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Sequence-to-sequence Learning For Molecular Structure Derivation From Infrared Spectra
French, Ethan
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https://hdl.handle.net/2142/116578
Description
- Title
- Sequence-to-sequence Learning For Molecular Structure Derivation From Infrared Spectra
- Author(s)
- French, Ethan
- Contributor(s)
- Lin, Zhou
- Issue Date
- 2022-06-22
- Keyword(s)
- Mini-symposium: Machine Learning
- Abstract
- \begin{wrapfigure}{r}{0pt} \includegraphics[scale=0.4]{model.eps} \end{wrapfigure} Fully identifying unknown molecules via infrared spectroscopy can be a challenging task for even the most experienced researchers. Current data-driven computational methods usually identify unknown spectra by matching them against databases of known spectra. However, this method can be problematic for novel complex molecules given the relative lack of information. Deep learning provides a potential solution to this problem. Sequence-to-sequence learning has had great success in a wide range of areas such as language translation and speech recognition.\footnote{Ilya Sutskever, Oriol Vinyals, Quoc V. Le, ``Sequence to Sequence Learning with Neural Networks'', {\it NeurIPS}, {2014}, {\bf 27}.} In this work, an unsupervised sequence-to-sequence model was extended to chemical systems and used to derive complete molecular structures from infrared spectra. The model was trained on the infrared spectra of small organic molecules containing C, H, O, N, and F atoms. These molecules were represented using SELFIES, an improved version of the SMILES string molecular fingerprint descriptor.\footnote{Mario Krenn, Florian H\"{a}se, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik, ``Self-referencing embedded strings (SELFIES): A 100\% robust molecular string representation'', {\it Mach. Learn.: Sci. Technol.} 2020, {\bf 1}, 045024} Our model is able to achieve state-of-the-art results in successfully identifying a wide variety of molecules from their infrared spectra.
- Publisher
- International Symposium on Molecular Spectroscopy
- Type of Resource
- text
- Language
- eng
- Handle URL
- https://hdl.handle.net/2142/116578
- DOI
- https://doi.org/10.15278/isms.2022.WK05
- Copyright and License Information
- Copyright 2022 held by the authors
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