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Accurately predicting molecular spectra with deep learning

The accurate prediction of molecular spectra is essential for substance discovery and structure identification, but conventional quantum chemistry methods are computationally expensive. Now, DetaNet achieves the accuracy of quantum chemistry while improving the efficiency of prediction of organic molecular spectra.

Self-driving labs (SDL) are a rich, sophisticated domain that intertwines the robust capabilities of in situ techniques and artificial intelligence, presenting possibilities and challenges in the pursuit of strategies for molecular discovery and sustainable synthesis1,2. In situ spectroscopic techniques, including infrared, Raman, nuclear magnetic resonance and ultraviolet–visible, have long anchored the exploration and verification of molecular structures and properties across diverse scientific disciplines. Their integration into SDLs may enable a substantial challenge—realizing real-time, accurate analysis and information extraction, while simultaneously automating the synthesis and discovery process3,4,5—to be overcome. However, real-time information extraction from in situ experiments is not easy, particularly as computational methods traditionally used to assist this process require exhaustive electronic structure calculations across hundreds of thousands of ‘trial’ molecules. This restricts the utility of quantum chemistry, especially in scenarios demanding real-time applications, notably within the realm of SDL. Acknowledging this difficulty and the critical need for streamlined computational efficiency in SDL settings, Wei Hu and colleagues, writing in Nature Computational Science, introduce DetaNet: a deep learning model ingeniously designed to predict molecular spectra with heightened accuracy and efficacy6.

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Fig. 1: Conceptual integration of DetaNet within a self-driving lab workflow.

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  1. Department of Chemistry, University of Waterloo, Waterloo, Ontario, Canada

    Conrard Giresse Tetsassi Feugmo

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Tetsassi Feugmo, C.G. Accurately predicting molecular spectra with deep learning.
Nat Comput Sci (2023). https://doi.org/10.1038/s43588-023-00553-9

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  • Published: 15 November 2023

  • DOI: https://doi.org/10.1038/s43588-023-00553-9

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