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.

Fast prediction of anharmonic vibrational spectra for complex organic molecules / Miotto, Mattia; Monacelli, Lorenzo. - In: NPJ COMPUTATIONAL MATERIALS. - ISSN 2057-3960. - 10:1(2024), pp. 1-9. [10.1038/s41524-024-01400-9]

Fast prediction of anharmonic vibrational spectra for complex organic molecules

Miotto, Mattia
;
Monacelli, Lorenzo
2024

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.
2024
raman spectrum; infrared spectrum; time dependent scha
01 Pubblicazione su rivista::01a Articolo in rivista
Fast prediction of anharmonic vibrational spectra for complex organic molecules / Miotto, Mattia; Monacelli, Lorenzo. - In: NPJ COMPUTATIONAL MATERIALS. - ISSN 2057-3960. - 10:1(2024), pp. 1-9. [10.1038/s41524-024-01400-9]
File allegati a questo prodotto
File Dimensione Formato  
Miotto_Fast-prediction_2024.pdf

accesso aperto

Note: Articolo su rivista
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 1.48 MB
Formato Adobe PDF
1.48 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1724412
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 1
social impact