Boosting the Modeling of InfraRed and Raman Spectra of Bulk Phase Chromophores with Machine Learning

18 July 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In the field of vibrational spectroscopy simulation, hybrid approximations to Kohn-Sham density-functional theory (KS-DFT) are often considered computationally prohibitive due to the significant effort required to evaluate the exchange-correlation potential in planevawe codes. In this Letter, we show that by leveraging the porting of KS-DFT on GPU and incorporating machine-learning techniques, simulating IR and Raman spectra of real-life chromophores in bulk aqueous solution becomes a routine application at this level of theory.

Keywords

density-functional theory
vibrational spectroscopy
ab initio molecular dynamics
hybrid functionals

Supplementary materials

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Title
Boosting the Modeling of InfraRed and Raman Spectra of Bulk Phase Chromophores with Machine Learning
Description
The Supporting Information reports all the details regarding the time-series analysis methodology (Section S1), the computational details (Section S2), the list of the vibrational modes of ADOTA$^{+}$ (Section S3), and the description and implementation of the machine learning model (Section S4).
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