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<p>We report a novel, chemically intuitive, machine learning-based approach for assigning vibrational bands in complex molecular systems studied by infrared (IR) spectroscopy. As a complementary alternative to traditional power spectrum analysis, this method accelerates vibrational mode assignment by decomposing the IR spectrum into contributions from molecular fragments rather than analyzing atom-by-atom contributions. We demonstrate the effectiveness of this approach through a case study involving a chromophore in noncovalent interaction with a single solvent molecule. Specifically, we show that it rapidly reveals the IR signature associated with the hydrogen-bond interaction.</p>

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Decoding the signatures of chromophore–solvent interactions in infrared spectroscopy with machine learning: insights from a hybrid density-functional theory/molecular mechanics dynamics model

  • 期刊论文

  • 发表日期：

    2025年9月17日

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  • Abir Kebabsa,
  • François Maurel,
  • Éric Brémond

摘要

We report a novel, chemically intuitive, machine learning-based approach for assigning vibrational bands in complex molecular systems studied by infrared (IR) spectroscopy. As a complementary alternative to traditional power spectrum analysis, this method accelerates vibrational mode assignment by decomposing the IR spectrum into contributions from molecular fragments rather than analyzing atom-by-atom contributions. We demonstrate the effectiveness of this approach through a case study involving a chromophore in noncovalent interaction with a single solvent molecule. Specifically, we show that it rapidly reveals the IR signature associated with the hydrogen-bond interaction.