasets and code to implement the exercise in Python or Mathematica.

show abstract

Section: Four Common ML Classification Algorithms Are Implemented In This Exercise: Decisionmentioning

confidence: 99%

mentioning

confidence: 99%

Machine learning for functional group identification in vibrational spectroscopy: A pedagogical lab for undergraduate chemistry students

Thrall

Lee

Schrier

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…23 Machine learning algorithms have shown great promise in their ability to solve ill-defined inverse problems 24 and thus offer an appealing route to obtain fully automated structure elucidation using NMR data as input. ML methods have previously been used to identify the presence of functional groups using IR spectra [25][26][27][28][29] , NMR spectra 30,31 , and mass spectrometry data [32][33][34] . However, these existing methods only predict the presence of a small set of functional groups and thus do not provide enough information to elucidate full molecular structures.…”

Section: Introductionmentioning

confidence: 99%

A framework for automated structure elucidation from routine NMR spectra

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recently, it was reported that molecular formula, as well as functional groups that are likely to be present in a given compound, can be identified from the data obtained using rotational spectroscopy . A computer-assisted approach to detect the presence/absence of 16 functional groups from the experimental infrared spectra was also reported …”

Section: Introductionmentioning

confidence: 99%

Chiral Molecular Structure Determination for a Desired Compound Just from Its Molecular Formula and Vibrational Optical Activity Spectra

Johnson

Polavarapu

2021

J. Phys. Chem. A

View full text Add to dashboard Cite

A novel proof-of-concept model for chiral molecular structure determination using just the molecular formula and vibrational optical activity (VOA) spectra is presented. To verify this concept, the molecular formula of a desired compound is used to generate all possible chiral structural isomers and their VOA spectra are predicted. The similarity analyses of predicted VOA spectra were then carried out in two different ways: (a) similarity between VOA spectrum of one structural isomer with those of the rest, referred to as cross-correlations; (b) similarity between VOA spectra of all chiral structural isomers with the experimental VOA spectra of the desired compound. Three different molecular formulae, C 4 H 8 O, C 3 H 5 ClO, and C 6 H 10 O, and their chiral structural isomers (6, 9, and 75 respectively), were investigated. In each case, the correct chiral molecular structure of the desired compound was identified without ambiguity. Cross-correlation analysis revealed the uniqueness of VOA spectra in deducing the chiral molecular structure solely from its molecular formula. Different chiral structural isomers associated with the molecular formula CH 3 NO 2 were also found to have no significant cross-correlations between their VOA spectra, opening a pathway to detect and identify the elusive chiral Nhydroxyoxaziridine from its VOA spectra.

show abstract

Functional groups prediction from infrared spectra based on computer-assist approaches

Cited by 23 publications

References 29 publications

Machine learning for functional group identification in vibrational spectroscopy: A pedagogical lab for undergraduate chemistry students

Machine learning for functional group identification in vibrational spectroscopy: A pedagogical lab for undergraduate chemistry students

A framework for automated structure elucidation from routine NMR spectra

Chiral Molecular Structure Determination for a Desired Compound Just from Its Molecular Formula and Vibrational Optical Activity Spectra

Contact Info

Product

Resources

About