Fast prediction of anharmonic vibrational spectra for complex organic molecules

Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.

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Publication Details

Journal
npj Computational Materials
Published
2024-10-10
DOI
https://doi.org/10.1038/s41524-024-01400-9
Citations
8
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.94

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article

Fast prediction of anharmonic vibrational spectra for complex organic molecules

Mattia Miotto, Lorenzo Monacelli
8 citations
npj Computational Materials
Machine Learning in Materials Science
0.94
article

Fast prediction of anharmonic vibrational spectra for complex organic molecules

Mattia Miotto, Lorenzo Monacelli
article en
8 citations

Abstract

Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.

npj Computational MaterialsVol. 10(1)
Italian Institute of Technology (IT), École Polytechnique Fédérale de Lausanne (CH), Sapienza University of Rome (IT)
European Commission, Horizon 2020 Framework Programme
Openalex Percentile: Top 23%
Machine Learning in Materials Science
0.94
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